diff --git a/.github/workflows/dockerhub.yml b/.github/workflows/dockerhub.yml index f1c54c108..fd724b268 100644 --- a/.github/workflows/dockerhub.yml +++ b/.github/workflows/dockerhub.yml @@ -1,7 +1,7 @@ --- -# Build and publish docker image(s) -# Publishing to Dockerhub requires a username and token -# as the secrets DOCKERHUB_USERNAME and DOCKERHUB_TOKEN +# Build and publish image(s) to GitHub (ghcr.io) +# Publishing to GitHub requires a username (who triggered the action) +# and password (as the secrets GITHUB_TOKEN) # remember to change repo-name and set dockerfile(s)/image name(s) in the matrix name: Publish Docker Image @@ -32,22 +32,13 @@ jobs: matrix: include: - dockerfile: Dockerfiles/Dockerfile - images: | - docker.io/scilifelabdatacentre/covid-portal - ghcr.io/scilifelabdatacentre/covid-portal + images: ghcr.io/scilifelabdatacentre/covid-portal permissions: contents: read packages: write steps: - name: Check out the repo uses: actions/checkout@v3 - # only needed when publishing to Dockerhub - - name: Log in to Docker Hub - uses: docker/login-action@v2 - with: - # available in scilifelabdatacentre, ask admin for help - username: ${{ secrets.DOCKERHUB_USERNAME }} - password: ${{ secrets.DOCKERHUB_TOKEN }} # only needed when publishing to Github (ghcr.io) - name: Log in to Github Container Repository uses: docker/login-action@v2 diff --git a/config.yaml b/config.yaml index c25141be5..8a830433f 100644 --- a/config.yaml +++ b/config.yaml @@ -83,6 +83,8 @@ caches: taxonomies: highlights_topic: highlights_topics highlights_voc: highlights_vocs + dashboards_topic: dashboards_topics permalinks: highlights_topics: "/highlights/topics/:slug/" highlights_vocs: "/highlights/vocs/:slug/" + dashboards_topics: "/dashboards/topics/:slug/" diff --git a/content/english/dashboards/RECOVAC.md b/content/english/dashboards/RECOVAC.md index 6adbe2e4d..3a5aca635 100644 --- a/content/english/dashboards/RECOVAC.md +++ b/content/english/dashboards/RECOVAC.md @@ -5,9 +5,10 @@ banner: /dashboard_thumbs/RECOVAC.png toc: true plotly: true menu: - dashboard_menu: - identifier: recovac - name: Register-based vaccination (RECOVAC) + dashboard_menu: + identifier: recovac + name: Register-based vaccination (RECOVAC) +dashboards_topics: [COVID-19, Infectious diseases] --- ## RECOVAC project overview @@ -26,9 +27,9 @@ EPN Nr 2020-01800, 2020-05829, 2021-00267, 2021-00829, 2021-02106, 2021-04098, 2
All data last updated: {{% RECOVAC_date_modified %}}
-*All code used to produce the visualisations on this page is available on [GitHub](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/tree/main/RECOVAC). The particular scripts used in each case are linked below the plots.* +_All code used to produce the visualisations on this page is available on [GitHub](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/tree/main/RECOVAC). The particular scripts used in each case are linked below the plots._ -The visualisations on this page broadly relate to two types of data; (a) data regarding the Swedish population at large, and (b) data on individuals in the Swedish population with certain comorbidities (at the start of the pandemic, assessed as of 1 Jan 2020 based on information from 2015-2019). More detail about the data used to produce the visualisations is available in the two subsections below. Instructions on how to manipulate the interactive plots are provided above the plots in each subsection. +The visualisations on this page broadly relate to two types of data; (a) data regarding the Swedish population at large, and (b) data on individuals in the Swedish population with certain comorbidities (at the start of the pandemic, assessed as of 1st Jan 2020, based on information from 2015-2019). More detail about the data used to produce the visualisations is available in the two subsections below. Instructions on how to manipulate the interactive plots are provided above the plots in each subsection. In each case, the data are subdivided according to the number of doses received by individuals. @@ -36,7 +37,7 @@ In each case, the data are subdivided according to the number of doses received #### Information on vaccination coverage -The top graph in this section shows data on vaccine coverage (i.e. the proportion of the population that has received a given number of doses). The data are from the National Vaccination Register at the Public Health Agency of Sweden (PHAS) and population register data from Statistics Sweden (SCB). The data spans the period 21st December 2020 until 30th September 2022. +The top graph in this section shows data on vaccine coverage (i.e. the proportion of the population that has received a given number of doses). The data are from the National Vaccination Register at the Public Health Agency of Sweden (PHAS) and population register data from Statistics Sweden (SCB). COVID-19 vaccinations first became available in Sweden in late December 2020. They were first offered to those most at risk of developing serious symptoms (i.e. the elderly and those with certain comorbidities). For more details on the vaccine rollout in Sweden, please see our [page on vaccines](https://www.covid19dataportal.se/dashboards/vaccines/). @@ -44,14 +45,18 @@ There is a clear trend in the coverage for each dose. Specifically, the levels o #### Information on admission to intensive care units (ICU) -The lower graph in this section shows the number of people admitted to intensive care (ICU), and how many vaccine doses they had received upon admission. These data are from the registers already mentioned, as well as the Swedish Intensive Care Register (SIR), a healthcare quality register. The current data covers the period 2nd March 2020 - 30th September 2022. +The lower graph in this section shows the number of people admitted to intensive care (ICU), and how many vaccine doses they had received upon admission. These data are from the registers already mentioned, as well as the Swedish Intensive Care Register (SIR), a healthcare quality register. There are three major peaks in admissions to intensive care (ICU). The timing of these peaks corresponds to peaks in COVID-19 cases, specifically in spring 2020, winter 2021, and spring 2021, when the rates of infection were high. In order to infer the impact of vaccination on ICU admissions, it is best to directly compare the data over the same time period. After aligning the timeframes in the below graphs, it is clear that the number of patients admitted with one or two vaccine doses increases with the coverage of these doses. This is unsurprising, given that the vaccine does not completely protect against serious illness, especially after only a single dose. There is considerable evidence of the protective effect of vaccination, though. Firstly, a majority of admissions in most weeks were related to individuals that had not received any vaccine doses, despite the fact that this represents an increasingly smaller portion of the population over time. Secondly, the number of admissions decreases as vaccine coverage increases, particularly when considering the coverage of multiple doses. The absolute numbers are, of course, also affected by the changes in intensity of the pandemic. This means that they tend to be lower in the summer months and other periods when the spread of the infection was low, and higher over the winter-spring period. There is, however, some evidence of a de-coupling between case number and ICU admissions for the first time in winter 2021. Cases were generally high over this period (see e.g. data from the [Swedish public health agency](https://experience.arcgis.com/experience/09f821667ce64bf7be6f9f87457ed9aa)). This is evidence that vaccines are protective against the onset of serious illness, because it means a smaller proportion of cases led to ICU admission. This de-coupling is also evident through 2022. +##### Visualisations + +The graphs below have multiple interactive features. In brief, it is possible to view different parts of the data using the buttons above the graphs. For exmaple, it is possible to look only at data from only those over 60 years of age by clicking '>60'. The 'Align timeline' button will change the timeline of the graphs so that only period for which data is available for both types of data shown is visible. The 'Show all data' button can be used to see all of the available data for both datasets (the timelines of the two are not the same). +
@@ -70,7 +75,7 @@ The data are categorised according to the number of doses received, and each cat ##### Changing the timeframe displayed -The two graphs are shown on different time scales. This is because vaccinations were not made available until early 2021, whilst data on ICU admissions resulting from COVID-19 infection are available from March 2020. The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on ICU admission. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'View whole time series' button to view all of the available data. +The two graphs are shown on different time scales. This is in part because vaccinations were not made available until early 2021, whilst data on ICU admissions resulting from COVID-19 infection are available from March 2020. Further, the latest date for which data is available can differ between the two datasets. The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on ICU admission. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'Show all data' button to view all of the available data. ##### Accurately read data values @@ -80,15 +85,12 @@ It is possible to view the underlying data values by hovering the cursor over th When hovering over the plot with the cursor, additional grey icons appear in the top right. The +, -, and magnifying glass icons can be used to zoom in/out of the plot. Alternatively, it is possible to zoom into a given part of the graph by clicking and dragging with the cursor to select that portion. The autoscale and reset axes icons (which look like a box containing arrows and a house, respectively) can be used to scale the axes appropriately for the data selected. The plot can be downloaded in .png format by clicking on the camera icon. -
- -
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/vaccination_RECO_timeseries_buttons.json" height="500px" >}}
+
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/ICUadmiss_vaccinationlevel_button.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/swedishpop_subplot_button.json" height="800px" >}}
**Code used to produce plots:** [Preparation for vaccine coverage data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/Swedishpop_vaccinecov_dataprep.py), [Graph of vaccine coverage](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/Swedishpop_vaccinecov_plotwbuttons.py), [Graph and data preparation for ICU admissions data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/Swedishpop_ICU_plotwbuttons.py). @@ -97,12 +99,16 @@ When hovering over the plot with the cursor, additional grey icons appear in the This section considers data on individuals with one of four comorbidities; cardiovascular disease, diabetes, respiratory disease, and cancer. The data were obtained from the registers mentioned in the section above, as well as the National Patient Register (NPR) and SmiNet. The NPR contains specialised outpatient and hospitalised inpatient care, and is held by the National Board of Health and Welfare (Socialstyrelsen). SmiNet is the national register for mandatory registration of notifiable infectious diseases, and is held at the PHAS. Coded diagnoses in 2015-2019 from the NPR are used to classify individuals by comorbidity conditions. Note that individuals may be included in one or more of these comorbidity groups. COVID-19 cases diagnosed among people with comorbidities are determined using a positive SARS-CoV-2 test from SmiNet and/or a diagnosis of COVID-19 in NPR (the overwhelming majority have a positive test). -The buttons in the below plots can be used to display data on a given comorbidity. The data in both graphs will change according to the comorbidity selected. Data related to vaccine coverage (upper graph) includes the period 21st December 2020 - 30th September 2022. Data on the incidence of COVID-19 (lower graph) instead spans the period February 2020 - 30th September 2022. All data available on a given comorbidity will be displayed by default, but the timelines can be aligned. +The buttons in the below plots can be used to display data on a given comorbidity (see the blow subsection for details). All data available on a given comorbidity will be displayed by default, this may mean that different timelines are shown in the two graphs. However, the timelines can be aligned so that more direct comparisons can be made between the data in the two graphs. + +There is clear evidence of the benefits of vaccination for patients with each comorbidity considered. When switching between comorbidities, it is evident that they follow the same overall pattern in terms of the number of COVID-19 cases detected, but there are variations in magnitude. Similar to the above data considering the population over 18, it is clear that the proportion of vaccinated individuals diagnosed with COVID-19 increases with vaccine coverage. This is expected because the vaccine is not completely effective at preventing infection (although it does have a higher effectiveness against the development of serious disease, as shown in many studies, see also the [above data on ICU admission rates](#swedish-population-in-general)). Thus, as the proportion of the population that is vaccinated increases, more vaccinated individuals are likely to develop COVID-19. Some evidence of a protective effect can already be inferred from a superficial consideration of the number of cases detected in autumn 2020 compared to autumn 2021. The initial reduction in cases that occurs as vaccination coverage increases could, in part, be attributed to a general reduction in cases over the 2021 summer months. There was a peak in cases during winter 2021, and it is clear that many of the cases occurred in vaccinated individuals, but this is not evidence that vaccines do not have a protective effect. Rather, it reflects that a large part of the population was vaccinated by this time. The peak in winter 2021 can be attributed to the emergence of the new Omicron virus variant that was first detected in November 2021. This variant spreads more easily than earlier variants, even in vaccinated individuals. The number of COVID-19 cases presented in the graph below, and the number of ICU admissions in the graph in the section above, do not consider equivalent populations. However, considering both datasets together does provide some insights into the protective effect of vaccines. Specifically, each peak in COVID-19 cases in patients with each of the comorbidities prior to summer 2021 is clearly reflected in the number of ICU admissions in the adult population of Sweden. By contrast, this Omicron peak in cases is not reflected in a peak in ICU admissions. This is evidence that vaccines were protective against the onset of the more severe symptoms. A protective effect has also been confirmed in many studies. Ideally, studies considering a protective effect should directly compare COVID-19 cases and vaccine coverage in a manner that accounts for age-group and time point. + +##### Visualisations -There is clear evidence of the benefits of vaccination for patients with each comorbidity considered. When switching between comorbidities, it is evident that they follow the same overall pattern in terms of the number of COVID-19 cases detected, but there are variations in magnitude. Similar to the above data considering the population over 18, it is clear that the proportion of vaccinated individuals diagnosed with COVID-19 increases with vaccine coverage. This is expected because the vaccine is not completely effective at preventing infection, although it does have a higher effectiveness against the development of serious disease, as shown in many studies (see also the [above data on ICU admission rates](#swedish-population-in-general) and results from [Xu *et al.* (2022)](https://doi.org/10.3390/vaccines10122074); a recent study based on RECOVAC data). Thus, as the proportion of the population that is vaccinated increases, more vaccinated individuals are likely to develop COVID-19. Some evidence of a protective effect can already be inferred from a superficial consideration of the number of cases detected in autumn 2020 compared to autumn 2021. The initial reduction in cases that occurs as vaccination coverage increases could, in part, be attributed to a general reduction in cases over the 2021 summer months. There was a peak in cases during winter 2021, and it is clear that many of the cases occurred in vaccinated individuals, but this is not evidence that vaccines do not have a protective effect. Rather, it reflects that a large part of the population was vaccinated by this time. The peak in winter 2021 can be attributed to the emergence of the new Omicron virus variant that was first detected in November 2021. This variant spreads more easily than earlier variants, even in vaccinated individuals. The number of COVID-19 cases presented in the graph below, and the number of ICU admissions in the graph in the section above, do not consider equivalent populations. However, considering both datasets together does provide some insights into the protective effect of vaccines. Specifically, each peak in COVID-19 cases in patients with each of the comorbidities prior to summer 2021 is clearly reflected in the number of ICU admissions in the adult population of Sweden. By contrast, this Omicron peak in cases is not reflected in a peak in ICU admissions. This is evidence that vaccines were protective against the onset of the more severe symptoms. A protective effect has also been confirmed in many studies. Ideally, studies considering a protective effect should directly compare COVID-19 cases and vaccine coverage in a manner that accounts for age-group and time point. +The graphs below have multiple interactive features. It is possible to see all of the data available for a given comorbidity for clicking on the corresponding button. The 'Align timeline' button will change the timeframe shown so that only the time period that is common between the two graphs is shown. The 'Show all data' button can be used to see all of the available data for both datasets (the timelines of the two are not the same).
@@ -117,35 +123,39 @@ Click on the button corresponding to the cormorbidity of interest to display dat ##### Changing the timeframe displayed -The two graphs are shown on different time scales. This is because vaccinations were not made available until early 2021, whilst data on COVID-19 infection are available from March 2020. The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on COVID-19 cases. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'View whole time series' button to view all of the available data. +The two graphs are shown on different time scales. This is partly because vaccinations were not made available until early 2021, whilst data on COVID-19 infection are available from March 2020. Further, the latest data available can differ between data types. + +The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on COVID-19 cases. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'Show all data' button to view all of the available data. ##### Accurately read data values -It is possible to view the underlying data values by hovering the cursor over the graph. All values for a given date are shown together. +It is possible to view the underlying data values by hovering the cursor over the graph. All values for a given date are shown together. ##### Other features When hovering over the plot with the cursor, additional grey icons appear in the top right. The +, -, and magnifying glass icons can be used to zoom in/out of the plot. Alternatively, it is possible to zoom into a given part of the graph by clicking and dragging with the cursor to select that portion. The autoscale and reset axes icons (which look like a box containing arrows and a house, respectively) can be used to scale the axes appropriately for the data selected. The plot can be downloaded in .png format by clicking on the camera icon. +
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/comorbs_subplot_button.json" height="800px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/comorbs_subplot_button.json" height="800px" >}}
**Code used to produce plots:** [Preparation of COVID-19 case data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/comorbidity_cases_dataprep.py), [Preparation of vaccination coverage data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/comorbidity_vaccinecov_dataprep.py), [Graph containing subplots](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/comorbidity_subplots_wbuttons.py). ## Determinants of vaccination -The rapid development of COVID-19 vaccination and high vaccination coverage have been crucial in mitigating the societal effects of the COVID-19 pandemic. Two recently published articles by Spetz and colleagues ([Spetz *et al.* (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz *et al.* (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860)) at Gothenburg University used registry-based data to examine sociodemographic determinants of vaccination coverage. In brief, although the overall vaccination coverage in Sweden was over 82%, vaccination was lower among certain demographic groups. Specifically, vaccination coverage was lower among younger individuals, those on low incomes, those born outside of Sweden, and males. Individuals born in a low- or middle-income country were found to be six times less likely to be vaccinated than individuals born in Sweden.  When considering the whole working age population (18-64 years), vaccination coverage varied extensively between different subgroups, as defined by the factors studied, from 32% to as high as 96%. Similar results were seen in the older population (65 years of age and older). These results indicate that taking social factors into consideration is important in future health and vaccination efforts. Read more about these findings in [Spetz *et al.* (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz *et al.* (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860). + +The rapid development of COVID-19 vaccination and high vaccination coverage have been crucial in mitigating the societal effects of the COVID-19 pandemic. Two recently published articles by Spetz and colleagues ([Spetz _et al._ (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz _et al._ (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860)) at Gothenburg University used registry-based data to examine sociodemographic determinants of vaccination coverage. In brief, although the overall vaccination coverage in Sweden was over 82%, vaccination was lower among certain demographic groups. Specifically, vaccination coverage was lower among younger individuals, those on low incomes, those born outside of Sweden, and males. Individuals born in a low- or middle-income countries were found to be six times less likely to be vaccinated than individuals born in Sweden.  When considering the whole working age population (18-64 years), vaccination coverage varied extensively between different subgroups, as defined by the factors studied, from 32% to 96%. Similar results were seen in the older population (65 years of age and older). These results indicate that taking social factors into consideration is important in future health and vaccination efforts. Read more about these findings in [Spetz _et al._ (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz _et al._ (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860). ## Vaccine effectiveness -Using long-term data about the Swedish general population from RECOVAC, [Xu *et al.* (2022)](https://doi.org/10.3390/vaccines10122074) provided a more detailed insight into time-varying vaccine effectiveness (VE) against COVID-19 infection, hospitalisation, ICU admission, and death up to 13 months after vaccination. Two vaccine doses offered good, long-lasting protection against infection before the Omicron variant emerged (VE above 85% for all time intervals), but more limited protection against infection with the Omicron variant. For severe COVID-19 outcomes, however, high VE was observed during the entire follow-up period with better protection among individuals above age 65. For more details, see [Xu *et al.* (2022)](https://doi.org/10.3390/vaccines10122074). + +Using long-term data about the Swedish general population from RECOVAC, [Xu _et al._ (2022)](https://doi.org/10.3390/vaccines10122074) provided a more detailed insight into time-varying vaccine effectiveness (VE) against COVID-19 infection, hospitalisation, ICU admission, and death up to 13 months after vaccination. Two vaccine doses offered good, long-lasting protection against infection before the Omicron variant emerged (VE above 85% for all time intervals), but more limited protection against infection with the Omicron variant. For severe COVID-19 outcomes, however, high VE was observed during the entire follow-up period with better protection among individuals above age 65. For more details, see [Xu _et al._ (2022)](https://doi.org/10.3390/vaccines10122074). ## Contact information -Prof Fredrik Nyberg, Project Leader and Professor of Register Epidemiology, School of Public Health and Community Medicine, Institute of Medicine, Gothenburg University. Email: [fredrik.nyberg.2@gu.se](mailto:fredrik.nyberg.2@gu.se). -Patricia Ernst, Project Coordinator SCIFI-PEARL and RECOVAC. Email: [patricia.ernst@gu.se](mailto:patricia.ernst@gu.se) +Prof Fredrik Nyberg, Project Leader and Professor of Register Epidemiology, School of Public Health and Community Medicine, Institute of Medicine, Gothenburg University. Email: [fredrik.nyberg.2@gu.se](mailto:fredrik.nyberg.2@gu.se). You are also welcome to use the general email addresses for the projects: [recovac@gu.se](recovac@gu.se) or [scifipearl@medicin.gu.se](mailto:scifipearl@medicin.gu.se) @@ -163,29 +173,29 @@ The data in this study are pseudonymized individual-level data from Swedish heal We will consider proposals for collaboration in a positive vein, based on an assessment of their interest and importance, fit with the project and realistic possibility of implementing (especially given internal resource within the project). We actively seek and encourage cross-functional collaboration and invite new ideas and expertise. Collaborations will be structured as sub projects under **SCIFI-PEARL/RECOVAC** and the existing PI and leadership, in accordance with existing ethics approvals. Arrangements for data access, cost sharing and collaboration support from core project staff and resource will be discussed on a case by case basis. Data will not be shared outside of our protected data environments. Before you contact us, think carefully through the aims for the collaboration with **SCIFI-PEARL / RECOVAC** that you wish to initiate. Please attach a project idea / subproject synopsis or preliminary project plan. You can use your own format, or the template available here if you wish. We cannot finance research projects, but are open to collaborate in funding applications. -*For more information, please contact those involved with the project using the contact details [above](/dashboards/recovac/#contact-information).* +_For more information, please contact those involved with the project using the contact details [above](/dashboards/recovac/#contact-information)._ ## Publications and Preprints Below is a list of publications and preprints from this group. Please see the [SCIFI-PEARL webpage](https://www.gu.se/forskning/scifi-pearl) for a more comprehensive overview of outputs (e.g. lists of conference abstracts and recently submitted articles). The publication lists on that page are also updated more regularly (weekly) than the list below. -* Xu, Y., Li, H., Kirui, B., Santosa, A., Gisslen, M., Leach, S., Wettermark, B., Vanfleteren, L. E. G. W., Nyberg, F. (2022). Effectiveness of COVID-19 Vaccines Over 13 Months Covering the Period of the Emergence of the Omicron Variant in the Swedish Population. *Available at SSRN as a preprint:* [https://doi.org/10.3390/vaccines10122074](https://doi.org/10.3390/vaccines10122074) +- Xu, Y., Li, H., Kirui, B., Santosa, A., Gisslen, M., Leach, S., Wettermark, B., Vanfleteren, L. E. G. W., Nyberg, F. (2022). Effectiveness of COVID-19 Vaccines Over 13 Months Covering the Period of the Emergence of the Omicron Variant in the Swedish Population. _Available at SSRN as a preprint:_ [https://doi.org/10.3390/vaccines10122074](https://doi.org/10.3390/vaccines10122074) -* Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Ng, N., Leach, S., Gisslén, M., Hammar, N., Nyberg, F., Rosvall, M. (2022) An intersectional analysis of sociodemographic disparities in Covid-19 vaccination: a nationwide register-based study in Sweden. *Vaccine, 40*, 6640-6648. [https://doi.org/10.1016/j.vaccine.2022.09.065](https://doi.org/10.1016/j.vaccine.2022.09.065) +- Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Ng, N., Leach, S., Gisslén, M., Hammar, N., Nyberg, F., Rosvall, M. (2022) An intersectional analysis of sociodemographic disparities in Covid-19 vaccination: a nationwide register-based study in Sweden. _Vaccine, 40_, 6640-6648. [https://doi.org/10.1016/j.vaccine.2022.09.065](https://doi.org/10.1016/j.vaccine.2022.09.065) -* Mousa, S. I., Nyberg, F., Hajiebrahimi, M., Bertilsson, R., Nåtman, J., Santosa, A., Wettermark, B. (2022). Initiation of antihypertensive drugs to patients with confirmed COVID-19-A population-based cohort study in Sweden. *Basic & Clinical Pharmacology & Toxicology, 131*, 196-204. [https://doi.org/10.1111/bcpt.13766](https://doi.org/10.1111/bcpt.13766) +- Mousa, S. I., Nyberg, F., Hajiebrahimi, M., Bertilsson, R., Nåtman, J., Santosa, A., Wettermark, B. (2022). Initiation of antihypertensive drugs to patients with confirmed COVID-19-A population-based cohort study in Sweden. _Basic & Clinical Pharmacology & Toxicology, 131_, 196-204. [https://doi.org/10.1111/bcpt.13766](https://doi.org/10.1111/bcpt.13766) -* Sundh, J., Ekström, M., Palm, A., Ljunggren, M., Emilsson, Ö. I., Grote, L., Cajander, S., Li, H., Nyberg, F. (2022). COVID-19 and Risk of Oxygen-dependent Chronic Respiratory Failure: A National Cohort Study. *American Journal of Respiratory and Critical Care Medicine, 206* 506-509. [https://doi.org/10.1164/rccm.202202-0323le](https://doi.org/10.1164/rccm.202202-0323le) +- Sundh, J., Ekström, M., Palm, A., Ljunggren, M., Emilsson, Ö. I., Grote, L., Cajander, S., Li, H., Nyberg, F. (2022). COVID-19 and Risk of Oxygen-dependent Chronic Respiratory Failure: A National Cohort Study. _American Journal of Respiratory and Critical Care Medicine, 206_ 506-509. [https://doi.org/10.1164/rccm.202202-0323le](https://doi.org/10.1164/rccm.202202-0323le) -* Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Leach, S., Gisslén, M., Hammar, N., Rosvall, M., Nyberg, F. (2022). The social patterning of Covid-19 vaccine uptake in older adults: A register-based cross-sectional study in Sweden. *The Lancet Regional Health - Europe, 15,* 100331. [https://doi.org/10.1016/j.lanepe.2022.100331](https://doi.org/10.1016/j.lanepe.2022.100331) +- Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Leach, S., Gisslén, M., Hammar, N., Rosvall, M., Nyberg, F. (2022). The social patterning of Covid-19 vaccine uptake in older adults: A register-based cross-sectional study in Sweden. _The Lancet Regional Health - Europe, 15,_ 100331. [https://doi.org/10.1016/j.lanepe.2022.100331](https://doi.org/10.1016/j.lanepe.2022.100331) -* Santosa, A., Franzén, S., Nåtman, J., Wettermark, B., Parmryd, I., Nyberg, F. (2022). Protective effects of statins on COVID-19 risk, severity and fatal outcome – a nationwide Swedish cohort study. *Research Square* [https://doi.org/10.21203/rs.3.rs-1432508/v1](https://doi.org/10.21203/rs.3.rs-1432508/v1) +- Santosa, A., Franzén, S., Nåtman, J., Wettermark, B., Parmryd, I., Nyberg, F. (2022). Protective effects of statins on COVID-19 risk, severity and fatal outcome – a nationwide Swedish cohort study. _Research Square_ [https://doi.org/10.21203/rs.3.rs-1432508/v1](https://doi.org/10.21203/rs.3.rs-1432508/v1) -* Nwaru, C.A., Santosa, A., Franzén, S., Nyberg, F. (2022). Occupation and COVID-19 diagnosis, hospitalisation and ICU admission among foreign-born and Swedish-born employees: a register-based study. *Journal of Epidemiology and Community Health, 7,* jech-2021-218278. [https://doi.org/10.1136/jech-2021-218278](https://doi.org/10.1136/jech-2021-218278) +- Nwaru, C.A., Santosa, A., Franzén, S., Nyberg, F. (2022). Occupation and COVID-19 diagnosis, hospitalisation and ICU admission among foreign-born and Swedish-born employees: a register-based study. _Journal of Epidemiology and Community Health, 7,_ jech-2021-218278. [https://doi.org/10.1136/jech-2021-218278](https://doi.org/10.1136/jech-2021-218278) -* Nyberg, F., Franzén, S., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Björck, S., Gisslén, M. (2021). Swedish Covid-19 Investigation for Future Insights – A Population Epidemiology Approach Using Register Linkage (SCIFI-PEARL). *Clinical Epidemiology, 13,* 649-659. [https://doi.org/10.2147/CLEP.S312742](https://doi.org/10.2147/CLEP.S312742) +- Nyberg, F., Franzén, S., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Björck, S., Gisslén, M. (2021). Swedish Covid-19 Investigation for Future Insights – A Population Epidemiology Approach Using Register Linkage (SCIFI-PEARL). _Clinical Epidemiology, 13,_ 649-659. [https://doi.org/10.2147/CLEP.S312742](https://doi.org/10.2147/CLEP.S312742) -* Nyberg, F., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Kirui, B. K., Gisslén, M. (2021). Adverse events of special interest for COVID-19 vaccines - background incidences vary by sex, age and time period and are affected by the pandemic. *medRxiv* [https://doi.org/10.1101/2021.10.04.21263507](https://doi.org/10.1101/2021.10.04.21263507) +- Nyberg, F., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Kirui, B. K., Gisslén, M. (2021). Adverse events of special interest for COVID-19 vaccines - background incidences vary by sex, age and time period and are affected by the pandemic. _medRxiv_ [https://doi.org/10.1101/2021.10.04.21263507](https://doi.org/10.1101/2021.10.04.21263507) ## Information about SCIFI-PEARL diff --git a/content/english/dashboards/_index.md b/content/english/dashboards/_index.md index 66e13aec7..63ac456d7 100644 --- a/content/english/dashboards/_index.md +++ b/content/english/dashboards/_index.md @@ -7,4 +7,10 @@ menu: name: Dashboards identifier: dashboards post: Dashboards are pages describing research done on a given subject. They include visualisations of and links to data from the research groups(s) involved. See all dashboards +aliases: + - /dashboards + - /visualisations + - /dashboards_topics/ --- + +The *Dashboards* section describes research done on a given subject relevant to pandemic preparedness research by researchers affiliated with a Swedish research institute. Specifically, in our case we write about COVID-19 and SARS-CoV-2, infectious diseases in general, antibiotic resistance, and Mpox. The goal is to highlight and visualise openly shared data that can potentially be used by many other researchers to make further discoveries. diff --git a/content/english/dashboards/covid_publications.md b/content/english/dashboards/covid_publications.md index 276402734..033566a9a 100644 --- a/content/english/dashboards/covid_publications.md +++ b/content/english/dashboards/covid_publications.md @@ -10,6 +10,7 @@ menu: name: "COVID-19 publication overview" aliases: - /projects/dashboard/ +dashboards_topics: [COVID-19, Infectious diseases] --- The visualisations on this page evaluate the development of COVID-19 and SARS-CoV-2 research across Sweden by assessing publication output. Specifically, we consider multiple aspects of journal publications and preprints where at least one author has an affiliation with a Swedish research institute. The database containing the publications themselves [can be found on this page](/publications/). Note that our database is manually curated and, as such, may not be exhaustive. The full database is available for download and use for other purposes, please see [DOI: 10.17044/scilifelab.14124014](https://doi.org/10.17044/scilifelab.14124014) for details. @@ -41,13 +42,13 @@ These wordclouds display the words and two word phrases that appear most frequen #### All publications -
+
#### Publications attributed to particular research funders Wordclouds are displayed for each funder that we identified as having been associated with at least 20 publications in the database. -
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
+
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
## Most frequent words or two word phrases in the abstracts @@ -57,10 +58,10 @@ These wordclouds display the words and two word phrases that appear most frequen #### All publications -
+
#### Publications attributed to particular research funders Wordclouds are displayed for each funder that we identified as having been associated with at least 20 publications in the database. -
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
+
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
diff --git a/content/english/dashboards/crush_covid.md b/content/english/dashboards/crush_covid.md index d16abef99..79571aeee 100644 --- a/content/english/dashboards/crush_covid.md +++ b/content/english/dashboards/crush_covid.md @@ -4,25 +4,40 @@ description: CRUSH Covid maps outbreaks in Uppsala County by visualising the num banner: /dashboard_thumbs/CRUSH.png toc: false menu: - dashboard_menu: - identifier: crush_covid - name: "CRUSH Covid Uppsala (Partner)" + dashboard_menu: + identifier: crush_covid + name: "CRUSH Covid Uppsala (Partner)" aliases: - - /data_types/health_data/crush_covid/ + - /data_types/health_data/crush_covid/ +dashboards_topics: [COVID-19, Infectious diseases] --- -

CRUSH Covid is a collaboration between the Region Uppsala and researchers from five different departments at Uppsala University. The purpose of the project is to map outbreaks of COVID-19 in Uppsala County and attempt to mitigate their impact by keeping the public informed. Information on the CRUSH Covid dashboard was originally updated weekly, but the update schedule changed in autumn 2022. The dashboard will now be updated every 1-2 months.

-

CRUSH Covid is led by Mats Martinell (senior lecturer at the Department of Public Health and Caring Sciences, General Medicine and Preventive Medicine, Uppsala University) and Tove Fall (professor of Molecular Epidemiology at the Department of Medical Sciences, Molecular Epidemiology, Uppsala University). Sewage analysis is supported by SciLifeLab and Uppsala Vatten. Modelling work is supported by Vinnova.

-

For questions and feedback, please contact Georgios Varotsis (georgios.varotsis@medsci.uu.se).

CRUSH Covid has received ethical approval from the Swedish Ethical Review Authority (ref. no. 2020-04210, 2020-06315 and 2020-06501).

-
CRUSH Covid
Uppsala University
-
Region Uppsala
+
+
+
+

CRUSH Covid Uppsala is a research project in which Region Uppsala collaborates with researchers from five different research departments at Uppsala University. The purpose of the project is to map outbreaks of COVID-19 in Uppsala County and to try to mitigate the impact of outbreaks by informing the public.

+ +

CRUSH Covid is led by Mats Martinell (senior lecturer at the Department of Public Health and Caring Sciences, General Medicine and Preventive Medicine, Uppsala University) and Tove Fall (professor of Molecular Epidemiology at the Department of Medical Sciences, Molecular Epidemiology, Uppsala University). Sewage analysis is supported by SciLifeLab and Uppsala Vatten. Modelling work is supported by Vinnova.

+ +

For questions and feedback, please contact Georgios Varotsis (georgios.varotsis@medsci.uu.se).

+ +

CRUSH Covid has received ethical approval from the Swedish Ethical Review Authority (ref. no. 2020-04210, 2020-06315 and 2020-06501).

+ +

The CRUSH Covid team has released data and information about the project in two places. The primary source of data and information was their custom shiny app, named the CRUSH Covid dashboard, which contained data visualisations as well as reports. As of September 2022, updates to the app ceased. The Portal's dashboard (i.e. this webpage) is the secondary source of data and information for this project. The data generated from CRUSH Covid between 2020-2022 can be downloaded directly below.

+
+
+
CRUSH Covid
+
Uppsala University
+
Region Uppsala
+
+
+
#### Download CRUSH Covid data -
Last updated: {{% crush_covid_modified %}}.
+
Last updated: 2022-09-15
-* [Number of tests and % positivity by postal code in Uppsala County, .csv file](https://blobserver.dckube.scilifelab.se/blob/CRUSH_Covid_data.csv) (updated every 1-2 months from September 2022). - For each postal code which is found within the Uppsala län, the dataset contains weekly data on cases per capita, tests per capita and % positivity. Our estimates are calculated based on the adult population of each postal code (individuals 15 years of age and older). For reference, both the total population and the adult population are included. +- [Number of tests and % positivity by postal code in Uppsala County, .csv file](https://blobserver.dc.scilifelab.se/blob/CRUSH_Covid_data.csv). For each postal code which is found within the Uppsala län, the dataset contains weekly data on cases per capita, tests per capita and % positivity. The estimates are calculated based on the adult population of each postal code (individuals 15 years of age and older). For reference, both the total population and the adult population are included. #### Interactive dashboard and reports diff --git a/content/english/dashboards/npc-statistics.md b/content/english/dashboards/npc-statistics.md index c7028c40e..3f99ae147 100644 --- a/content/english/dashboards/npc-statistics.md +++ b/content/english/dashboards/npc-statistics.md @@ -9,6 +9,7 @@ menu: toc: false aliases: - /data_types/health_data/npc-statistics/ +dashboards_topics: [COVID-19, Infectious diseases] ---
diff --git a/content/english/dashboards/post_covid.md b/content/english/dashboards/post_covid.md index 965790015..c13d930c7 100644 --- a/content/english/dashboards/post_covid.md +++ b/content/english/dashboards/post_covid.md @@ -10,6 +10,7 @@ menu: name: Post COVID-19 condition in Sweden aliases: - /data_types/health_data/post_covid/ +dashboards_topics: [COVID-19, Infectious diseases] --- Since the beginning of 2020, the COVID-19 pandemic has challenged healthcare and dramatically changed daily life for people worldwide. The severity of symptoms experienced by patients during the acute infection phase of COVID-19 disease varies between individuals from mild to severe. After this phase, there are usually no indications that the disease will have any long-term effects on their health, regardless of the severity of symptoms experienced during the acute infection phase. However, some patients continue to exhibit symptoms for prolonged periods after the acute phase. The symptoms experienced by such patients are broad, but can include, for example, deep fatigue, joint pain, ‘brain fog’ (difficulty concentrating on certain tasks for longer periods of time), and heart palpitations ([Brodin, 2021](https://doi.org/10.1038/s41591-020-01202-8), [Marx, 2021](https://doi.org/10.1038/s41592-021-01145-z)). These symptoms can have a significant impact on the patients' quality of life. @@ -49,7 +50,7 @@ These plots display the number of times that patients were assigned the diagnose #### Diagnosis U09.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U099_agesex_casedist.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U099_agesex_casedist.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/create_agesex_distcases.py). @@ -57,7 +58,7 @@ These plots display the number of times that patients were assigned the diagnose #### Diagnosis Z86.1A/U08.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U089_agesex_casedist.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U089_agesex_casedist.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/create_agesex_distcases.py). @@ -69,7 +70,7 @@ The maps below show the number of people that received the diagnoses of interest #### Diagnosis U09.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_population_U099.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_population_U099.json" height="500px" >}}
**Code used to produce plot:** [Data preparation script](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Script to produce map](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_population_U099.py). @@ -77,7 +78,7 @@ The maps below show the number of people that received the diagnoses of interest #### Diagnosis Z86.1A/U08.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_population_U089.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_population_U089.json" height="500px" >}}
**Code used to produce plot:** [Data preparation script](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Script to produce map](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_population_U089.py). @@ -93,7 +94,7 @@ The maps below show the number of people that received the diagnoses of interest #### Diagnosis U09.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U099.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U099.json" height="500px" >}}
**Code used to produce plot:** [Data preparation script](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Script to produce map](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_cases_U099.py). @@ -101,7 +102,7 @@ The maps below show the number of people that received the diagnoses of interest #### Diagnosis Z86.1A/U08.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U089.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U089.json" height="500px" >}}
**Code used to produce plot:** [Data preparation script](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Script to produce map](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_cases_U089.py). @@ -115,7 +116,7 @@ The maps below show the number of people that received the diagnoses of interest The below table displays the most common types of diagnosis (diagnosis groups) that have been reported together with the *U09.9 (ICD-10-SE) - Postinfectious state associated with COVID-19, unspecified* diagnosis. In particular, the values in the table represent the amount of individuals that received the *U09.9* diagnosis alongside one of the diagnoses below. The data was recorded between 16th October 2020 and the most recent data update (see above).
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/accompdiag_table.json" height="527px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/accompdiag_table.json" height="527px" >}}
*Note that an individual may have more than one of the accompanying diagnoses. However, if an individual has the same issue on multiple doctor visits/healthcare contacts, the diagnosis will only be counted once* @@ -131,7 +132,7 @@ The below plot shows the number of times that patients given the diagnoses of in
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/weeklycontacts_healthcare.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/weeklycontacts_healthcare.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/weeklycontacts_healthcare.py). @@ -147,7 +148,7 @@ The below plots show the number of times that patients given one of the diagnose
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U099_healthcare_divsex.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U099_healthcare_divsex.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/weeklycontacts_healthcare_divsex.py). @@ -159,7 +160,7 @@ The below plots show the number of times that patients given one of the diagnose
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U089_healthcare_divsex.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U089_healthcare_divsex.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/weeklycontacts_healthcare_divsex.py). diff --git a/content/english/dashboards/serology-statistics.md b/content/english/dashboards/serology-statistics.md index f15d73043..353939aaf 100644 --- a/content/english/dashboards/serology-statistics.md +++ b/content/english/dashboards/serology-statistics.md @@ -13,6 +13,7 @@ menu: name: Antibody tests for SARS-CoV-2 at SciLifeLab aliases: - /data_types/health_data/serology-statistics/ +dashboards_topics: [COVID-19, Infectious diseases] ---
Graphs on this page are based on data as per {{% serology_date_modified %}}.
diff --git a/content/english/dashboards/symptom_study_sweden.md b/content/english/dashboards/symptom_study_sweden.md index 7eae30494..04496328a 100644 --- a/content/english/dashboards/symptom_study_sweden.md +++ b/content/english/dashboards/symptom_study_sweden.md @@ -10,6 +10,7 @@ menu: name: "COVID Symptom Study Sweden (Partner)" aliases: - /data_types/health_data/symptom_study_sweden/ +dashboards_topics: [COVID-19, Infectious diseases] --- **COVID Symptom Study Sweden** is a national research initiative for large-scale data collection and analysis of symptoms, exposure, and risk factors associated with the COVID-19 infection. The project is run by Lund University and Uppsala University in collaboration with King’s College London and Zoe Global Ltd. COVID Symptom Study Sweden is led by prof. Paul Franks and prof. Maria Gomez (Lund University) as well as prof. Tove Fall (Uppsala University). @@ -25,7 +26,7 @@ COVID Symptom Study Sweden has two main objectives. The first objective is to in Below are estimates of the prevalence of symptomatic COVID-19 cases in various counties in Sweden. The estimates are made based on the app users' data using the prediction model developed by the team of researchers behind the COVID Symptoms Study Sweden; see [this page](https://www.covid19app.lu.se/artikel/uppdatering-av-prediktionsmodell-0) for more information about the prediction model (only available in Swedish). More detailed prevalence estimates and other results can be explored [on the official dashboard of the project results](https://csss-resultat.shinyapps.io/csss_dashboard/).
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/symptoms_map_english.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/symptoms_map_english.json" height="500px" >}}
#### Access to the collected data for use in other research projects diff --git a/content/english/dashboards/vaccines.md b/content/english/dashboards/vaccines.md index 5e394c588..843fffe63 100644 --- a/content/english/dashboards/vaccines.md +++ b/content/english/dashboards/vaccines.md @@ -10,6 +10,7 @@ menu: name: "Vaccine administration: COVID-19" aliases: - /data_types/health_data/vaccines/ +dashboards_topics: [COVID-19, Infectious diseases] ---
@@ -69,7 +70,7 @@ Vaccination data is spread between multiple tabs of the [FoHM data file](https:/ **Note on the graph:** Click on the coloured squares in the legend of the below graph to toggle which datasets are displayed. A single click will toggle just that dataset on/off. It is possible to display only one of the datasets by double-clicking on the desired dataset.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/Total_vaccinated_barchart.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/Total_vaccinated_barchart.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_indicator_barchart.py). @@ -89,7 +90,7 @@ The below graph shows vaccine coverage across the whole of Sweden. We use the 'w **Note on the graph:** Click on the coloured squares in the legend of the below graph to toggle which datasets are displayed. A single click will toggle just that dataset on/off. It is possible to display only one of the datasets by double-clicking on the desired dataset.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/vaccine_timeseries_pop_barchart.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/vaccine_timeseries_pop_barchart.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_timeseries_barchart.py). @@ -105,7 +106,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least one vaccine dose
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/onedose_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/onedose_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -113,7 +114,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least two vaccine doses
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/twodoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/twodoses_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -121,7 +122,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least three vaccine doses
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/threedoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/threedoses_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -129,7 +130,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least four vaccine doses
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/fourdoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/fourdoses_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -140,10 +141,10 @@ Please note the differences between the two below maps. Coverage appears to be v
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/fivedoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/fivedoses_pop_map.json" height="500px" >}}
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/fivedoses_elig_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/fivedoses_elig_map.json" height="500px" >}}
@@ -162,7 +163,7 @@ Data is available on the number of individuals aged 65-69 that have received the **Note about the heatmap:** A white colouration indicates that no data is available for that age group.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/vaccine_heatmap.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/vaccine_heatmap.json" height="500px" >}}
**Code used to produce plot:** [Script to produce heatmap](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_heatmaps.py). diff --git a/content/english/dashboards/wastewater/introduction.md b/content/english/dashboards/wastewater/_index.md similarity index 97% rename from content/english/dashboards/wastewater/introduction.md rename to content/english/dashboards/wastewater/_index.md index dee95d72a..e475fe91a 100644 --- a/content/english/dashboards/wastewater/introduction.md +++ b/content/english/dashboards/wastewater/_index.md @@ -2,7 +2,7 @@ title: Wastewater-based epidemiology in Sweden description: Surveillance of wastewater for pathogens can be an effective means of predicting upcoming outbreaks. This dashboard contains data originating from the multiple research groups across Sweden. banner: /dashboard_thumbs/wastewater.jpg -toc: false +inline_toc: true type: wastewater menu: dashboard_menu: @@ -19,7 +19,8 @@ plotly: true aliases: - /data_types/environment/wastewater/ - /data_types/environment/ - - /dashboards/wastewater + - /dashboards/wastewater/introduction/ +dashboards_topics: [COVID-19, Infectious diseases] ---
Please note: the wastewater dashboard is undergoing expansion over the next few months. We have now separated the data related to the amount of SARS-CoV-2 in wastewater according to the research group that collected and analysed the data (see below for information about the groups involved). In the coming months, more information and data will be added about SARS-CoV-2 and on other infectious diseases.
@@ -47,7 +48,7 @@ All code used to produce the visualisations on the tabs on this dashboard is ava Below is a map showing the wastewater treatment plants (WWTPs) from which wastewater samples are being collected and analysed by groups sharing data on this dashboard.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_map_test.json" height="600px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_map_test.json" height="600px" >}}
**Code used to produce map:** [Script to produce map](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/interactive_wastewater_map.py). diff --git a/content/english/dashboards/wastewater/covid_quantification.md b/content/english/dashboards/wastewater/covid_quantification.md deleted file mode 100644 index a6eee1dde..000000000 --- a/content/english/dashboards/wastewater/covid_quantification.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -title: Wastewater-based epidemiology in Sweden -toc: false -type: wastewater -menu: - wastewater: - name: SARS-CoV-2 quantification - weight: 20 -plotly: true ---- - -## Quantification of SARS-CoV-2 across Sweden -
- -All three groups involved in this dashboard quantify the levels of SARS-CoV-2 in wastewater. **The groups each measure different regions of Sweden, and some regions are covered by multiple groups**. Below are lists of the areas covered by each group. Click on the name of the group to go to their SARS-CoV-2 quantification data. - -- [**Gothenburg university (GU):**](/dashboards/wastewater/covid_quant_gu/) Quantification of the level of SARS-CoV-2 in wastewater from Gothenburg by the Norder group at GU. - -- [**SEEC-KTH node:**](/dashboards/wastewater/covid_quant_kth/) Quantification of the levels of SARS-CoV-2 in wastewater from Stockholm and Malmö by the SEEC-KTH node. - -- [**SEEC-SLU node:**](/dashboards/wastewater/covid_quant_slu/) Quantification of the levels of SARS-CoV-2 in wastewater from multiple sites, including Stockholm, Malmö, Gothenburg, Uppsala, Västerås, Örebro, Umeå, and many others, by the SEEC-SLU node. diff --git a/content/english/dashboards/wastewater/covid_quantification/_index.md b/content/english/dashboards/wastewater/covid_quantification/_index.md new file mode 100644 index 000000000..d4cd20dc6 --- /dev/null +++ b/content/english/dashboards/wastewater/covid_quantification/_index.md @@ -0,0 +1,20 @@ +--- +title: SARS-CoV-2 quantification +type: wastewater +menu: + wastewater: + name: SARS-CoV-2 quantification + weight: 20 +plotly: true +--- + +## Quantification of SARS-CoV-2 across Sweden +
+ +All three groups involved in this dashboard quantify the levels of SARS-CoV-2 in wastewater. **The groups each measure different regions of Sweden, and some regions are covered by multiple groups**. Below are lists of the areas covered by each group. Click on the name of the group to go to their SARS-CoV-2 quantification data. + +- [**Gothenburg university (GU):**](/dashboards/wastewater/covid_quantification/covid_quant_gu/) Quantification of the level of SARS-CoV-2 in wastewater from Gothenburg by the Norder group at GU. + +- [**SEEC-KTH node:**](/dashboards/wastewater/covid_quantification/covid_quant_kth/) Quantification of the levels of SARS-CoV-2 in wastewater from Stockholm and Malmö by the SEEC-KTH node. + +- [**SEEC-SLU node:**](/dashboards/wastewater/covid_quantification/covid_quant_slu/) Quantification of the levels of SARS-CoV-2 in wastewater from multiple sites, including Stockholm, Malmö, Gothenburg, Uppsala, Västerås, Örebro, Umeå, and many others, by the SEEC-SLU node. diff --git a/content/english/dashboards/wastewater/covid_quant_GU.md b/content/english/dashboards/wastewater/covid_quantification/covid_quant_GU.md similarity index 95% rename from content/english/dashboards/wastewater/covid_quant_GU.md rename to content/english/dashboards/wastewater/covid_quantification/covid_quant_GU.md index 40773ed20..daa082294 100644 --- a/content/english/dashboards/wastewater/covid_quant_GU.md +++ b/content/english/dashboards/wastewater/covid_quantification/covid_quant_GU.md @@ -1,7 +1,8 @@ --- title: Amount of SARS-CoV-2 in wastewater (GU) -toc: false plotly: true +aliases: + - /dashboards/wastewater/covid_quant_gu/ ---
@@ -55,7 +56,7 @@ Influent wastewater samples were collected from Ryaverket wastewater treatment p
-->
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_gothenburg.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_gothenburg.json" height="550px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/gothenburg_covid.py). @@ -68,7 +69,7 @@ Influent wastewater samples were collected from Ryaverket wastewater treatment p ## Dataset -**Download the data:** [Quantification of SARS-CoV-2 and enteric viruses in wastewater](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_gu_allviruses.xlsx). Results are available for SARS-CoV-2 from week 7 of 2020 (with a small gap over winter 2022-2023), and for enteric viruses from week 2 of 2023. Updated weekly.\ +**Download the data:** [Quantification of SARS-CoV-2 and enteric viruses in wastewater](https://blobserver.dc.scilifelab.se/blob/wastewater_data_gu_allviruses.xlsx). Results are available for SARS-CoV-2 from week 7 of 2020 (with a small gap over winter 2022-2023), and for enteric viruses from week 2 of 2023. Updated weekly.\ **Contact:** **How to cite the dataset:** Norder, H., Nyström, K. Patzi Churqui, M., Tunovic, T., Wang, H. (2023). Detection of SARS-CoV-2 and other human enteric viruses in wastewater from Gothenburg. [https://doi.org/10.17044/scilifelab.22510501](https://doi.org/10.17044/scilifelab.22510501). diff --git a/content/english/dashboards/wastewater/covid_quant_KTH.md b/content/english/dashboards/wastewater/covid_quantification/covid_quant_KTH.md similarity index 94% rename from content/english/dashboards/wastewater/covid_quant_KTH.md rename to content/english/dashboards/wastewater/covid_quantification/covid_quant_KTH.md index bea69c63e..b86dab564 100644 --- a/content/english/dashboards/wastewater/covid_quant_KTH.md +++ b/content/english/dashboards/wastewater/covid_quantification/covid_quant_KTH.md @@ -1,6 +1,8 @@ --- title: Amount of SARS-CoV-2 in wastewater (SEEC-KTH) plotly: true +aliases: + - /dashboards/wastewater/covid_quant_kth/ ---
@@ -62,7 +64,7 @@ Please also note that although the same methods are used for all cities shown on
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_combined_stockholm.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_combined_stockholm.json" height="550px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/combined_stockholm_regular.py). @@ -76,7 +78,7 @@ Please also note that although the same methods are used for all cities shown on
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_kthmalmo.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_kthmalmo.json" height="550px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/quant_malmo_kthplot.py). @@ -89,7 +91,7 @@ Please also note that although the same methods are used for all cities shown on ## Dataset -**Download the data:** [N3-gene copy number per PMMoV gene copy number; Excel file](https://blobserver.dckube.scilifelab.se/blob/stockholm_wastewater_method_Sep_2021.xlsx). Results are available (partially) starting from week 16 of 2020 for Stockholm and starting from week 39 of 2021 for Malmö; updated weekly.\ +**Download the data:** [N3-gene copy number per PMMoV gene copy number; Excel file](https://blobserver.dc.scilifelab.se/blob/stockholm_wastewater_method_Sep_2021.xlsx). Results are available (partially) starting from week 16 of 2020 for Stockholm and starting from week 39 of 2021 for Malmö; updated weekly.\ **Contact:** **How to cite dataset:** @@ -103,7 +105,7 @@ After concentration, filtering, and preparation, the samples are analysed using ## Archived data -- [Historic data for Stockholm; Gene copy number/week (raw wastewater) with bovine + PMMoV factor between April 2020 and August 2021](/dashboards/wastewater/historic_stockholm). +- [Historic data for Stockholm; Gene copy number/week (raw wastewater) with bovine + PMMoV factor between April 2020 and August 2021](/dashboards/wastewater/covid_quantification/historic_stockholm).
diff --git a/content/english/dashboards/wastewater/covid_quant_SLU.md b/content/english/dashboards/wastewater/covid_quantification/covid_quant_SLU.md similarity index 95% rename from content/english/dashboards/wastewater/covid_quant_SLU.md rename to content/english/dashboards/wastewater/covid_quantification/covid_quant_SLU.md index fce2714db..b8f8e0101 100644 --- a/content/english/dashboards/wastewater/covid_quant_SLU.md +++ b/content/english/dashboards/wastewater/covid_quantification/covid_quant_SLU.md @@ -1,6 +1,8 @@ --- title: Amount of SARS-CoV-2 in wastewater (SEEC-SLU) plotly: true +aliases: + - /dashboards/wastewater/covid_quant_slu/ ---
@@ -19,7 +21,7 @@ The data and visualisation on this page are usually updated weekly, typically on SLU-SEEC collect and analyse samples from multiple areas. The below table shows details about each of these sites. The table lists the towns/cities monitored, wastewater treatment plants (WWTP) that samples were collected from, the number of people in the catchment area (Number of people), and the dates that monitoring by SLU-SEEC started and ended monitoring (Start and End date, respectively). A value of 'null' for the end date indicates that collection is ongoing. An asterisk next to the number of people indicates that the figure is preliminary.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_slusites.json" height="775px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_slusites.json" height="775px" >}}
@@ -47,7 +49,7 @@ SLU-SEEC collect and analyse samples from multiple areas. The below table shows
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_slusites.json" height="600px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_slusites.json" height="600px" >}}
@@ -94,7 +96,7 @@ Please note that although the same methods are used for all cities shown on this
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_combined_slu_regular.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_combined_slu_regular.json" height="550px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/combined_slu_regular.py). @@ -107,7 +109,7 @@ Please note that although the same methods are used for all cities shown on this ## Reports from the research group -The group provide reports to summarise their latest findings. The latest report is available [here](https://blobserver.dckube.scilifelab.se/blob/Latest_weekly_report_SEEC-SLU) (only available in Swedish). +The group provide reports to summarise their latest findings. The latest report is available [here](https://blobserver.dc.scilifelab.se/blob/Latest_weekly_report_SEEC-SLU) (only available in Swedish). ## Dataset @@ -133,7 +135,7 @@ The data in the below graph and datafile represent the ratio of the copy numbers ## Archived data -- [Historic data for Örebro and Umeå; amount of SARS-CoV-2 in Umeå and Örebro wastewater between October 2020 and June 2021](/dashboards/wastewater/historic_orebro_umea). +- [Historic data for Örebro and Umeå; amount of SARS-CoV-2 in Umeå and Örebro wastewater between October 2020 and June 2021](/dashboards/wastewater/covid_quantification/historic_orebro_umea).
diff --git a/content/english/dashboards/wastewater/historic_orebro_umea.md b/content/english/dashboards/wastewater/covid_quantification/historic_orebro_umea.md similarity index 91% rename from content/english/dashboards/wastewater/historic_orebro_umea.md rename to content/english/dashboards/wastewater/covid_quantification/historic_orebro_umea.md index 12ece8f0b..8b5ba2360 100644 --- a/content/english/dashboards/wastewater/historic_orebro_umea.md +++ b/content/english/dashboards/wastewater/covid_quantification/historic_orebro_umea.md @@ -1,8 +1,8 @@ --- title: "Historic data for Örebro and Umeå" -toc: false aliases: - /data_types/environment/wastewater/historic_orebro_umea/ + - /dashboards/wastewater/historic_orebro_umea/ --- This page displays data on the amount of SARS-CoV-2 in Umeå and Örebro wastewater between October 2020 and June 2021. After June 2021, a new method is used for analyses of wastewater, and [the most recent data can be found here](../). @@ -15,7 +15,7 @@ In the plots plots below, the amount of SARS-CoV-2 for each week is measured/dep ### Amount of SARS-CoV-2 in Umeå wastewater between between October 2020 and June 2021 -**Download the data:** [Gene copy number change (%) relative to 6th Nov 2020 and flow level at each measurement day and weekly numbers, Excel file.](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_Umeaa.xlsx). Data will be available from week 44 of 2020 until week 22 of 2021. +**Download the data:** [Gene copy number change (%) relative to 6th Nov 2020 and flow level at each measurement day and weekly numbers, Excel file.](https://blobserver.dc.scilifelab.se/blob/wastewater_data_Umeaa.xlsx). Data will be available from week 44 of 2020 until week 22 of 2021. **How to cite:** Malmberg, M., Myrmel, M. & Khatri, M. Dataset of SARS-CoV-2 in wastewater in Umeå, Sweden. [https://doi.org/10.17044/scilifelab.14376881.v1](https://doi.org/10.17044/scilifelab.14376881.v1) (2021). @@ -34,7 +34,7 @@ Malmberg, M., Myrmel, M. & Khatri, M. Dataset of SARS-CoV-2 in wastewater in Ume ### Amount of SARS-CoV-2 in Örebro wastewater between October 2020 and June 2021 -**Download the data:** [Gene copy number change (%) relative to 6th Nov 2020 and flow level at each measurement day and weekly numbers, Excel file.](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_Orebro.xlsx). Data will be available from week 42 of 2020 until week 22 of 2021. +**Download the data:** [Gene copy number change (%) relative to 6th Nov 2020 and flow level at each measurement day and weekly numbers, Excel file.](https://blobserver.dc.scilifelab.se/blob/wastewater_data_Orebro.xlsx). Data will be available from week 42 of 2020 until week 22 of 2021. **How to cite:** Malmberg, M., Myrmel, M. & Khatri, M. Dataset of SARS CoV-2 in wastewater in Örebro, Sweden. [https://doi.org/10.17044/scilifelab.14377097.v1](https://doi.org/10.17044/scilifelab.14377097.v1) (2021). diff --git a/content/english/dashboards/wastewater/historic_stockholm.md b/content/english/dashboards/wastewater/covid_quantification/historic_stockholm.md similarity index 90% rename from content/english/dashboards/wastewater/historic_stockholm.md rename to content/english/dashboards/wastewater/covid_quantification/historic_stockholm.md index 0f7573c65..8df6fdc80 100644 --- a/content/english/dashboards/wastewater/historic_stockholm.md +++ b/content/english/dashboards/wastewater/covid_quantification/historic_stockholm.md @@ -1,8 +1,8 @@ --- title: "Historic data for Stockholm" -toc: false aliases: - /data_types/environment/wastewater/historic_stockholm/ + - /dashboards/wastewater/historic_stockholm/ --- This page displays data on the amount of SARS-CoV-2 in Stockholm between April 2020 and August 2021 calculated as Gene copy number/week (raw wastewater) with bovine + PMMoV factor. From September 2021 onwards, the method was changed. Please [see this page for the most recent data](./). @@ -13,7 +13,7 @@ After concentration, filtering, and preparation, the samples are analyzed using See also [the page of the research group where summaries of data and preliminary conclusions are presented](https://www.kth.se/water/research/covid-1.979048). -**Download the data:** [Gene copy number/week (raw wastewater) with bovine + PMMoV factor; Excel file.](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_Stockholm.xlsx) Numbers for Stockholm overall and divided by Inlet Henriksdal, Sickla, Hässelby, Järva, Riksby, and Käppala are available. Results are available (partially) starting from week 16 of 2020 and until week 34 of 2021. +**Download the data:** [Gene copy number/week (raw wastewater) with bovine + PMMoV factor; Excel file.](https://blobserver.dc.scilifelab.se/blob/wastewater_data_Stockholm.xlsx) Numbers for Stockholm overall and divided by Inlet Henriksdal, Sickla, Hässelby, Järva, Riksby, and Käppala are available. Results are available (partially) starting from week 16 of 2020 and until week 34 of 2021. **How to cite:** Cetecioglu Z G, Williams, C, Khatami, K, Atasoy, M, Nandy, P, Jafferali, M H, Birgersson, M. SARS-CoV-2 Wastewater Data from Stockholm, Sweden. [https://doi.org/10.17044/scilifelab.14315483](https://doi.org/10.17044/scilifelab.14315483) (2021). diff --git a/content/english/highlights/mettl3_localisation.md b/content/english/highlights/mettl3_localisation.md new file mode 100644 index 000000000..86195fbc6 --- /dev/null +++ b/content/english/highlights/mettl3_localisation.md @@ -0,0 +1,45 @@ +--- +title: METTL3 localisation during SARS-CoV-2 infection could highlight new novel antiviral strategy +date: 2023-06-02 +summary: Vaid and Mendez, and collaborators, studied how the gene expression profile of m6A mRNA is affected both during and after COVID-19 infection. All sequencing data and the source code for analysis are shared. +banner: /highlights/banners/mettl3_localisation_small.png +banner_large: /highlights/banners/mettl3_localisation.png +banner_caption: Graphical abstract (Image courtesy Vaid and Mendez et al. (2023)). +highlights_topics: [COVID-19, Infectious Diseases] +announcement: "This data highlight was also [published on the SciLifeLab Data Platform](https://data.scilifelab.se/highlights/mettl3_localisation/), as the work described in this highlight constitutes data-driven life science. The Platform is a hub for data-driven life science in Sweden, containing multiple relevant resources, tools, and services. It includes information on multiple subjects, including infectious diseases, please check out the [Data Platform](https://data.scilifelab.se/) for more." +--- + +During recent years, the rapid development of COVID-19 treatments and vaccines have proven crucial in controlling the spread of the SARS-COV-2 virus and mitigating the effects of the COVID-19 pandemic. The COVID-19 pandemic is now considered to be at an end, with the virus becoming endemic. However, it is still important that we continue to deepen our understanding of SARS-CoV-2 pathogenesis (e.g. the basic mechanisms of how the virus infects hosts, host-virus interactions, and host response). This is not only because SARS-CoV-2 continues to circulate in populations, but also to protect against future pandemics by this or similar viruses. + +The N6-Methyladenosine modification (m6A) is one of the most common cellular RNA modifications, and known to play a crucial role in the regulation of RNA metabolism during the stress response. However, little is known about how m6a expression is altered during SARS-CoV-2 infection or the impact that any changes might have. This knowledge may hold clues for novel or improved antiviral therapeutics for SARS-CoV-2. It may also increase our general knowledge about the mechanism of coronaviruses, and other potential pandemic threats. + +In a recent publication in _Genome Research_, researchers from Sweden, in collaboration with researchers from South Korea, India, and France (_First authors:_ Roshan Vaid and Akram Mendez, _Corresponding author:_ Tanmoy Mondal), studied how the gene expression profile of m6A mRNA is affected during and after COVID-19 infection. + +Vaid and Mendez _et al_ used Vero cells and air/liquid interface (ALI) cultures of human airway epithelia to study SARS-CoV-2 infection. The researchers chose to study three SARS-CoV-2 variants; B.1, B.1.1.7, and B.1.351. All three SARS-CoV-2 variants showed changes in the expression of genes related to RNA catabolism, including m6A readers and erasers. Whilst m6A was found to be abundantly detected in viral RNA, infection with the SARS-CoV-2 variants studied caused a loss of m6A in cellular RNAs. + +The methyltransferase METTL3/METTL14 complex, is the key cellular enzyme complex responsible for depositing m6A modifications. It is normally localised in the nucleus. The initial finding of the study by Vaid and Mendez _et al_, i.e. that there is a global loss of m6A peaks during SARS-CoV-2 infection, could indicate that the function of the METTL3/METTL14 is altered during SARS-CoV-2 infection. Vaid and Mendez _et al._ continued to investigate this finding and found that, during SARS-CoV-2 infection, METTL3 is partially relocalised from the nucleus to the cytoplasm. This change in localisation from nucleus to was more prominent during infection with the SARS-CoV-2 B.1 and B.1.1.7 variants, than with the SARS-CoV-2 B.1.351 variant. These results were consistent with the finding that there was a greater reduction in the level of m6a during infection with SARS-CoV-2 B.1 and B.1.1.7, compared to infection with B.1.351. In addition, the researchers were able to restore the localisation of the METTL3 by inhibiting the export protein XPO1. This inhibition also caused a recovery of m6A in cellular RNA, and increased mRNA expression. + +> "We were surprised by the drastic loss of host cell m6A RNA modification during SARS-CoV-2 infection. We do not know how long it takes to regain m6A post-infection. SARS-COV-2 may leave a long-lasting mark in the infected cells and this might provide clues to why some people have chronic symptoms that persist long after COVID.” says corresponding author Tanmoy Mondal. + +In conclusion, future pandemic preparedness efforts will require the development of new antiviral strategies and antiviral drugs. The findings from Vaid and Mendez _et al_ highlight how SARS-CoV-2 perturbs the m6A RNA modification pathway to deregulate cellular RNAs, and thereby limits stress granule formation. METTL3 localisation during SARS-CoV-2 infection could highlight a new novel antiviral strategy for treatment of COVID-19. Further studies into this potential novel strategy are warranted. + +#### Data + +The researchers first published their articles as a preprint on 22nd December 2022 on BioRXiv. They also adhere to Open Science by sharing data and code openly. + +- All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus [(GEO)](https://www.ncbi.nlm.nih.gov/geo/) under accession number [GSE188477](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE188477). +- The source code used for the analysis of data in this study is available on [GitHub](https://github.com/AkramMendez/m6a_sarscov2) and [in supplementary data](https://genome.cshlp.org/content/suppl/2023/03/24/gr.276407.121.DC1). + +#### Article + +DOI: [10.1101/gr.276407.121](https://doi.org/10.1101/gr.276407.121) + +Vaid, R., Mendez, A., Thombare, K., Burgos-Panadero, R., Robinot, R., Fonseca, B. F., Gandasi, N. R., Ringlander, J., Hassan Baig, M., Dong, J.-J., Cho, J. Y., Reinius, B., Chakrabarti, L. A., Nystrom, K., & Mondal, T. (2023). Global loss of cellular m6A RNA methylation following infection with different SARS-CoV-2 variants. In Genome Research (Vol. 33, Issue 3, pp. 299–313). + +#### Funding + +This work was funded by grants from the Swedish Research Council, Sweden-South Korea COVID-19 grant from the Swedish Research Council, Svenska Läkaresällskapet; Kungl. Vetenskaps- och Vitterhets-Samhället (KVVS), as well as several international funders. + +#### Infrastructure + +The core facility at Novum, BEA, Bioinformatics and Expression Analysis has supported with sequencing, and the Proteomic core facility at Gothenburg University supported with LC-MS/MS. Swedish National Infrastructure for Computing (SNIC) at UPPMAX has supported computations and data handling. diff --git a/content/english/pathogens/voc/omicron.md b/content/english/pathogens/voc/omicron.md index c57ae5186..74fb9d13f 100644 --- a/content/english/pathogens/voc/omicron.md +++ b/content/english/pathogens/voc/omicron.md @@ -34,7 +34,7 @@ This section presents a list of the available data related to Omicron (or relate #### Data available from research groups in Sweden
    -

  • The Swedish Environmental Epidemiology Center is collecting wastewater samples from cities across Sweden and investigating frequency of variant specific SARS-CoV-2 mutations. Samples from Kalmar, Umeå, Uppsala, Örebro, Stockholm, Malmö wastewater treatment plants were analyzed. Obtained data for weeks 49-52 of 2021 and 1-2 of 2022 can be downloaded here. PIs: Maja Malmberg (maja.malmberg@slu.se), Anna J. Székely (anna.szekely@slu.se), Zeynep Çetecioğlu Gürol (zeynepcg@kth.se).

    +

  • The Swedish Environmental Epidemiology Center is collecting wastewater samples from cities across Sweden and investigating frequency of variant specific SARS-CoV-2 mutations. Samples from Kalmar, Umeå, Uppsala, Örebro, Stockholm, Malmö wastewater treatment plants were analyzed. Obtained data for weeks 49-52 of 2021 and 1-2 of 2022 can be downloaded here. PIs: Maja Malmberg (maja.malmberg@slu.se), Anna J. Székely (anna.szekely@slu.se), Zeynep Çetecioğlu Gürol (zeynepcg@kth.se).

    Frequency of variant specific mutations

  • Proportion of Omicron and other variants of concern in positive samples sequenced in Region Skåne every week: Excel file for download. See also other data from Region Skåne.

    • diff --git a/content/english/topics/anbiobiotic-resistance.md b/content/english/topics/antibiotic-resistance.md similarity index 85% rename from content/english/topics/anbiobiotic-resistance.md rename to content/english/topics/antibiotic-resistance.md index 63972d6f8..b3ac0f9e5 100644 --- a/content/english/topics/anbiobiotic-resistance.md +++ b/content/english/topics/antibiotic-resistance.md @@ -1,13 +1,23 @@ --- title: Antibiotic resistance +description: Antibiotic resistance is a global health threat. It occurs when microorganisms develop resistance to antibiotics, impacting healthcare, and food security. Monitoring, research, and policies are crucial to combat resistance and develop effective treatments. +banner: /topic_thumbs/topic_antibiotic.jpg +credits: toc: false topic: Antibiotic resistance menu: topics_menu: name: Antibiotic resistance + identifier: antibiotic_resistance weight: 30 --- +
    +   +   Antibiotic resistance is a new topic on the Portal, and we are currently working on adding more + information. +
    + ## Background COVID-19 has been the focus of people across the world during the last two years of the current pandemic. **Antibiotic resistance**, by contrast, has been termed a 'silent pandemic' because whilst, like COVID-19, it is widespread and poses a real and present danger to public health, it is receiving relatively little attention. diff --git a/content/english/topics/covid-19.md b/content/english/topics/covid-19.md index 078715b0a..0b8f022af 100644 --- a/content/english/topics/covid-19.md +++ b/content/english/topics/covid-19.md @@ -1,16 +1,17 @@ --- title: COVID-19 +description: COVID-19, caused by SARS-CoV-2, is a global pandemic challenging societies worldwide. Vaccines are crucial, but research is ongoing to address early detection, variant identification, treatment development, and future preparedness. +banner: /topic_thumbs/topic_covid.jpg +credits: toc: false topic: COVID-19 menu: topics_menu: name: COVID-19 + identifier: covid_19 weight: 10 -header_illustration: /img/csm_coronavirus_mc.jpg --- - - ## Background Coronavirus disease (**COVID-19**) is an infectious disease caused by the novel SARS-CoV-2 virus. Since its start in early 2020, the COVID-19 pandemic has challenged societies worldwide. While most people infected experience mild to moderate respiratory illness and recover without treatment, the disease can be severe or fatal. Advanced age and underlying medical conditions, such as cardiovascular disease, diabetes, cancer, or chronic respiratory disease, have all been associated with increased risk of severe outcome. However, anyone independent of age or comorbidities is at risk for COVID-19 severe disease outcome. As of March 2022, more than 200 million confirmed cases and over 4 million deaths have been [reported](https://ourworldindata.org/). diff --git a/content/english/topics/infectious-diseases.md b/content/english/topics/infectious-diseases.md index d4dbc7b0b..7636743a8 100644 --- a/content/english/topics/infectious-diseases.md +++ b/content/english/topics/infectious-diseases.md @@ -1,10 +1,14 @@ --- title: Infectious diseases +description: Infectious diseases pose a global health threat. COVID-19 highlighted coronavirus impact, but diseases like Dengue, Ebola, HIV, Measles persist. Research is crucial for understanding pathogenicity, resistance, and developing treatments. +banner: /topic_thumbs/topic_infectious.jpg +credits: toc: false topic: Infectious diseases menu: topics_menu: name: Infectious diseases + identifier: infectious_diseases weight: 20 --- diff --git a/content/english/topics/mpox.md b/content/english/topics/mpox.md index 54b15941e..20adcd1bb 100644 --- a/content/english/topics/mpox.md +++ b/content/english/topics/mpox.md @@ -1,10 +1,14 @@ --- title: Mpox +description: Mpox, or Monkeypox, is a zoonotic disease caused by the mpox virus. It belongs to the Orthopoxvirus genus and is closely related to smallpox. While the virus is endemic to West and Central Africa, recent outbreaks in Europe, including Sweden, have raised concerns. +banner: /topic_thumbs/topic_mpox.jpg +credits: Image by UK Research and Innovation toc: false topic: Mpox menu: topics_menu: name: Mpox + identifier: mpox weight: 40 aliases: - /topics/monkeypox/ @@ -15,8 +19,6 @@ aliases:   This page is under development, with more resources being added shortly. In the meantime, check out the mpox page in our 'emerging pathogens' section. That page contains more extensive information and useful links to other related resources.
-For a fuller overview, refer to the [mpox page](/pathogens/mpox/) of our [emerging pathogens section](/pathogens/). - ## Background Mpox a.k.a. Monkeypox is caused by the mpox virus, and is a zoonotic disease (i.e. it is transmitted from animals to humans). The mpox virus is a member of the *Orthopoxvirus* genus in the family *Poxviridae*. It is closely related to the variola virus, which causes smallpox and has had a marked impact on human populations throughout history. [According to WHO](https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON385) there are currently two clades of mpox virus; the West African clade and the Congo Basin (Central African) clade. An outbreak began in Europe during May 2022. As of Septmber 2022, more than 20,000 cases have been detected in Europe, with 161 cases detected in Sweden and reported to the [Swedish Public Health Agency](https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/apkoppor-internationellt-maj-2022-/). diff --git a/content/svenska/dashboards/RECOVAC.md b/content/svenska/dashboards/RECOVAC.md index b10336623..910bc25b0 100644 --- a/content/svenska/dashboards/RECOVAC.md +++ b/content/svenska/dashboards/RECOVAC.md @@ -5,10 +5,11 @@ banner: /dashboard_thumbs/RECOVAC.png toc: true plotly: true menu: - dashboard_menu: - identifier: recovac - name: Registerbaserade vaccindata (RECOVAC) + dashboard_menu: + identifier: recovac + name: Registerbaserade vaccindata (RECOVAC) --- +
En svensk översättning av denna sida kommer inom kort. @@ -30,9 +31,9 @@ EPN Nr 2020-01800, 2020-05829, 2021-00267, 2021-00829, 2021-02106, 2021-04098, 2
All data last updated: {{% RECOVAC_date_modified %}}
-*All code used to produce the visualisations on this page is available on [GitHub](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/tree/main/RECOVAC). The particular scripts used in each case are linked below the plots.* +_All code used to produce the visualisations on this page is available on [GitHub](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/tree/main/RECOVAC). The particular scripts used in each case are linked below the plots._ -The visualisations on this page broadly relate to two types of data; (a) data regarding the Swedish population at large, and (b) data on individuals in the Swedish population with certain comorbidities (at the start of the pandemic, assessed as of 1 Jan 2020 based on information from 2015-2019). More detail about the data used to produce the visualisations is available in the two subsections below. Instructions on how to manipulate the interactive plots are provided above the plots in each subsection. +The visualisations on this page broadly relate to two types of data; (a) data regarding the Swedish population at large, and (b) data on individuals in the Swedish population with certain comorbidities (at the start of the pandemic, assessed as of 1st Jan 2020, based on information from 2015-2019). More detail about the data used to produce the visualisations is available in the two subsections below. Instructions on how to manipulate the interactive plots are provided above the plots in each subsection. In each case, the data are subdivided according to the number of doses received by individuals. @@ -40,7 +41,7 @@ In each case, the data are subdivided according to the number of doses received #### Information on vaccination coverage -The top graph in this section shows data on vaccine coverage (i.e. the proportion of the population that has received a given number of doses). The data are from the National Vaccination Register at the Public Health Agency of Sweden (PHAS) and population register data from Statistics Sweden (SCB). The data spans the period 21st December 2020 until 30th September 2022. +The top graph in this section shows data on vaccine coverage (i.e. the proportion of the population that has received a given number of doses). The data are from the National Vaccination Register at the Public Health Agency of Sweden (PHAS) and population register data from Statistics Sweden (SCB). COVID-19 vaccinations first became available in Sweden in late December 2020. They were first offered to those most at risk of developing serious symptoms (i.e. the elderly and those with certain comorbidities). For more details on the vaccine rollout in Sweden, please see our [page on vaccines](https://www.covid19dataportal.se/dashboards/vaccines/). @@ -48,14 +49,18 @@ There is a clear trend in the coverage for each dose. Specifically, the levels o #### Information on admission to intensive care units (ICU) -The lower graph in this section shows the number of people admitted to intensive care (ICU), and how many vaccine doses they had received upon admission. These data are from the registers already mentioned, as well as the Swedish Intensive Care Register (SIR), a healthcare quality register. The current data covers the period 2nd March 2020 - 30th September 2022. +The lower graph in this section shows the number of people admitted to intensive care (ICU), and how many vaccine doses they had received upon admission. These data are from the registers already mentioned, as well as the Swedish Intensive Care Register (SIR), a healthcare quality register. There are three major peaks in admissions to intensive care (ICU). The timing of these peaks corresponds to peaks in COVID-19 cases, specifically in spring 2020, winter 2021, and spring 2021, when the rates of infection were high. In order to infer the impact of vaccination on ICU admissions, it is best to directly compare the data over the same time period. After aligning the timeframes in the below graphs, it is clear that the number of patients admitted with one or two vaccine doses increases with the coverage of these doses. This is unsurprising, given that the vaccine does not completely protect against serious illness, especially after only a single dose. There is considerable evidence of the protective effect of vaccination, though. Firstly, a majority of admissions in most weeks were related to individuals that had not received any vaccine doses, despite the fact that this represents an increasingly smaller portion of the population over time. Secondly, the number of admissions decreases as vaccine coverage increases, particularly when considering the coverage of multiple doses. The absolute numbers are, of course, also affected by the changes in intensity of the pandemic. This means that they tend to be lower in the summer months and other periods when the spread of the infection was low, and higher over the winter-spring period. There is, however, some evidence of a de-coupling between case number and ICU admissions for the first time in winter 2021. Cases were generally high over this period (see e.g. data from the [Swedish public health agency](https://experience.arcgis.com/experience/09f821667ce64bf7be6f9f87457ed9aa)). This is evidence that vaccines are protective against the onset of serious illness, because it means a smaller proportion of cases led to ICU admission. This de-coupling is also evident through 2022. +##### Visualisations + +The graphs below have multiple interactive features. In brief, it is possible to view different parts of the data using the buttons above the graphs. For exmaple, it is possible to look only at data from only those over 60 years of age by clicking '>60'. The 'Align timeline' button will change the timeline of the graphs so that only period for which data is available for both types of data shown is visible. The 'Show all data' button can be used to see all of the available data for both datasets (the timelines of the two are not the same). +
@@ -74,7 +79,7 @@ The data are categorised according to the number of doses received, and each cat ##### Changing the timeframe displayed -The two graphs are shown on different time scales. This is because vaccinations were not made available until early 2021, whilst data on ICU admissions resulting from COVID-19 infection are available from March 2020. The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on ICU admission. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'View whole time series' button to view all of the available data. +The two graphs are shown on different time scales. This is in part because vaccinations were not made available until early 2021, whilst data on ICU admissions resulting from COVID-19 infection are available from March 2020. Further, the latest date for which data is available can differ between the two datasets. The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on ICU admission. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'Show all data' button to view all of the available data. ##### Accurately read data values @@ -84,15 +89,12 @@ It is possible to view the underlying data values by hovering the cursor over th When hovering over the plot with the cursor, additional grey icons appear in the top right. The +, -, and magnifying glass icons can be used to zoom in/out of the plot. Alternatively, it is possible to zoom into a given part of the graph by clicking and dragging with the cursor to select that portion. The autoscale and reset axes icons (which look like a box containing arrows and a house, respectively) can be used to scale the axes appropriately for the data selected. The plot can be downloaded in .png format by clicking on the camera icon. -
-
-
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/vaccination_RECO_timeseries_buttons.json" height="500px" >}}
+
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/ICUadmiss_vaccinationlevel_button.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/swedishpop_subplot_button.json" height="800px" >}}
**Code used to produce plots:** [Preparation for vaccine coverage data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/Swedishpop_vaccinecov_dataprep.py), [Graph of vaccine coverage](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/Swedishpop_vaccinecov_plotwbuttons.py), [Graph and data preparation for ICU admissions data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/Swedishpop_ICU_plotwbuttons.py). @@ -101,12 +103,16 @@ When hovering over the plot with the cursor, additional grey icons appear in the This section considers data on individuals with one of four comorbidities; cardiovascular disease, diabetes, respiratory disease, and cancer. The data were obtained from the registers mentioned in the section above, as well as the National Patient Register (NPR) and SmiNet. The NPR contains specialised outpatient and hospitalised inpatient care, and is held by the National Board of Health and Welfare (Socialstyrelsen). SmiNet is the national register for mandatory registration of notifiable infectious diseases, and is held at the PHAS. Coded diagnoses in 2015-2019 from the NPR are used to classify individuals by comorbidity conditions. Note that individuals may be included in one or more of these comorbidity groups. COVID-19 cases diagnosed among people with comorbidities are determined using a positive SARS-CoV-2 test from SmiNet and/or a diagnosis of COVID-19 in NPR (the overwhelming majority have a positive test). -The buttons in the below plots can be used to display data on a given comorbidity. The data in both graphs will change according to the comorbidity selected. Data related to vaccine coverage (upper graph) includes the period 21st December 2020 - 30th September 2022. Data on the incidence of COVID-19 (lower graph) instead spans the period February 2020 - 30th September 2022. All data available on a given comorbidity will be displayed by default, but the timelines can be aligned. +The buttons in the below plots can be used to display data on a given comorbidity (see the blow subsection for details). All data available on a given comorbidity will be displayed by default, this may mean that different timelines are shown in the two graphs. However, the timelines can be aligned so that more direct comparisons can be made between the data in the two graphs. + +There is clear evidence of the benefits of vaccination for patients with each comorbidity considered. When switching between comorbidities, it is evident that they follow the same overall pattern in terms of the number of COVID-19 cases detected, but there are variations in magnitude. Similar to the above data considering the population over 18, it is clear that the proportion of vaccinated individuals diagnosed with COVID-19 increases with vaccine coverage. This is expected because the vaccine is not completely effective at preventing infection (although it does have a higher effectiveness against the development of serious disease, as shown in many studies, see also the [above data on ICU admission rates](#swedish-population-in-general)). Thus, as the proportion of the population that is vaccinated increases, more vaccinated individuals are likely to develop COVID-19. Some evidence of a protective effect can already be inferred from a superficial consideration of the number of cases detected in autumn 2020 compared to autumn 2021. The initial reduction in cases that occurs as vaccination coverage increases could, in part, be attributed to a general reduction in cases over the 2021 summer months. There was a peak in cases during winter 2021, and it is clear that many of the cases occurred in vaccinated individuals, but this is not evidence that vaccines do not have a protective effect. Rather, it reflects that a large part of the population was vaccinated by this time. The peak in winter 2021 can be attributed to the emergence of the new Omicron virus variant that was first detected in November 2021. This variant spreads more easily than earlier variants, even in vaccinated individuals. The number of COVID-19 cases presented in the graph below, and the number of ICU admissions in the graph in the section above, do not consider equivalent populations. However, considering both datasets together does provide some insights into the protective effect of vaccines. Specifically, each peak in COVID-19 cases in patients with each of the comorbidities prior to summer 2021 is clearly reflected in the number of ICU admissions in the adult population of Sweden. By contrast, this Omicron peak in cases is not reflected in a peak in ICU admissions. This is evidence that vaccines were protective against the onset of the more severe symptoms. A protective effect has also been confirmed in many studies. Ideally, studies considering a protective effect should directly compare COVID-19 cases and vaccine coverage in a manner that accounts for age-group and time point. -There is clear evidence of the benefits of vaccination for patients with each comorbidity considered. When switching between comorbidities, it is evident that they follow the same overall pattern in terms of the number of COVID-19 cases detected, but there are variations in magnitude. Similar to the above data considering the population over 18, it is clear that the proportion of vaccinated individuals diagnosed with COVID-19 increases with vaccine coverage. This is expected because the vaccine is not completely effective at preventing infection, although it does have a higher effectiveness against the development of serious disease, as shown in many studies (see also the [above data on ICU admission rates](#swedish-population-in-general) and results from [Xu *et al.* (2022)](https://doi.org/10.3390/vaccines10122074); a recent study based on RECOVAC data). Thus, as the proportion of the population that is vaccinated increases, more vaccinated individuals are likely to develop COVID-19. Some evidence of a protective effect can already be inferred from a superficial consideration of the number of cases detected in autumn 2020 compared to autumn 2021. The initial reduction in cases that occurs as vaccination coverage increases could, in part, be attributed to a general reduction in cases over the 2021 summer months. There was a peak in cases during winter 2021, and it is clear that many of the cases occurred in vaccinated individuals, but this is not evidence that vaccines do not have a protective effect. Rather, it reflects that a large part of the population was vaccinated by this time. The peak in winter 2021 can be attributed to the emergence of the new Omicron virus variant that was first detected in November 2021. This variant spreads more easily than earlier variants, even in vaccinated individuals. The number of COVID-19 cases presented in the graph below, and the number of ICU admissions in the graph in the section above, do not consider equivalent populations. However, considering both datasets together does provide some insights into the protective effect of vaccines. Specifically, each peak in COVID-19 cases in patients with each of the comorbidities prior to summer 2021 is clearly reflected in the number of ICU admissions in the adult population of Sweden. By contrast, this Omicron peak in cases is not reflected in a peak in ICU admissions. This is evidence that vaccines were protective against the onset of the more severe symptoms. A protective effect has also been confirmed in many studies. Ideally, studies considering a protective effect should directly compare COVID-19 cases and vaccine coverage in a manner that accounts for age-group and time point. +##### Visualisations + +The graphs below have multiple interactive features. It is possible to see all of the data available for a given comorbidity for clicking on the corresponding button. The 'Align timeline' button will change the timeframe shown so that only the time period that is common between the two graphs is shown. The 'Show all data' button can be used to see all of the available data for both datasets (the timelines of the two are not the same).
@@ -121,35 +127,39 @@ Click on the button corresponding to the cormorbidity of interest to display dat ##### Changing the timeframe displayed -The two graphs are shown on different time scales. This is because vaccinations were not made available until early 2021, whilst data on COVID-19 infection are available from March 2020. The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on COVID-19 cases. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'View whole time series' button to view all of the available data. +The two graphs are shown on different time scales. This is partly because vaccinations were not made available until early 2021, whilst data on COVID-19 infection are available from March 2020. Further, the latest data available can differ between data types. + +The default view will show all of the data available for both datasets. However, it is useful for align the timeframes, especially when making inferences about the effects of vaccination on COVID-19 cases. Use the 'Align timeframe' button in the 'Timeframe' buttons list to align the timeframes. Click the 'Show all data' button to view all of the available data. ##### Accurately read data values -It is possible to view the underlying data values by hovering the cursor over the graph. All values for a given date are shown together. +It is possible to view the underlying data values by hovering the cursor over the graph. All values for a given date are shown together. ##### Other features When hovering over the plot with the cursor, additional grey icons appear in the top right. The +, -, and magnifying glass icons can be used to zoom in/out of the plot. Alternatively, it is possible to zoom into a given part of the graph by clicking and dragging with the cursor to select that portion. The autoscale and reset axes icons (which look like a box containing arrows and a house, respectively) can be used to scale the axes appropriately for the data selected. The plot can be downloaded in .png format by clicking on the camera icon. +
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/comorbs_subplot_button.json" height="800px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/comorbs_subplot_button.json" height="800px" >}}
**Code used to produce plots:** [Preparation of COVID-19 case data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/comorbidity_cases_dataprep.py), [Preparation of vaccination coverage data](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/comorbidity_vaccinecov_dataprep.py), [Graph containing subplots](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/RECOVAC/comorbidity_subplots_wbuttons.py). ## Determinants of vaccination -The rapid development of COVID-19 vaccination and high vaccination coverage have been crucial in mitigating the societal effects of the COVID-19 pandemic. Two recently published articles by Spetz and colleagues ([Spetz *et al.* (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz *et al.* (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860)) at Gothenburg University used registry-based data to examine sociodemographic determinants of vaccination coverage. In brief, although the overall vaccination coverage in Sweden was over 82%, vaccination was lower among certain demographic groups. Specifically, vaccination coverage was lower among younger individuals, those on low incomes, those born outside of Sweden, and males. Individuals born in a low- or middle-income country were found to be six times less likely to be vaccinated than individuals born in Sweden.  When considering the whole working age population (18-64 years), vaccination coverage varied extensively between different subgroups, as defined by the factors studied, from 32% to as high as 96%. Similar results were seen in the older population (65 years of age and older). These results indicate that taking social factors into consideration is important in future health and vaccination efforts. Read more about these findings in [Spetz *et al.* (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz *et al.* (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860). + +The rapid development of COVID-19 vaccination and high vaccination coverage have been crucial in mitigating the societal effects of the COVID-19 pandemic. Two recently published articles by Spetz and colleagues ([Spetz _et al._ (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz _et al._ (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860)) at Gothenburg University used registry-based data to examine sociodemographic determinants of vaccination coverage. In brief, although the overall vaccination coverage in Sweden was over 82%, vaccination was lower among certain demographic groups. Specifically, vaccination coverage was lower among younger individuals, those on low incomes, those born outside of Sweden, and males. Individuals born in a low- or middle-income countries were found to be six times less likely to be vaccinated than individuals born in Sweden.  When considering the whole working age population (18-64 years), vaccination coverage varied extensively between different subgroups, as defined by the factors studied, from 32% to 96%. Similar results were seen in the older population (65 years of age and older). These results indicate that taking social factors into consideration is important in future health and vaccination efforts. Read more about these findings in [Spetz _et al._ (2022a)](https://www.sciencedirect.com/science/article/pii/S2666776222000242?via%3Dihub) and [Spetz _et al._ (2022b)](https://www.sciencedirect.com/science/article/pii/S0264410X22011860). ## Vaccine effectiveness -Using long-term data about the Swedish general population from RECOVAC, [Xu *et al.* (2022)](https://doi.org/10.3390/vaccines10122074) provided a more detailed insight into time-varying vaccine effectiveness (VE) against COVID-19 infection, hospitalisation, ICU admission, and death up to 13 months after vaccination. Two vaccine doses offered good, long-lasting protection against infection before the Omicron variant emerged (VE above 85% for all time intervals), but more limited protection against infection with the Omicron variant. For severe COVID-19 outcomes, however, high VE was observed during the entire follow-up period with better protection among individuals above age 65. For more details, see [Xu *et al.* (2022)](https://doi.org/10.3390/vaccines10122074). + +Using long-term data about the Swedish general population from RECOVAC, [Xu _et al._ (2022)](https://doi.org/10.3390/vaccines10122074) provided a more detailed insight into time-varying vaccine effectiveness (VE) against COVID-19 infection, hospitalisation, ICU admission, and death up to 13 months after vaccination. Two vaccine doses offered good, long-lasting protection against infection before the Omicron variant emerged (VE above 85% for all time intervals), but more limited protection against infection with the Omicron variant. For severe COVID-19 outcomes, however, high VE was observed during the entire follow-up period with better protection among individuals above age 65. For more details, see [Xu _et al._ (2022)](https://doi.org/10.3390/vaccines10122074). ## Contact information -Prof Fredrik Nyberg, Project Leader and Professor of Register Epidemiology, School of Public Health and Community Medicine, Institute of Medicine, Gothenburg University. Email: [fredrik.nyberg.2@gu.se](mailto:fredrik.nyberg.2@gu.se). -Patricia Ernst, Project Coordinator SCIFI-PEARL and RECOVAC. Email: [patricia.ernst@gu.se](mailto:patricia.ernst@gu.se) +Prof Fredrik Nyberg, Project Leader and Professor of Register Epidemiology, School of Public Health and Community Medicine, Institute of Medicine, Gothenburg University. Email: [fredrik.nyberg.2@gu.se](mailto:fredrik.nyberg.2@gu.se). You are also welcome to use the general email addresses for the projects: [recovac@gu.se](recovac@gu.se) or [scifipearl@medicin.gu.se](mailto:scifipearl@medicin.gu.se) @@ -167,29 +177,29 @@ The data in this study are pseudonymized individual-level data from Swedish heal We will consider proposals for collaboration in a positive vein, based on an assessment of their interest and importance, fit with the project and realistic possibility of implementing (especially given internal resource within the project). We actively seek and encourage cross-functional collaboration and invite new ideas and expertise. Collaborations will be structured as sub projects under **SCIFI-PEARL/RECOVAC** and the existing PI and leadership, in accordance with existing ethics approvals. Arrangements for data access, cost sharing and collaboration support from core project staff and resource will be discussed on a case by case basis. Data will not be shared outside of our protected data environments. Before you contact us, think carefully through the aims for the collaboration with **SCIFI-PEARL / RECOVAC** that you wish to initiate. Please attach a project idea / subproject synopsis or preliminary project plan. You can use your own format, or the template available here if you wish. We cannot finance research projects, but are open to collaborate in funding applications. -*For more information, please contact those involved with the project using the contact details [above](/dashboards/recovac/#contact-information).* +_For more information, please contact those involved with the project using the contact details [above](/dashboards/recovac/#contact-information)._ ## Publications and Preprints Below is a list of publications and preprints from this group. Please see the [SCIFI-PEARL webpage](https://www.gu.se/forskning/scifi-pearl) for a more comprehensive overview of outputs (e.g. lists of conference abstracts and recently submitted articles). The publication lists on that page are also updated more regularly (weekly) than the list below. -* Xu, Y., Li, H., Kirui, B., Santosa, A., Gisslen, M., Leach, S., Wettermark, B., Vanfleteren, L. E. G. W., Nyberg, F. (2022). Effectiveness of COVID-19 Vaccines Over 13 Months Covering the Period of the Emergence of the Omicron Variant in the Swedish Population. *Available at SSRN as a preprint:* [https://doi.org/10.3390/vaccines10122074](https://doi.org/10.3390/vaccines10122074) +- Xu, Y., Li, H., Kirui, B., Santosa, A., Gisslen, M., Leach, S., Wettermark, B., Vanfleteren, L. E. G. W., Nyberg, F. (2022). Effectiveness of COVID-19 Vaccines Over 13 Months Covering the Period of the Emergence of the Omicron Variant in the Swedish Population. _Available at SSRN as a preprint:_ [https://doi.org/10.3390/vaccines10122074](https://doi.org/10.3390/vaccines10122074) -* Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Ng, N., Leach, S., Gisslén, M., Hammar, N., Nyberg, F., Rosvall, M. (2022) An intersectional analysis of sociodemographic disparities in Covid-19 vaccination: a nationwide register-based study in Sweden. *Vaccine, 40*, 6640-6648. [https://doi.org/10.1016/j.vaccine.2022.09.065](https://doi.org/10.1016/j.vaccine.2022.09.065) +- Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Ng, N., Leach, S., Gisslén, M., Hammar, N., Nyberg, F., Rosvall, M. (2022) An intersectional analysis of sociodemographic disparities in Covid-19 vaccination: a nationwide register-based study in Sweden. _Vaccine, 40_, 6640-6648. [https://doi.org/10.1016/j.vaccine.2022.09.065](https://doi.org/10.1016/j.vaccine.2022.09.065) -* Mousa, S. I., Nyberg, F., Hajiebrahimi, M., Bertilsson, R., Nåtman, J., Santosa, A., Wettermark, B. (2022). Initiation of antihypertensive drugs to patients with confirmed COVID-19-A population-based cohort study in Sweden. *Basic & Clinical Pharmacology & Toxicology, 131*, 196-204. [https://doi.org/10.1111/bcpt.13766](https://doi.org/10.1111/bcpt.13766) +- Mousa, S. I., Nyberg, F., Hajiebrahimi, M., Bertilsson, R., Nåtman, J., Santosa, A., Wettermark, B. (2022). Initiation of antihypertensive drugs to patients with confirmed COVID-19-A population-based cohort study in Sweden. _Basic & Clinical Pharmacology & Toxicology, 131_, 196-204. [https://doi.org/10.1111/bcpt.13766](https://doi.org/10.1111/bcpt.13766) -* Sundh, J., Ekström, M., Palm, A., Ljunggren, M., Emilsson, Ö. I., Grote, L., Cajander, S., Li, H., Nyberg, F. (2022). COVID-19 and Risk of Oxygen-dependent Chronic Respiratory Failure: A National Cohort Study. *American Journal of Respiratory and Critical Care Medicine, 206* 506-509. [https://doi.org/10.1164/rccm.202202-0323le](https://doi.org/10.1164/rccm.202202-0323le) +- Sundh, J., Ekström, M., Palm, A., Ljunggren, M., Emilsson, Ö. I., Grote, L., Cajander, S., Li, H., Nyberg, F. (2022). COVID-19 and Risk of Oxygen-dependent Chronic Respiratory Failure: A National Cohort Study. _American Journal of Respiratory and Critical Care Medicine, 206_ 506-509. [https://doi.org/10.1164/rccm.202202-0323le](https://doi.org/10.1164/rccm.202202-0323le) -* Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Leach, S., Gisslén, M., Hammar, N., Rosvall, M., Nyberg, F. (2022). The social patterning of Covid-19 vaccine uptake in older adults: A register-based cross-sectional study in Sweden. *The Lancet Regional Health - Europe, 15,* 100331. [https://doi.org/10.1016/j.lanepe.2022.100331](https://doi.org/10.1016/j.lanepe.2022.100331) +- Spetz, M., Lundberg, L., Nwaru, C., Li, H., Santosa, A., Leach, S., Gisslén, M., Hammar, N., Rosvall, M., Nyberg, F. (2022). The social patterning of Covid-19 vaccine uptake in older adults: A register-based cross-sectional study in Sweden. _The Lancet Regional Health - Europe, 15,_ 100331. [https://doi.org/10.1016/j.lanepe.2022.100331](https://doi.org/10.1016/j.lanepe.2022.100331) -* Santosa, A., Franzén, S., Nåtman, J., Wettermark, B., Parmryd, I., Nyberg, F. (2022). Protective effects of statins on COVID-19 risk, severity and fatal outcome – a nationwide Swedish cohort study. *Research Square* [https://doi.org/10.21203/rs.3.rs-1432508/v1](https://doi.org/10.21203/rs.3.rs-1432508/v1) +- Santosa, A., Franzén, S., Nåtman, J., Wettermark, B., Parmryd, I., Nyberg, F. (2022). Protective effects of statins on COVID-19 risk, severity and fatal outcome – a nationwide Swedish cohort study. _Research Square_ [https://doi.org/10.21203/rs.3.rs-1432508/v1](https://doi.org/10.21203/rs.3.rs-1432508/v1) -* Nwaru, C.A., Santosa, A., Franzén, S., Nyberg, F. (2022). Occupation and COVID-19 diagnosis, hospitalisation and ICU admission among foreign-born and Swedish-born employees: a register-based study. *Journal of Epidemiology and Community Health, 7,* jech-2021-218278. [https://doi.org/10.1136/jech-2021-218278](https://doi.org/10.1136/jech-2021-218278) +- Nwaru, C.A., Santosa, A., Franzén, S., Nyberg, F. (2022). Occupation and COVID-19 diagnosis, hospitalisation and ICU admission among foreign-born and Swedish-born employees: a register-based study. _Journal of Epidemiology and Community Health, 7,_ jech-2021-218278. [https://doi.org/10.1136/jech-2021-218278](https://doi.org/10.1136/jech-2021-218278) -* Nyberg, F., Franzén, S., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Björck, S., Gisslén, M. (2021). Swedish Covid-19 Investigation for Future Insights – A Population Epidemiology Approach Using Register Linkage (SCIFI-PEARL). *Clinical Epidemiology, 13,* 649-659. [https://doi.org/10.2147/CLEP.S312742](https://doi.org/10.2147/CLEP.S312742) +- Nyberg, F., Franzén, S., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Björck, S., Gisslén, M. (2021). Swedish Covid-19 Investigation for Future Insights – A Population Epidemiology Approach Using Register Linkage (SCIFI-PEARL). _Clinical Epidemiology, 13,_ 649-659. [https://doi.org/10.2147/CLEP.S312742](https://doi.org/10.2147/CLEP.S312742) -* Nyberg, F., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Kirui, B. K., Gisslén, M. (2021). Adverse events of special interest for COVID-19 vaccines - background incidences vary by sex, age and time period and are affected by the pandemic. *medRxiv* [https://doi.org/10.1101/2021.10.04.21263507](https://doi.org/10.1101/2021.10.04.21263507) +- Nyberg, F., Lindh, M., Vanfleteren, L., Hammar, N., Wettermark, B., Sundström, J., Santosa, A., Kirui, B. K., Gisslén, M. (2021). Adverse events of special interest for COVID-19 vaccines - background incidences vary by sex, age and time period and are affected by the pandemic. _medRxiv_ [https://doi.org/10.1101/2021.10.04.21263507](https://doi.org/10.1101/2021.10.04.21263507) ## Information about SCIFI-PEARL diff --git a/content/svenska/dashboards/covid_publications.md b/content/svenska/dashboards/covid_publications.md index 34bb41cc5..3293deaea 100644 --- a/content/svenska/dashboards/covid_publications.md +++ b/content/svenska/dashboards/covid_publications.md @@ -46,13 +46,13 @@ These wordclouds display the words and two word phrases that appear most frequen #### All publications -
+
#### Publications attributed to particular research funders Wordclouds are displayed for each funder that we identified as having been associated with at least 20 publications in the database. -
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
+
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
## Most frequent words or two word phrases in the abstracts @@ -62,10 +62,10 @@ These wordclouds display the words and two word phrases that appear most frequen #### All publications -
+
#### Publications attributed to particular research funders Wordclouds are displayed for each funder that we identified as having been associated with at least 20 publications in the database. -
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
+
Swedish Research Council:
SciLifeLab/KAW:
Horizon 2020:
diff --git a/content/svenska/dashboards/crush_covid.md b/content/svenska/dashboards/crush_covid.md index 478c9592f..c131cbb42 100644 --- a/content/svenska/dashboards/crush_covid.md +++ b/content/svenska/dashboards/crush_covid.md @@ -4,26 +4,37 @@ description: CRUSH Covid kartlägger smittspridningen av Covid-19 i Uppsala län banner: /dashboard_thumbs/CRUSH.png toc: false aliases: - - /sv/data_types/health_data/crush_covid/ + - /sv/data_types/health_data/crush_covid/ menu: - dashboard_menu: - identifier: crush_covid - name: CRUSH Covid Uppsala (Partnerprojekt) + dashboard_menu: + identifier: crush_covid + name: CRUSH Covid Uppsala (Partnerprojekt) --- -

CRUSH Covid är ett samarbete mellan Region Uppsala och forskare från fem olika institutioner vid Uppsala universitet. Syftet med projektet är att kartlägga utbrott av covid-19 i Uppsala län och att bidra till att dämpa påverkan genom information till allmänheten. Informationen som visas på CRUSH Covid´s dashboard uppdaterades ursprungligen veckovis. Men sedan september 2022 sker uppdateringarna varje eller varannan månad.

-

CRUSH Covid leds av Mats Martinell (universitetslektor vid Institutionen för folkhälso-och vårdvetenskap, Allmänmedicin och Preventivmedicin, Uppsala universitet) och Tove Fall (professor vid Institutionen för medicinska vetenskaper, Molekylär epidemiologi, Uppsala universitet). Provtagningen på avloppsvatten stöds av SciLifeLab och Uppsala Vatten. Data modelleringen har anslag från Vinnova.

-

För frågor och feedback, kontakta Elin Clauson (elin.clauson@medsci.uu.se).

CRUSH Covid har etiskt tillstånd från Etikprövningsmyndigheten (DNR 2020-04210, 2020-06315 och 2020-06501).

-
CRUSH Covid
Uppsala University
-
Region Uppsala
+
+
+
+

CRUSH Covid Uppsala är ett forskningsprojekt där Region Uppsala samarbetar med forskare från fem olika forskningsavdelningar vid Uppsala universitet. Syftet med projektet är att kartlägga utbrott av covid-19 i Uppsala län och att försöka mildra effekterna av utbrott genom att informera allmänheten.

+ +

CRUSH Covid leds av Mats Martinell (universitetslektor vid Institutionen för folkhälso-och vårdvetenskap, Allmänmedicin och Preventivmedicin, Uppsala universitet) och Tove Fall (professor vid Institutionen för medicinska vetenskaper, Molekylär epidemiologi, Uppsala universitet). Provtagningen på avloppsvatten stöds av SciLifeLab och Uppsala Vatten. Data modelleringen har anslag från Vinnova.

+ +

För frågor och feedback, kontakta Elin Clauson (elin.clauson@medsci.uu.se).

CRUSH Covid har etiskt tillstånd från Etikprövningsmyndigheten (DNR 2020-04210, 2020-06315 och 2020-06501).

+ +

CRUSH Covid-teamet har släppt data och information om projektet på två ställen. Den primära källan till data och information var deras anpassade shiny app, som heter CRUSH Covid-instrumentpanelen, som innehöll datavisualiseringar samt rapporter. Från och med september 2022 upphörde uppdateringar av appen. Portalens instrumentpanel (dvs den här webbsidan) är den sekundära källan till data och information för detta projekt. Data som genereras från CRUSH Covid mellan 2020-2022 kan laddas ner direkt nedan.

+
+
+
CRUSH Covid
+
Uppsala University
+
Region Uppsala
+
+
+
#### Ladda ner CRUSH Covid data -
Senast uppdaterad: {{% crush_covid_modified %}}.
+
Senast uppdaterad: 2022-09-15
-* [Antal test per capita och % positiva fall i varje postnummer i Uppsala län, .csv fil](https://blobserver.dckube.scilifelab.se/blob/CRUSH_Covid_data.csv) (Uppdateras varje eller varannan månad sedan september 2022). - För varje postnummer i Uppsala län, innehåller vår dataset datauppgifter om Covid-19 fall per capita, test per capita och % positiva fall. Våra uppskattningar beräknas utifrån den vuxna befolkningen i varje postnummer (personer 15 år och äldre). Som referens har både den totala befolkningen och den vuxna befolkningen inkluderats. -* Mer data kommer att publiceras inom kort. +- [Antal test per capita och % positiva fall i varje postnummer i Uppsala län, .csv fil](https://blobserver.dc.scilifelab.se/blob/CRUSH_Covid_data.csv). För varje postnummer i Uppsala län, innehåller vår dataset datauppgifter om Covid-19 fall per capita, test per capita och % positiva fall. Våra uppskattningar beräknas utifrån den vuxna befolkningen i varje postnummer (personer 15 år och äldre). Som referens har både den totala befolkningen och den vuxna befolkningen inkluderats. #### Dashboard med interaktiv grafik och veckorapport diff --git a/content/svenska/dashboards/post_covid.md b/content/svenska/dashboards/post_covid.md index b0a40c71f..1893772e2 100644 --- a/content/svenska/dashboards/post_covid.md +++ b/content/svenska/dashboards/post_covid.md @@ -53,7 +53,7 @@ Detta diagram visar antalet gånger patienter som diagnostiserats med diagnoser #### Diagnoskod U09.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U099_agesex_casedist.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U099_agesex_casedist.json" height="500px" >}}
**Källkod som används för att skapa grafen:** [Källkod som används för att skapa visualisering](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/create_agesex_distcases.py). @@ -61,7 +61,7 @@ Detta diagram visar antalet gånger patienter som diagnostiserats med diagnoser #### Diagnoskod Z86.1A/U08.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U089_agesex_casedist.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U089_agesex_casedist.json" height="500px" >}}
**Källkod som används för att skapa grafen:** [Källkod som används för att skapa visualisering](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/create_agesex_distcases.py). @@ -73,7 +73,7 @@ Geografisk fördelning av diagnostiserade fall i förhållande till befolkningss #### Diagnoskod U09.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_population_U099_Swedish.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_population_U099_Swedish.json" height="500px" >}}
**Källkod som används för att skapa visualisering:** [Källkod som används för databeredning](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Källkod som används för att skapa kartan](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_population_U099.py). @@ -81,7 +81,7 @@ Geografisk fördelning av diagnostiserade fall i förhållande till befolkningss #### Diagnoskod Z86.1A/U08.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_population_U089_Swedish.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_population_U089_Swedish.json" height="500px" >}}
**Källkod som används för att skapa visualisering:** [Källkod som används för databeredning](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Källkod som används för att skapa kartan](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_population_U089.py). @@ -97,7 +97,7 @@ Kartorna nedan visar antal individer som fått diagnoskoderna av intresse per l #### Diagnoskod U09.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U099_Swedish.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U099_Swedish.json" height="500px" >}}
**Källkod som används för att skapa visualisering:** [Källkod som används för databeredning](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Källkod som används för att skapa kartan](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_cases_U099.py). @@ -105,7 +105,7 @@ Kartorna nedan visar antal individer som fått diagnoskoderna av intresse per l #### Diagnoskod Z86.1A/U08.9
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U089_Swedish.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/map_postcovid_percent_of_covidcases_U089_Swedish.json" height="500px" >}}
**Källkod som används för att skapa visualisering:** [Källkod som används för databeredning](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_dataprep.py), [Källkod som används för att skapa kartan](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/postcovid_mapfig_cases_U089.py). @@ -119,7 +119,7 @@ Kartorna nedan visar antal individer som fått diagnoskoderna av intresse per l Denna tabell visar de vanligaste diagnosgrupper som har rapporterats tillsammans med diagnoskoden *U09.9 (ICD-10-SE)-Post-infektiöst tillstånd efter covid-19 (Postcovid)*. Siffrorna och procentsatserna nedan visar hur många individer som fått diagnosen U09.9 och samtidigt har diagnoser från nedanstående diagnosgrupper. Data nedan återspeglar perioden från och med den 16 oktober 2020 och fram till den senaste uppdateringen (se ovan).
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/accompdiag_table_swe.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/accompdiag_table_swe.json" height="500px" >}}
*Observera att en individ kan ha mer än en diagnosgrupp som rapporteras tillsammans med U09.9 Postinfektiöst tillstånd efter covid-19 (Postcovid). Om en individ har samma besvär vid flera vårdtillfällen/läkarbesök räknas diagnosen bara en gång.* @@ -135,7 +135,7 @@ Denna graf visar antal vårdkontakter för patienter med de av de tre diagnoskod
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/weeklycontacts_healthcare.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/weeklycontacts_healthcare.json" height="500px" >}}
**Källkod som används för att skapa grafen:** [Källkod som används för att skapa grafen](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/weeklycontacts_healthcare.py). @@ -151,7 +151,7 @@ Dessa grafer visar antal vårdkontakter för patienter som diagnostiserats med e
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U099_healthcare_divsex.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U099_healthcare_divsex.json" height="500px" >}}
**Källkod som används för att skapa grafen:** [Källkod som används för att skapa grafen](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/weeklycontacts_healthcare_divsex.py). @@ -163,7 +163,7 @@ Dessa grafer visar antal vårdkontakter för patienter som diagnostiserats med e
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/U089_healthcare_divsex.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/U089_healthcare_divsex.json" height="500px" >}}
**Källkod som används för att skapa grafen:** [Källkod som används för att skapa grafen](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/postCOVID/weeklycontacts_healthcare_divsex.py). diff --git a/content/svenska/dashboards/symptom_study_sweden.md b/content/svenska/dashboards/symptom_study_sweden.md index 5229b8305..74a6dc34f 100644 --- a/content/svenska/dashboards/symptom_study_sweden.md +++ b/content/svenska/dashboards/symptom_study_sweden.md @@ -25,7 +25,7 @@ COVID Symptom Study Sverige har två huvudsyften. Det första syftet är att und Nedan redovisas uppskattad förekomst av symtomatiska covid-19 fall i olika svenska regioner. Uppskattningen baseras på användardata från appen och en beräkningsmetod. som används för prediktion har utvecklats av forskargruppen bakom COVID Symptoms Study Sverige; se [denna sida](https://www.covid19app.lu.se/artikel/uppdatering-av-prediktionsmodell-0) för mer information om prediktionsmodellen. För mer detaljerad information om hur uppskattad förekomst och andra resultat vänligen se [COVID Symptoms Study Sveriges dashboard](https://csss-resultat.shinyapps.io/csss_dashboard/).
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/symptoms_map_swedish.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/symptoms_map_swedish.json" height="500px" >}}
#### Tillgång till insamlade data för användning inom andra forskningsprojekt diff --git a/content/svenska/dashboards/vaccines.md b/content/svenska/dashboards/vaccines.md index f753f407d..b1ade3c1d 100644 --- a/content/svenska/dashboards/vaccines.md +++ b/content/svenska/dashboards/vaccines.md @@ -68,7 +68,7 @@ Vaccination data is spread between multiple tabs of the [FoHM data file](https:/ **Note on the graph:** Click on the coloured squares in the legend of the below graph to toggle which datasets are displayed. A single click will toggle just that dataset on/off. It is possible to display only one of the datasets by double-clicking on the desired dataset.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/Total_vaccinated_barchart.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/Total_vaccinated_barchart.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_indicator_barchart.py). @@ -88,7 +88,7 @@ The below graph shows vaccine coverage across the whole of Sweden. We use the 'w **Note on the graph:** Click on the coloured squares in the legend of the below graph to toggle which datasets are displayed. A single click will toggle just that dataset on/off. It is possible to display only one of the datasets by double-clicking on the desired dataset.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/vaccine_timeseries_pop_barchart.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/vaccine_timeseries_pop_barchart.json" height="500px" >}}
**Code used to produce plot:** [Script to produce plot](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_timeseries_barchart.py). @@ -104,7 +104,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least one vaccine dose
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/onedose_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/onedose_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -112,7 +112,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least two vaccine doses
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/twodoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/twodoses_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -120,7 +120,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least three vaccine doses
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/threedoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/threedoses_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -128,7 +128,7 @@ Again, please note that percentage values calculated using the 'whole population #### Received at least four vaccine doses
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/fourdoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/fourdoses_pop_map.json" height="500px" >}}
**Code used to produce plot:** [Script to produce maps](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_maps_population.py). @@ -139,10 +139,10 @@ Please note the differences between the two below maps. Coverage appears to be v
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/fivedoses_pop_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/fivedoses_pop_map.json" height="500px" >}}
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/fivedoses_elig_map.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/fivedoses_elig_map.json" height="500px" >}}
@@ -161,7 +161,7 @@ Data is available on the number of individuals aged 65-69 that have received the **Note about the heatmap:** A white colouration indicates that no data is available for that age group.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/vaccine_heatmap.json" height="500px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/vaccine_heatmap.json" height="500px" >}}
**Code used to produce plot:** [Script to produce heatmap](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/Vaccine_page/vaccine_heatmaps.py). diff --git a/content/svenska/dashboards/wastewater/introduction.md b/content/svenska/dashboards/wastewater/_index.md similarity index 98% rename from content/svenska/dashboards/wastewater/introduction.md rename to content/svenska/dashboards/wastewater/_index.md index f0c88d39c..86e60ff30 100644 --- a/content/svenska/dashboards/wastewater/introduction.md +++ b/content/svenska/dashboards/wastewater/_index.md @@ -2,7 +2,7 @@ title: Avloppsvattensepidemiologiska analyser i Sverige description: Monitorering av olika patogener i avloppsvatten kan vara ett effektivt sätt att förutse framtida virusutbrott. Denna dashboard innehåller data som ursprungligen samlats in av ett flertal olika forskargrupper runt om i Sverige. banner: /dashboard_thumbs/wastewater.jpg -toc: false +inline_toc: true type: wastewater menu: dashboard_menu: @@ -19,7 +19,7 @@ plotly: true aliases: - /sv/data_types/environment/wastewater/ - /sv/data_types/environment/ - - /sv/dashboards/wastewater + - /sv/dashboards/wastewater/introduction/ ---
Notera: Dashboarden för avloppsvatten kommer att utökas framöver. Vi har nu grupperat mätningarna för mängden av SARS-CoV-2 i avloppsvatten enligt den forskargrupp som samlade in och analyserade respektive data (se nedan för information om de deltagande grupperna). Under kommande månader kommer mer data och information om SARS-CoV-2 och andra infektionssjukdomar att läggas till.
@@ -47,7 +47,7 @@ All källkod som skapats för visualiseringarna under de olika flikarna på den Nedan finns en karta som visar de avloppsreningsverk (på engelska wastewater treatment plants, WWTP) från vilka avloppsprover samlas in och analyseras av de grupper som delar data på den här dashboarden.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_map_test.json" height="600px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_map_test.json" height="600px" >}}
**Källkod som använts för att skapa kartan:** [Script to produce map](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/interactive_wastewater_map.py). diff --git a/content/svenska/dashboards/wastewater/covid_quantification.md b/content/svenska/dashboards/wastewater/covid_quantification/_index.md similarity index 53% rename from content/svenska/dashboards/wastewater/covid_quantification.md rename to content/svenska/dashboards/wastewater/covid_quantification/_index.md index 1bc15a5a3..15e827ca9 100644 --- a/content/svenska/dashboards/wastewater/covid_quantification.md +++ b/content/svenska/dashboards/wastewater/covid_quantification/_index.md @@ -1,6 +1,5 @@ --- -title: Kvantifiering av mängd SARS-COV-2 i avloppsvatten runt om i Sverige -toc: false +title: SARS-CoV-2 kvantifiering type: wastewater menu: wastewater: @@ -14,8 +13,8 @@ plotly: true Alla tre forskargrupperna som delar data på denna dashboard gör egna mätningar av mängd SARS-CoV-2 i avloppsvatten. **Forskargrupperna mäter mängd SARS-CoV-2 i avloppsvatten i olika svenska regioner, i vissa regioner sker mätningar av flera forskargrupper**. Se nedan för en lista över områden och städer som varje forskargrupp mäter. Klicka på forskargruppens namn för att komma direkt till respektive grupps webbsida med information om deras respektive mätningar av SARS-CoV-2 i avloppsvatten. -- [**Göteborgs universitet (GU):**](/sv/dashboards/wastewater/covid_quant_gu/) Kvantifiering av mängd SARS-CoV-2 i avloppsvatten från Göteborg från forskargruppen Helene Norder vid GU. +- [**Göteborgs universitet (GU):**](/sv/dashboards/wastewater/covid_quantification/covid_quant_gu/) Kvantifiering av mängd SARS-CoV-2 i avloppsvatten från Göteborg från forskargruppen Helene Norder vid GU. -- [**SEEC-KTH noden:**](/sv/dashboards/wastewater/covid_quant_kth/)Kvantifiering av mängd SARS-CoV-2 i avloppsvatten från Malmö och Stockholm Göteborg från forskargruppen SEEC-KTH. +- [**SEEC-KTH noden:**](/sv/dashboards/wastewater/covid_quantification/covid_quant_kth/)Kvantifiering av mängd SARS-CoV-2 i avloppsvatten från Malmö och Stockholm Göteborg från forskargruppen SEEC-KTH. -- [**SEEC-SLU noden:**](/sv/dashboards/wastewater/covid_quant_slu/) Kvantifiering av mängd SARS-CoV-2 i avloppsvatten från ett antal städer inkl. Stockholm, Malmö, Gothenburg, Uppsala, Västerås, Örebro, Umeå, av SEEC-SLU noden. +- [**SEEC-SLU noden:**](/sv/dashboards/wastewater/covid_quantification/covid_quant_slu/) Kvantifiering av mängd SARS-CoV-2 i avloppsvatten från ett antal städer inkl. Stockholm, Malmö, Gothenburg, Uppsala, Västerås, Örebro, Umeå, av SEEC-SLU noden. diff --git a/content/svenska/dashboards/wastewater/covid_quant_GU.md b/content/svenska/dashboards/wastewater/covid_quantification/covid_quant_GU.md similarity index 95% rename from content/svenska/dashboards/wastewater/covid_quant_GU.md rename to content/svenska/dashboards/wastewater/covid_quantification/covid_quant_GU.md index de5561b22..4e21a5951 100644 --- a/content/svenska/dashboards/wastewater/covid_quant_GU.md +++ b/content/svenska/dashboards/wastewater/covid_quantification/covid_quant_GU.md @@ -1,7 +1,8 @@ --- title: Mängd SARS-COV-2 i avloppsvatten (GU) -toc: false plotly: true +aliases: + - /sv/dashboards/wastewater/covid_quant_gu/ ---
@@ -53,7 +54,7 @@ Ingående avloppsvattenprover insamlas från Ryaverkets avloppsreningsverk (eng.
-->
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_gothenburg.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_gothenburg.json" height="550px" >}}
**Källskod som används för att skapa grafen:** [Källskod](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/gothenburg_covid.py). @@ -68,7 +69,7 @@ Ingående avloppsvattenprover insamlas från Ryaverkets avloppsreningsverk (eng. **Kontakt:** -**Nedladdning av data:** [Quantification of SARS-CoV-2 and enteric viruses in wastewater](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_gu_allviruses.xlsx). Resultat finns tillgängliga för mängd SARS-CoV-2 från vecka 7 2020 (ett mindre uppehåll under vintern 2022-2023) , och för enterovirus från vecka 2 2023. Data uppdateras veckovis.\ +**Nedladdning av data:** [Quantification of SARS-CoV-2 and enteric viruses in wastewater](https://blobserver.dc.scilifelab.se/blob/wastewater_data_gu_allviruses.xlsx). Resultat finns tillgängliga för mängd SARS-CoV-2 från vecka 7 2020 (ett mindre uppehåll under vintern 2022-2023) , och för enterovirus från vecka 2 2023. Data uppdateras veckovis.\ **För att citera datasetet:** Norder, H., Nyström, K. Patzi Churqui, M., Tunovic, T., Wang, H. (2023). Detection of SARS-CoV-2 and other human enteric viruses in wastewater from Gothenburg. [https://doi.org/10.17044/scilifelab.22510501](https://doi.org/10.17044/scilifelab.22510501). diff --git a/content/svenska/dashboards/wastewater/covid_quant_KTH.md b/content/svenska/dashboards/wastewater/covid_quantification/covid_quant_KTH.md similarity index 95% rename from content/svenska/dashboards/wastewater/covid_quant_KTH.md rename to content/svenska/dashboards/wastewater/covid_quantification/covid_quant_KTH.md index 3e877c0c6..4b86737d7 100644 --- a/content/svenska/dashboards/wastewater/covid_quant_KTH.md +++ b/content/svenska/dashboards/wastewater/covid_quantification/covid_quant_KTH.md @@ -1,6 +1,8 @@ --- title: Mängd SARS-COV-2 i avloppsvatten (SEEC-KTH) plotly: true +aliases: + - /sv/dashboards/wastewater/covid_quant_kth/ ---
@@ -61,7 +63,7 @@ Notera också att även om samma metoder används för alla städer som visas p
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_combined_stockholm.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_combined_stockholm.json" height="550px" >}}
**Källskod som används för att skapa grafen:** [Källskod](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/combined_stockholm_regular.py). @@ -75,7 +77,7 @@ Notera också att även om samma metoder används för alla städer som visas p
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_kthmalmo.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_kthmalmo.json" height="550px" >}}
**Källskod som används för att skapa grafen:** [Källskod](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/quant_malmo_kthplot.py). @@ -90,7 +92,7 @@ Notera också att även om samma metoder används för alla städer som visas p **Kontakt:** -**Ladda ner data:** [N3-genkopiatal per PMMoV-genkopiatal; Excelfil.](https://blobserver.dckube.scilifelab.se/blob/stockholm_wastewater_method_Sep_2021.xlsx). Data tillgänglig (delvis) från och med vecka 16 2020; uppdateras varje vecka. +**Ladda ner data:** [N3-genkopiatal per PMMoV-genkopiatal; Excelfil.](https://blobserver.dc.scilifelab.se/blob/stockholm_wastewater_method_Sep_2021.xlsx). Data tillgänglig (delvis) från och med vecka 16 2020; uppdateras varje vecka. **Hur man citerar dataset:** Cetecioglu, Z. G., Williams, C., Khatami, K., Atasoy, M., Nandy, P., Jafferali, M. H., Birgersson, M. (2021). SARS-CoV-2 Wastewater Data from Stockholm, Sweden. [https://doi.org/10.17044/scilifelab.14315483](https://doi.org/10.17044/scilifelab.14315483). @@ -103,7 +105,7 @@ Efter koncentration, filtrering och beredning analyseras proverna med RT-qPCR -t ## Arkiverade data -- [Historiska data for Stockholm; genkopieantal per vecka (ofiltrerat avloppsvatten) standardiserat med bovint coronavirus och PMMoV, april 2020 till augusti 2021](/sv/dashboards/wastewater/historic_stockholm). +- [Historiska data for Stockholm; genkopieantal per vecka (ofiltrerat avloppsvatten) standardiserat med bovint coronavirus och PMMoV, april 2020 till augusti 2021](/sv/dashboards/wastewater/covid_quantification/historic_stockholm).
diff --git a/content/svenska/dashboards/wastewater/covid_quant_SLU.md b/content/svenska/dashboards/wastewater/covid_quantification/covid_quant_SLU.md similarity index 95% rename from content/svenska/dashboards/wastewater/covid_quant_SLU.md rename to content/svenska/dashboards/wastewater/covid_quantification/covid_quant_SLU.md index 3cf6f0f56..c73f925f4 100644 --- a/content/svenska/dashboards/wastewater/covid_quant_SLU.md +++ b/content/svenska/dashboards/wastewater/covid_quantification/covid_quant_SLU.md @@ -1,6 +1,8 @@ --- title: Mängd SARS-COV-2 i avloppsvatten (SEEC-SLU) plotly: true +aliases: + - /sv/dashboards/wastewater/covid_quant_slu/ ---
@@ -19,7 +21,7 @@ Data och visualiseringar på den här sidan uppdateras vanligtvis veckovis, ofta SLU-SEEC samlar in och analyserar prover från ett flertal orter/städer. Nedan visas en tabell med detaljerad information om alla insamlingsplatser. Tabellen listar orter/städer som monitoreras, avloppsreningsverk (WWTP) där proverna samlas in, antal personer i upptagningsområdet (antal invånare), mellan vilka datum SLU-SEEC mätningarna skett (startdatum och slutdatum). Ett värde ’null’ istället för slutdatum innebär att insamlingen fortfarande pågår. En asterisk bredvid antal invånare innebär att antal invånare är preliminärt.
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_slusites.json" height="775px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_slusites.json" height="775px" >}}
@@ -47,7 +49,7 @@ SLU-SEEC samlar in och analyserar prover från ett flertal orter/städer. Nedan
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_slusites.json" height="600px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_slusites.json" height="600px" >}}
@@ -93,7 +95,7 @@ Notera också att även om samma metoder används för alla städer som visas p
-
{{< plotly json="https://blobserver.dckube.scilifelab.se/blob/wastewater_combined_slu_regular.json" height="550px" >}}
+
{{< plotly json="https://blobserver.dc.scilifelab.se/blob/wastewater_combined_slu_regular.json" height="550px" >}}
**Källskod som används för att skapa grafen:** [Källskod](https://github.com/ScilifelabDataCentre/covid-portal-visualisations/blob/main/wastewater/combined_slu_regular.py). @@ -106,7 +108,7 @@ Notera också att även om samma metoder används för alla städer som visas p ## Rapporter från forskargruppen -Forskargruppen delar även en rapport som sammanfattar den senaste informationen från deras avloppsvattenmätningar. Den senaste rapporten finns tillgänglig som pdf [här](https://blobserver.dckube.scilifelab.se/blob/Latest_weekly_report_SEEC-SLU) (endast tillgänglig på svenska). +Forskargruppen delar även en rapport som sammanfattar den senaste informationen från deras avloppsvattenmätningar. Den senaste rapporten finns tillgänglig som pdf [här](https://blobserver.dc.scilifelab.se/blob/Latest_weekly_report_SEEC-SLU) (endast tillgänglig på svenska). ## Dataset @@ -132,7 +134,7 @@ Data som presenteras i grafen är ett ratio av kopieantal som uppmätts med N1- ## Arkiverade data -- [Historiska data för Örebro och Umeå, mängd SARS-CoV-2 i avloppsvatten från Umeå respektive Örebro mellan oktober 2020 och juni 2021.](/sv/dashboards/wastewater/historic_orebro_umea). +- [Historiska data för Örebro och Umeå, mängd SARS-CoV-2 i avloppsvatten från Umeå respektive Örebro mellan oktober 2020 och juni 2021.](/sv/dashboards/wastewater/covid_quantification/historic_orebro_umea).
diff --git a/content/svenska/dashboards/wastewater/historic_orebro_umea.md b/content/svenska/dashboards/wastewater/covid_quantification/historic_orebro_umea.md similarity index 91% rename from content/svenska/dashboards/wastewater/historic_orebro_umea.md rename to content/svenska/dashboards/wastewater/covid_quantification/historic_orebro_umea.md index 5526eb31d..9d25bc9b9 100644 --- a/content/svenska/dashboards/wastewater/historic_orebro_umea.md +++ b/content/svenska/dashboards/wastewater/covid_quantification/historic_orebro_umea.md @@ -1,8 +1,8 @@ --- title: "Historiska data för Örebro och Umeå" -toc: false aliases: - /sv/data_types/environment/wastewater/historic_orebro_umea/ + - /sv/dashboards/wastewater/historic_orebro_umea/ --- Denna sida visar data för mängd SARS-CoV-2 i avloppsvatten från Umeå och Örebro mellan oktober 2020 och juni 2021. Från juni 2021 ändrades metoden. Se [den här sidan](../) för de senaste uppgifterna. @@ -13,7 +13,7 @@ Efter beredning har proverna extraherats och ultrafiltrats samt analyserats för ### Mängd SARS-CoV-2 i avloppsvatten från Umeå mellan oktober 2020 och juni 2021 -**Ladda ner data:** [Förändring i mängd SARS CoV-2 RNA (%) jämfört med Nov 6 2020 och flöde varje dag samt veckonummer, Excel fil.](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_Umeaa.xlsx). Data startar vecka 44 of 2020; uppdateras en gång per månad. +**Ladda ner data:** [Förändring i mängd SARS CoV-2 RNA (%) jämfört med Nov 6 2020 och flöde varje dag samt veckonummer, Excel fil.](https://blobserver.dc.scilifelab.se/blob/wastewater_data_Umeaa.xlsx). Data startar vecka 44 of 2020; uppdateras en gång per månad. **Referera till detta dataset:** Malmberg, M., Myrmel, M. & Khatri, M. Dataset of SARS-CoV-2 in wastewater in Umeå, Sweden. [https://doi.org/10.17044/scilifelab.14376881.v1](https://doi.org/10.17044/scilifelab.14376881.v1) (2021). @@ -32,7 +32,7 @@ Malmberg, M., Myrmel, M. & Khatri, M. Dataset of SARS-CoV-2 in wastewater in Ume ### Mängd SARS-CoV-2 i avloppsvatten från Örebro mellan oktober 2020 och juni 2021 -**Ladda ner data:** [Förändring i mängd SARS CoV-2 RNA (%) jämfört med Nov 6 2020 och flöde varje dag samt veckonummer, Excel fil](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_Orebro.xlsx). Data startar vecka 44 of 2020; uppdateras en gång per månad. +**Ladda ner data:** [Förändring i mängd SARS CoV-2 RNA (%) jämfört med Nov 6 2020 och flöde varje dag samt veckonummer, Excel fil](https://blobserver.dc.scilifelab.se/blob/wastewater_data_Orebro.xlsx). Data startar vecka 44 of 2020; uppdateras en gång per månad. **Referera till detta dataset:** Malmberg, M., Myrmel, M. & Khatri, M. Dataset of SARS CoV-2 in wastewater in Örebro, Sweden. [https://doi.org/10.17044/scilifelab.14377097.v1](https://doi.org/10.17044/scilifelab.14377097.v1) (2021). diff --git a/content/svenska/dashboards/wastewater/historic_stockholm.md b/content/svenska/dashboards/wastewater/covid_quantification/historic_stockholm.md similarity index 93% rename from content/svenska/dashboards/wastewater/historic_stockholm.md rename to content/svenska/dashboards/wastewater/covid_quantification/historic_stockholm.md index e164a55a3..ef7725105 100644 --- a/content/svenska/dashboards/wastewater/historic_stockholm.md +++ b/content/svenska/dashboards/wastewater/covid_quantification/historic_stockholm.md @@ -1,8 +1,8 @@ --- title: "Historiska data för Stockholm" -toc: false aliases: - /sv/data_types/environment/wastewater/historic_stockholm/ + - /sv/dashboards/wastewater/historic_stockholm/ --- Den här sidan visar data om mängden SARS-CoV-2 i Stockholm mellan april 2020 och augusti 2021 beräknat som genkopienummer/vecka (från avloppsvatten) standardiserat med bovint coronavirus+ PMMoV. Från september 2021 ändrades metoden. Se [den här sidan för de senaste uppgifterna](../). @@ -11,7 +11,7 @@ Data som visas här samlades in inom ett forskningsprojekt lett av prof. Zeynep Efter koncentrering, filtrering och beredning analyserades proverna med RT-qPCR-teknik för SARS CoV-2 RNA. Primers mot nukleokapsidgenen (N) användes för att detektera SARS-COV-2-genen (tidigare använt och verifierat av [Medema et al (2020)](https://doi.org/10.1016/j.scitotenv.2020.142939). I vissa fall har det råa avloppsvattnet frusits ​​vid –20 °C och koncentrerat avloppsvatten eller renat RNA har lagrats vid -80°C innan nästa analyssteg genomfördes. Koncentrationsmetoden som använddes av prof. Zeynep Cetecioglu Gurol och hennes kollegor baseras på forskargruppens publicerade artikel ([Jafferali et al, 2021](https://doi.org/10.1016/j.scitotenv.2020.142939)) som jämför fyra olika metoder för att koncentrera avloppsvatten. Studiens slutsats var att den dubbla ultrafiltreringsmetoden som anpassats av KTH gruppen har betydligt högre effektivitet jämfört med enstaka filtrerings- och adsorptionsmetoder. För detaljerad information om metoden, se publikationen. -**Ladda ner data:** [Mängd SARS CoV-2 RNA per vecka i råavloppsvatten, med bovin faktor och PPMoV faktor, Excel-fil](https://blobserver.dckube.scilifelab.se/blob/wastewater_data_Stockholm.xlsx). Uppgifterna delas av Inlet Henriksdal, Sickla, Hässelby, Järva, Riksby och Käppala. Data tillgänglig (delvis) från och med vecka 16 2020; uppdateras varje vecka. +**Ladda ner data:** [Mängd SARS CoV-2 RNA per vecka i råavloppsvatten, med bovin faktor och PPMoV faktor, Excel-fil](https://blobserver.dc.scilifelab.se/blob/wastewater_data_Stockholm.xlsx). Uppgifterna delas av Inlet Henriksdal, Sickla, Hässelby, Järva, Riksby och Käppala. Data tillgänglig (delvis) från och med vecka 16 2020; uppdateras varje vecka. **Referera till detta dataset:** Cetecioglu Z G, Williams, C, Khatami, K, Atasoy, M, Nandy, P, Jafferali, M H, Birgersson, M. SARS-CoV-2 Wastewater Data from Stockholm, Sweden. [https://doi.org/10.17044/scilifelab.14315483](https://doi.org/10.17044/scilifelab.14315483) (2021). diff --git a/content/svenska/projects/dashboard.md b/content/svenska/projects/dashboard.md index a83b6fa81..cc5533723 100644 --- a/content/svenska/projects/dashboard.md +++ b/content/svenska/projects/dashboard.md @@ -33,13 +33,13 @@ Detta ordmoln visar de vanligaste orden eller fraserna som förekommer i publika #### Alla publikationer -
+
#### Publikationer uppdelade på forskningsfinansiärer Vi visar endast anslagsgivare där vi identifierat minst 20 publikationer i publikationsdatabasen. -
Vetenskapsrådet:
SciLifeLab/KAW:
Horizon 2020:
+
Vetenskapsrådet:
SciLifeLab/KAW:
Horizon 2020:
## Vanligast förkommande ord eller fraser i abstrakt @@ -49,10 +49,10 @@ Ordmolnen visar de vanligaste förekommande orden eller fraserna från abstrakte #### Alla publikationer -
+
#### Publikationer uppdelade på forskningsfinansiärer Vi visar endast anslagsgivare där vi identifierat minst 20 publikationer i publikationsdatabasen. -
Vetenskapsrådet:
SciLifeLab/KAW:
Horizon 2020:
+
Vetenskapsrådet:
SciLifeLab/KAW:
Horizon 2020:
diff --git a/layouts/dashboards/list.html b/layouts/dashboards/list.html index 3f5bc5242..9ee76df43 100644 --- a/layouts/dashboards/list.html +++ b/layouts/dashboards/list.html @@ -1,6 +1,9 @@ {{ define "main" }} -{{ .Content }} +
+ {{ .Content }} + To easily find dashboards relevant to a specific topic, simply click on the colored tag and the page will filter and display only the dashboards related to that topic. +
{{- partial "dashboards.html" . -}} diff --git a/layouts/dashboards_topics/list.html b/layouts/dashboards_topics/list.html new file mode 100644 index 000000000..b9909f913 --- /dev/null +++ b/layouts/dashboards_topics/list.html @@ -0,0 +1,11 @@ +{{ define "main" }} + +{{ with .Site.GetPage "/dashboards" }}{{ .Content }}{{ end }} + +{{ if eq $.Site.Language.LanguageName "English" }} +

Topic: {{ .Title }}

+ +{{- partial "dashboards.html" . -}} +{{ end }} + +{{ end }} diff --git a/layouts/highlights/list.html b/layouts/highlights/list.html index e1a1ad723..6192bedb8 100644 --- a/layouts/highlights/list.html +++ b/layouts/highlights/list.html @@ -2,7 +2,8 @@
{{ .Content }} - + To easily find dashboards relevant to a specific topic, simply click on the colored tag and the page will filter and display only the dashboards related to that topic. +

If you have published or are about to publish data that you think should be highlighted in this section, please click the button below to suggest a data highlight and the editorial team will get in touch with you as soon as possible to discuss your suggestion. diff --git a/layouts/partials/dashboards.html b/layouts/partials/dashboards.html index cf883feb5..6f9d15210 100644 --- a/layouts/partials/dashboards.html +++ b/layouts/partials/dashboards.html @@ -1,12 +1,27 @@ {{ $displayed_in_homepage := .IsHome }} +{{ $currentPage := path.Split (path.Clean .RelPermalink) }} {{ $homepage_dashboards := slice "post_covid" "vaccines" "wastewater" }} -
+ +
{{ range .Site.Menus.dashboard_menu }} - {{ if or (not $displayed_in_homepage) (in $homepage_dashboards .Identifier) }} + {{ if or + (eq $currentPage.File "dashboards") + (and ($displayed_in_homepage) (in $homepage_dashboards .Identifier)) + (and (eq $currentPage.Dir "/dashboards/topics/") (in (apply .Page.Params.dashboards_topics "urlize" ".") $currentPage.File)) + }}
+ {{ range (.Page.GetTerms "dashboards_topics") }} + {{ .LinkTitle }} + {{ end }}
{{ .Name }}

{{ .Page.Description }}

diff --git a/layouts/partials/footer.html b/layouts/partials/footer.html index b29a0d5eb..137ee1195 100644 --- a/layouts/partials/footer.html +++ b/layouts/partials/footer.html @@ -69,10 +69,12 @@

{{ if eq $.Site.Language.Language

+

{{ if eq $.Site.Language.LanguageName "Svenska" }}Följ oss{{ else }}Connect{{ end }}

+
-
+
{{ if eq $.Site.Language.Language href="https://linkedin.com/company/scilifelab-data-centre">
+
+ + SciLifeLab, Knut and Alice Wallenberg Foundation and SSF Logos + +
-
- SciLifeLab - Elixir Sweden -
-
+ +
{{ if .IsHome }} {{ .Title }} {{ end }}: {{ .Title }} + {{ else if eq .Parent.Section "dashboards_topics" }} + {{ with .Site.GetPage "/dashboards" }} + {{ .Title }} + {{ end }}: + {{ .Title }} {{ else if eq .Parent.Section "resources" }} {{ .Parent.Title }} {{ else }} @@ -38,15 +43,19 @@

{{ if .IsHome }} {{ end }} {{/* - Make an exception for topics which are only used for data highlights + Make an exception for topics which are only used for data highlights and dashboards */}} {{ if eq .Parent.Title "Highlights_topics" }}
  • Data highlights - {{ else }} + {{ else if eq .Parent.Title "Dashboards_topics" }} +
  • Dashboards + {{ else }}
  • {{ .Parent.Title }}
    • {{ end }} +
  • {{ .Title }}
    • +

    diff --git a/layouts/partials/highlights.html b/layouts/partials/highlights.html index 57eaa5abe..db166f236 100644 --- a/layouts/partials/highlights.html +++ b/layouts/partials/highlights.html @@ -1,10 +1,14 @@ -{{ $displayedInHome := .IsHome }} {{ $highlights := where (where $.Site.Pages "Section" "highlights" ) "Kind" "page" }} +{{ $currentPage := path.Split (path.Clean .RelPermalink) }} +{{ $displayedInHome := .IsHome }} {{ if $displayedInHome }} {{ $highlights = first 3 $highlights }} {{ end }} -
    + +
    {{ range $highlights }} + + {{ if or (ne $currentPage.Dir "/highlights/topics/") (in (apply .Params.highlights_topics "urlize" ".") $currentPage.File) }}
    {{ .Title }} @@ -28,5 +32,6 @@
    {{ .Title }}

    + {{ end }} {{ end }}
    diff --git a/layouts/partials/navbar.html b/layouts/partials/navbar.html index 61c62484c..f07bf1367 100644 --- a/layouts/partials/navbar.html +++ b/layouts/partials/navbar.html @@ -178,7 +178,7 @@
    -
    Topics:
    + {{ range $ind, $topic := .Site.Menus.topics_menu }} + {{ range .Site.Menus.topics_menu }} + {{ if (in $topics .Identifier) }} +
    +
    +
    + +
    {{ .Page.Params.credits }}
    +
    +
    {{ .Name }}
    +

    {{ .Page.Description }}

    +
    +
    + {{ end }} + {{ end }} +
    diff --git a/layouts/shortcodes/RECOVAC_date_modified.html b/layouts/shortcodes/RECOVAC_date_modified.html index 13c75566a..0a3d39056 100644 --- a/layouts/shortcodes/RECOVAC_date_modified.html +++ b/layouts/shortcodes/RECOVAC_date_modified.html @@ -7,6 +7,6 @@ document.getElementById("last_modified").innerText = String(dataset_info.modified).substring(0, 10); } }; - xmlhttp.open("GET", "https://blobserver.dckube.scilifelab.se/blob/vaccination_RECO_timeseries_buttons.json/info.json", true); + xmlhttp.open("GET", "https://blobserver.dc.scilifelab.se/blob/vaccination_RECO_timeseries_buttons.json/info.json", true); xmlhttp.send(); \ No newline at end of file diff --git a/layouts/shortcodes/crush_covid_modified.html b/layouts/shortcodes/crush_covid_modified.html deleted file mode 100644 index d09469671..000000000 --- a/layouts/shortcodes/crush_covid_modified.html +++ /dev/null @@ -1,11 +0,0 @@ - diff --git a/layouts/shortcodes/csss_date_modified.html b/layouts/shortcodes/csss_date_modified.html index b6cbcdd7d..2faa7148c 100644 --- a/layouts/shortcodes/csss_date_modified.html +++ b/layouts/shortcodes/csss_date_modified.html @@ -6,6 +6,6 @@ document.getElementById("last_modified").innerText = String(dataset_info.modified).substring(0,10); } }; -xmlhttp.open("GET", "https://blobserver.dckube.scilifelab.se/blob/CSSS_estimates_mostrecent.csv/info.json", true); +xmlhttp.open("GET", "https://blobserver.dc.scilifelab.se/blob/CSSS_estimates_mostrecent.csv/info.json", true); xmlhttp.send(); diff --git a/layouts/shortcodes/postcovid_date_modified.html b/layouts/shortcodes/postcovid_date_modified.html index a6614d0e3..9c5513d6c 100644 --- a/layouts/shortcodes/postcovid_date_modified.html +++ b/layouts/shortcodes/postcovid_date_modified.html @@ -7,6 +7,6 @@ document.getElementById("last_modified").innerText = String(dataset_info.modified).substring(0, 10); } }; - xmlhttp.open("GET", "https://blobserver.dckube.scilifelab.se/blob/accompdiag_table_swe.json/info.json", true); + xmlhttp.open("GET", "https://blobserver.dc.scilifelab.se/blob/accompdiag_table_swe.json/info.json", true); xmlhttp.send(); \ No newline at end of file diff --git a/layouts/shortcodes/publications_per_month.html b/layouts/shortcodes/publications_per_month.html index 69cd8735a..ce7164e51 100644 --- a/layouts/shortcodes/publications_per_month.html +++ b/layouts/shortcodes/publications_per_month.html @@ -1,5 +1,5 @@
    + type="text/javascript">Plotly.d3.json("https://blobserver.dc.scilifelab.se/blob/covid-portal-publication-counts.json", function (error, dyndata) { window.PLOTLYENV = window.PLOTLYENV || {}; if (document.getElementById("a2f7babf-25ab-4815-ba09-0909f42ebaca")) { Plotly.newPlot("a2f7babf-25ab-4815-ba09-0909f42ebaca", [{ hovertemplate: "Month: %{x|%B %Y}
    Publications Added: %{y}", marker: { color: "rgb(222,44,108)" }, name: "Added Per Month", type: "bar", x: dyndata["months"], y: dyndata["per_month"] }, { hovertemplate: "Date: %{x}
    Total Publications: %{y}", line: { width: 5 }, marker: { color: "rgb(46,104,165)" }, mode: "lines", name: "Cumulative Total", type: "scatter", x: dyndata["dates"], y: dyndata["cumsums"] }], { font: { size: 14 }, height: 500, margin: { r: 150 }, plot_bgcolor: "white", template: { data: { bar: [{ error_x: { color: "#2a3f5f" }, error_y: { color: "#2a3f5f" }, marker: { line: { color: "#E5ECF6", width: .5 } }, type: "bar" }], barpolar: [{ marker: { line: { color: "#E5ECF6", width: .5 } }, type: "barpolar" }], carpet: [{ aaxis: { endlinecolor: "#2a3f5f", gridcolor: "white", linecolor: "white", minorgridcolor: "white", startlinecolor: "#2a3f5f" }, baxis: { endlinecolor: "#2a3f5f", gridcolor: "white", linecolor: "white", minorgridcolor: "white", startlinecolor: "#2a3f5f" }, type: "carpet" }], choropleth: [{ colorbar: { outlinewidth: 0, ticks: "" }, type: "choropleth" }], contour: [{ colorbar: { outlinewidth: 0, ticks: "" }, colorscale: [[0, "#0d0887"], [.1111111111111111, "#46039f"], 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