diff --git a/orderly_config.yml b/orderly_config.yml index 636ea700..6c1ac52f 100644 --- a/orderly_config.yml +++ b/orderly_config.yml @@ -5,4 +5,5 @@ plugins: lassadoubledb: "Z:/Lassa/Databases/Double Extractions" eboladoubledb2: "Z:/Ebola/databases/NEW DOUBLE EXTRACTION DATABASES" eboladoubledb: "Z:/Ebola/databases/Double extraction databases" - ebolasingledb: "Z:/Ebola/databases/Single extraction databases" \ No newline at end of file + ebolasingledb: "Z:/Ebola/databases/Single extraction databases" + sarssingledb: "Y:/SARS/Databases/full_extraction" \ No newline at end of file diff --git a/shared/lassa_functions.R b/shared/lassa_functions.R index 8646f330..5a80ae8d 100644 --- a/shared/lassa_functions.R +++ b/shared/lassa_functions.R @@ -1,62 +1,86 @@ #function to tidy-up all dataframes -curation <- function(articles, outbreaks, models, parameters, plotting) { +data_curation <- function(articles, outbreaks, models, parameters, plotting) { articles <- articles %>% - mutate(refs = paste(first_author_first_name," (",year_publication,")",sep="")) %>% #define references - group_by(refs) %>% mutate(counter = row_number()) %>% ungroup() %>% #distinguish same-author-same-year references - mutate(new_refs = ifelse(refs %in% refs[duplicated(refs)], paste0(sub("\\)$", "", refs),letters[counter],")"), refs)) %>% - select(-counter,-refs) %>% rename(refs = new_refs) - outbreaks <- outbreaks %>% - mutate(refs = articles$refs[match(covidence_id, articles$covidence_id)]) + mutate(refs = paste(first_author_first_name," (",year_publication,")",sep="")) %>% #define references + group_by(refs) %>% mutate(counter = row_number()) %>% ungroup() %>% #distinguish same-author-same-year references + mutate(new_refs = ifelse(refs %in% refs[duplicated(refs)], paste0(sub("\\)$", "", refs),letters[counter],")"), refs)) %>% + select(-counter,-refs) %>% rename(refs = new_refs) + + if(dim(outbreaks)[1]>0) + { + outbreaks <- outbreaks %>% + mutate(refs = articles$refs[match(covidence_id, articles$covidence_id)]) + } + models <- models %>% - mutate(refs = articles$refs[match(covidence_id, articles$covidence_id)]) + mutate(refs = articles$refs[match(covidence_id, articles$covidence_id)]) + parameters <- parameters %>% - mutate(refs = articles$refs[match(covidence_id, articles$covidence_id)]) %>% - filter(!parameter_from_figure) + mutate(refs = articles$refs[match(covidence_id, articles$covidence_id)]) %>% + filter(!parameter_from_figure) param4plot <- parameters %>% - mutate_at(vars(parameter_value, parameter_lower_bound, parameter_upper_bound, - parameter_uncertainty_lower_value, parameter_uncertainty_upper_value), - list(~ ifelse(inverse_param, 1/.x, .x))) %>% - mutate_at(vars(parameter_value, parameter_lower_bound, parameter_upper_bound, - parameter_uncertainty_lower_value, parameter_uncertainty_upper_value), - list(~ .x * 10^exponent)) %>% - mutate_at(vars(parameter_value,parameter_lower_bound,parameter_upper_bound, - parameter_uncertainty_lower_value,parameter_uncertainty_upper_value), #account for different units - list(~ ifelse(parameter_unit == "Weeks", . * 7, .))) %>% - mutate(parameter_unit = ifelse(parameter_unit == "Weeks", "Days", parameter_unit)) %>% - mutate(no_unc = is.na(parameter_uncertainty_lower_value) & is.na(parameter_uncertainty_upper_value), #store uncertainty in pu_lower and pu_upper - parameter_uncertainty_lower_value = case_when( - parameter_uncertainty_singe_type == "Maximum" & no_unc ~ parameter_value, - parameter_uncertainty_singe_type == "Standard deviation (Sd)" & no_unc ~ parameter_value-parameter_uncertainty_single_value, - parameter_uncertainty_singe_type == "Standard Error (SE)" & no_unc ~ parameter_value-parameter_uncertainty_single_value, - distribution_type == "Gamma" & no_unc ~ qgamma(0.05, shape = (distribution_par1_value/distribution_par2_value)^2, rate = distribution_par1_value/distribution_par2_value^2), - TRUE ~ parameter_uncertainty_lower_value), - parameter_uncertainty_upper_value = case_when( - parameter_uncertainty_singe_type == "Maximum" & no_unc ~ parameter_uncertainty_single_value, - parameter_uncertainty_singe_type == "Standard deviation (Sd)" & no_unc ~ parameter_value+parameter_uncertainty_single_value, - parameter_uncertainty_singe_type == "Standard Error (SE)" & no_unc ~ parameter_value+parameter_uncertainty_single_value, - distribution_type == "Gamma" & no_unc ~ qgamma(0.95, shape = (distribution_par1_value/distribution_par2_value)^2, rate = distribution_par1_value/distribution_par2_value^2), - TRUE ~ parameter_uncertainty_upper_value)) %>% - select(-c(no_unc)) %>% - mutate(central = coalesce(parameter_value,100*cfr_ifr_numerator/cfr_ifr_denominator,0.5*(parameter_lower_bound+parameter_upper_bound))) #central value for plotting + mutate_at(vars(parameter_value, parameter_lower_bound, parameter_upper_bound, + parameter_uncertainty_lower_value, parameter_uncertainty_upper_value), + list(~ ifelse(inverse_param, 1/.x, .x))) %>% + mutate_at(vars(parameter_value, parameter_lower_bound, parameter_upper_bound, + parameter_uncertainty_lower_value, parameter_uncertainty_upper_value), + list(~ .x * 10^exponent)) %>% + mutate_at(vars(parameter_value,parameter_lower_bound,parameter_upper_bound, + parameter_uncertainty_lower_value,parameter_uncertainty_upper_value), #account for different units + list(~ ifelse(parameter_unit %in% "Weeks", . * 7, .))) %>% + mutate(parameter_unit = ifelse(parameter_unit %in% "Weeks", "Days", parameter_unit)) %>% + mutate(no_unc = is.na(parameter_uncertainty_lower_value) & is.na(parameter_uncertainty_upper_value), #store uncertainty in pu_lower and pu_upper + parameter_uncertainty_lower_value = case_when( + parameter_uncertainty_singe_type == "Maximum" & no_unc ~ parameter_value, + parameter_uncertainty_singe_type == "Standard deviation (Sd)" & no_unc ~ parameter_value-parameter_uncertainty_single_value, + parameter_uncertainty_singe_type == "Standard Error (SE)" & no_unc ~ parameter_value-parameter_uncertainty_single_value, + distribution_type == "Gamma" & no_unc ~ qgamma(0.05, shape = (distribution_par1_value/distribution_par2_value)^2, rate = distribution_par1_value/distribution_par2_value^2), + TRUE ~ parameter_uncertainty_lower_value), + parameter_uncertainty_upper_value = case_when( + parameter_uncertainty_singe_type == "Maximum" & no_unc ~ parameter_uncertainty_single_value, + parameter_uncertainty_singe_type == "Standard deviation (Sd)" & no_unc ~ parameter_value+parameter_uncertainty_single_value, + parameter_uncertainty_singe_type == "Standard Error (SE)" & no_unc ~ parameter_value+parameter_uncertainty_single_value, + distribution_type == "Gamma" & no_unc ~ qgamma(0.95, shape = (distribution_par1_value/distribution_par2_value)^2, rate = distribution_par1_value/distribution_par2_value^2), + TRUE ~ parameter_uncertainty_upper_value)) %>% + select(-c(no_unc)) %>% + mutate(central = coalesce(parameter_value,100*cfr_ifr_numerator/cfr_ifr_denominator,0.5*(parameter_lower_bound+parameter_upper_bound))) #central value for plotting if (plotting) { parameters <- param4plot } else { - parameters <- parameters %>% - left_join(param4plot %>% select(parameter_data_id, central), by = "parameter_data_id") + check_param_id <- (parameters$parameter_data_id == param4plot$parameter_data_id ) # check that parameter data ids didn't get scrambled + if(sum(check_param_id)==dim(parameters)[1]) + { + parameters$central <- param4plot$central + } else { + errorCondition('parameters not in right order to match') + } + } + + if(dim(outbreaks)[1]>0) + { + outbreaks <- outbreaks %>% mutate(outbreak_location = str_replace_all(outbreak_location, "\xe9" , "é")) } - outbreaks <- outbreaks %>% mutate(outbreak_location = str_replace_all(outbreak_location, "\xe9" , "é")) parameters <- parameters %>% mutate(parameter_type = str_replace_all(parameter_type, "\x96" , "–"), population_country = str_replace_all(population_country, c("昼㸴" = "ô", "�" = "ô"))) - + return(list(articles = articles, outbreaks = outbreaks, models = models, parameters = parameters)) } + +curation <- function(articles, outbreaks, models, parameters, plotting) { + #call data_curation function (which at some stage will move to epireview) but keep curation to be backward competible + df <- data_curation(articles,outbreaks,models,parameters,plotting) + + return(list(articles = df$articles, outbreaks = df$outbreaks, + models = df$models, parameters = df$parameters)) +} + # function to produce forest plot for given dataframe forest_plot <- function(df, label, color_column, lims) { @@ -222,7 +246,7 @@ metamean_wrap <- function(dataframe, estmeansd_method, gg <- png::readPNG("temp.png", native = TRUE) file.remove("temp.png") - #gg <- wrap_elements(plot = rasterGrob(pg, interpolate = TRUE)) + gg <- wrap_elements(plot = rasterGrob(gg, interpolate = TRUE)) return(list(result = mtan, plot = gg)) } diff --git a/shared/utils.R b/shared/utils.R index 03dfcc4a..7c59617d 100644 --- a/shared/utils.R +++ b/shared/utils.R @@ -81,7 +81,34 @@ database_files <- function(pathogen) { "DIDE Priority Pathogens LASSA - Thom.accdb", "DIDE Priority Pathogens LASSA - Tristan.accdb" ) - ) + ), + SARS = list( + sarssingledb = c( + "DIDE Priority Pathogens SARS - Anna Vicco.accdb", + "DIDE Priority Pathogens SARS - Anna Vicco double.accdb", + "DIDE Priority Pathogens SARS - Anne.accdb", + "DIDE Priority Pathogens SARS - Bethan.accdb", + "DIDE Priority Pathogens SARS - Christian.accdb", + "DIDE Priority Pathogens SARS - Dominic.accdb", + "DIDE Priority Pathogens SARS - Ettie.accdb", + "DIDE Priority Pathogens SARS - Joseph.accdb", + "DIDE Priority Pathogens SARS - Kanchan.accdb", + "DIDE Priority Pathogens SARS - Kelly.accdb", + "DIDE Priority Pathogens SARS - Kieran.accdb", + "DIDE Priority Pathogens SARS - Patrick.accdb", + "DIDE Priority Pathogens SARS - Paula_double.accdb", + "DIDE Priority Pathogens SARS - Paula_single.accdb", + "DIDE Priority Pathogens SARS - Rebecca.accdb", + "DIDE Priority Pathogens SARS - Richard.accdb", + "DIDE Priority Pathogens SARS - Rob.accdb", + "DIDE Priority Pathogens SARS - Ruth.accdb", + "DIDE Priority Pathogens SARS - Sangeeta.accdb", + "DIDE Priority Pathogens SARS - Sequoia.accdb", + "DIDE Priority Pathogens SARS - Thom.accdb", + "DIDE Priority Pathogens SARS - Tristan.accdb" + ) + ) + ## Nipah Database files ) diff --git a/shared/world_cases_table.xlsx b/shared/world_cases_table.xlsx new file mode 100644 index 00000000..949d75ce Binary files /dev/null and b/shared/world_cases_table.xlsx differ diff --git a/src/db_compilation/cleaning.R b/src/db_compilation/cleaning.R index 23cfcbec..6d4c1b74 100644 --- a/src/db_compilation/cleaning.R +++ b/src/db_compilation/cleaning.R @@ -203,6 +203,131 @@ clean_dfs <- function(df, pathogen) { # revised qa after parameter removed: now outbreak only df[df$covidence_id %in% 152, c("qa_m1", "qa_m2", "qa_a3", "qa_a4", "qa_d6", "qa_d7")] <- NA } + + if (pathogen == "SARS") + { + #journal consistency + df <- df %>% + mutate(journal = str_to_title(journal)) %>% + mutate(journal = sub("^The\\s+", "", journal)) %>% + mutate(journal = ifelse(journal == 'Bmj','British Medical Journal', + ifelse(journal == 'Transactions Of The Royal Society Tropical Medicine And Hygiene','Transactions Of The Royal Society Of Tropical Medicine And Hygiene', + journal))) %>% + #journal typos + # mutate(journal = case_when( + # covidence_id == 870 ~ 'Transactions Of The Royal Society Of Tropical Medicine And Hygiene', + # TRUE ~ journal)) %>% + #year typos + mutate(year_publication = case_when( + covidence_id == 613 ~ 2004, + covidence_id == 3394 ~ 2018, + covidence_id == 4233 ~ 2009, + covidence_id == 4261 ~ 2006, + covidence_id == 5861 ~ 2003, + covidence_id == 5880 ~ 2008, + covidence_id == 6144 ~ 2006, + TRUE ~ year_publication)) %>% + #volume typos + mutate(volume = case_when( + covidence_id == 2043 ~ 5, + covidence_id == 6239 ~ 68, + covidence_id == 7256 ~ 15, + TRUE ~ volume)) %>% + #issue typos + mutate(issue = case_when( + covidence_id %in% c(558, 4217, 4676, 7256, 5650, 5484) ~ '1', + covidence_id %in% c(3421, 7260, 8759, 10922, 4261) ~ '2', + covidence_id %in% c(1036, 1564, 3449, 3468, 4169, 5635, 11040) ~ '3', + covidence_id %in% c(1046, 5880, 6145) ~ '4', + covidence_id %in% c(1102, 2043, 3343, 4675) ~ '5', + covidence_id %in% c(2006, 11042) ~ '6', + covidence_id %in% c(523, 4614, 5585) ~ '9', + covidence_id %in% c(5589, 5595) ~ '10', + covidence_id %in% c(4184) ~ '12', + covidence_id %in% c(5879) ~ '14', + covidence_id %in% c(5069) ~ '1554', + covidence_id %in% c(3375) ~ '5627', + covidence_id %in% c(5393) ~ '7433', + TRUE ~ issue)) %>% + #page typos + mutate(page_first = case_when( + covidence_id %in% c(793) ~ 32, + covidence_id %in% c(1641) ~ 195, + covidence_id %in% c(4614) ~ 925, + # covidence_id %in% c(1413, 2567, 2610, 2661, 3215, 3258, 3635) ~ NA, + TRUE ~ page_first)) %>% + mutate(page_last = case_when( + covidence_id == 1641 ~ 194, + covidence_id == 4614 ~ 926, + TRUE ~ page_last)) %>% + #title typos + mutate(article_title = case_when( + covidence_id == 392 ~ 'Epidemiologic clues to SARS origin in China', + covidence_id == 513 ~ 'Prevalence of subclinical infection by the SARS coronavirus among general practitioners in Hong Kong', + covidence_id == 613 ~ 'Characterization of severe acute respiratory syndrome coronavirus genomes in Taiwan: molecular epidemiology and genome evolution', + covidence_id == 718 ~ 'Clinical description of a completed outbreak of SARS in Vietnam, February-May 2003', + covidence_id == 727 ~ 'Risk of severe acute respiratory syndrome-associated coronavirus transmission aboard commercial aircraft', + covidence_id == 745 ~ 'Brief Report: Incubation Period Duration and Severity of Clinical Disease Following Severe Acute Respiratory Syndrome Coronavirus Infection', + covidence_id == 1046 ~ 'The SARS outbreak in a general hospital in Tianjin, China -- the case of super-spreader', + covidence_id == 1564 ~ 'Theoretically estimated risk of severe acute respiratory syndrome transmission through blood transfusion during an epidemic in Shenzhen, Guangdong, China in 2003', + covidence_id == 1641 ~ 'SARS surveillance during emergency public health response, United States, March-July 2003', + covidence_id == 1672 ~ 'The effect of global travel on the spread of SARS', + covidence_id == 2043 ~ 'Risk factors for SARS transmission from patients requiring intubation: a multicentre investigation in Toronto, Canada', + covidence_id == 2613 ~ 'Rapid awareness and transmission of severe acute respiratory syndrome in Hanoi French Hospital, Vietnam', + covidence_id == 4285 ~ 'SARS in Singapore--predictors of disease severity', + covidence_id == 4455 ~ 'Severe acute respiratory syndrome (SARS) in Singapore: clinical features of index patient and initial contacts', + covidence_id == 4561 ~ 'Neutralizing antibody response and SARS severity', + covidence_id == 4612 ~ 'Secondary household transmission of SARS, Singapore', + covidence_id == 5387 ~ 'Transmission characteristics of MERS and SARS in the healthcare setting: a comparative study', + covidence_id == 5589 ~ 'A simple approximate mathematical model to predict the number of severe acute respiratory syndrome cases and deaths', + covidence_id == 5635 ~ 'Effectiveness of noninvasive positive pressure ventilation in the treatment of acute respiratory failure in severe acute respiratory syndrome', + covidence_id == 5743 ~ 'The outbreak of SARS at Tan Tock Seng Hospital--relating epidemiology to control', + covidence_id == 5878 ~ 'Alternative methods of estimating an incubation distribution: examples from severe acute respiratory syndrome', + covidence_id == 7260 ~ 'Reconstruction of the infection curve for SARS epidemic in Beijing, China using a back-projection method', + covidence_id == 12002 ~ 'Heterogeneous and Stochastic Agent-Based Models for Analyzing Infectious Diseases Super Spreaders', + TRUE ~ article_title)) %>% + #title consistency + mutate(article_title = gsub(";", ",", article_title)) %>% + mutate(article_title = gsub("\n", " ", article_title)) %>% + mutate(article_title = gsub("\\s+", " ", article_title)) %>% + mutate(article_title = str_to_title(article_title)) %>% + #missing dois + mutate(doi = sub(".*?10\\.", "10.", doi)) %>% + mutate(doi = case_when( + covidence_id == 1090 ~ '10.1016/s019606440300828x', + covidence_id == 4261 ~ '10.1017/s0950268805004826', + covidence_id == 4268 ~ '10.3201/eid1009.040155', + covidence_id == 4387 ~ '10.1093/aje/kwh056', + covidence_id == 5006 ~ '10.1007/s11325-004-0097-0', + covidence_id == 5635 ~ '10.1378/chest.126.3.845', + covidence_id == 5652 ~ '10.1001/jama.293.12.1450-c', + covidence_id == 5682 ~ '10.1128/jcm.44.2.359-365.2006', + covidence_id == 5879 ~ '10.1001/archinte.166.14.1505', + covidence_id == 6239 ~ '10.1016/s1726-4901(09)70124-8', + covidence_id == 7260 ~ '10.1080/03610910701792562', + covidence_id == 8001 ~ '10.1360/03ww0126', + covidence_id == 10922 ~ '10.1016/j.jmaa.2006.11.026', + covidence_id == 10924 ~ '10.1137/040615547', + covidence_id == 11000 ~ '10.3201/eid1309.070081', + TRUE ~ doi)) %>% + #paper copies + # mutate(paper_copy_only = case_when( + # covidence_id %in% c(845,917) ~ FALSE, + # TRUE ~ paper_copy_only)) %>% + #name typos + # mutate(first_author_surname = case_when( + # covidence_id == 2648 ~ 'Shirley C.', + # covidence_id == 1447 ~ 'N.A.', + # TRUE ~ first_author_surname)) %>% + mutate(first_author_first_name = sub(".*\\.(.*)", "\\1", first_author_first_name)) %>% + mutate(first_author_first_name = sub("^\\s+", "", first_author_first_name)) #%>% + # mutate(first_author_first_name = case_when( + # covidence_id == 2648 ~ 'Nimo-Paintsil', + # covidence_id == 2585 ~ 'Dalhat', + # covidence_id == 1033 ~ 'Ehichioya', + # covidence_id == 661 ~ 'Kerneis', + # TRUE ~ first_author_first_name)) + } } ################## @@ -284,6 +409,11 @@ clean_dfs <- function(df, pathogen) { ) ) } + + if (pathogen == 'SARS') { + df <- df %>% filter(!is.na(id)) + } + df <- df %>% select(-c("access_model_id")) } @@ -314,6 +444,7 @@ clean_dfs <- function(df, pathogen) { "Yugoslavia" ) ) + } if (pathogen == "LASSA") { # clean locations @@ -389,6 +520,7 @@ clean_dfs <- function(df, pathogen) { ), population_country = str_replace_all(population_country, ",", ", ") ) + } ############################################# # Pathogen-specific parameter data cleaning # diff --git a/src/db_compilation/orderly.R b/src/db_compilation/orderly.R index 567a29ce..fc938d47 100644 --- a/src/db_compilation/orderly.R +++ b/src/db_compilation/orderly.R @@ -64,6 +64,10 @@ orderly_resource( "lassa_params_fixing.csv", "lassa_models_fixing.csv", "lassa_outbreaks_fixing.csv", + ## SARS FIXING FILES + "sars_qa_fixing.csv", + "sars_params_fixing.csv", + "sars_models_fixing.csv", ## NIPAH FIXING FILES "cleaning.R" ) @@ -79,6 +83,11 @@ fixing_files <- list( models_fix = "ebola_models_fixing.csv", qa_fix = "ebola_qa_fixing.csv" ), + SARS = list( + params_fix = "sars_params_fixing.csv", + models_fix = "sars_models_fixing.csv", + qa_fix = "sars_qa_fixing.csv" + ), LASSA = list( params_fix = "lassa_params_fixing.csv", models_fix = "lassa_models_fixing.csv", @@ -211,7 +220,7 @@ if (pathogen == "LASSA") { select(-any_of(c("fixed", "num_rows", "matching"))) } -if (pathogen == "EBOLA") { +if (pathogen %in% c("EBOLA","SARS")) { # join article data to qa files article_double_details <- article_double %>% select(-c(starts_with("qa"))) @@ -337,7 +346,7 @@ if (pathogen == "LASSA") { outbreak_all <- clean_dfs(outbreak_all, pathogen) } -if (pathogen == "EBOLA") { +if (pathogen %in% c("EBOLA","SARS")) { # Add article QA scores to article data article_all <- add_qa_scores(article_all, parameter_all) @@ -356,7 +365,7 @@ write_csv(parameter_all, "parameters.csv") write_csv(model_all, "models.csv") write_csv(article_all, "articles.csv") -if (pathogen == "EBOLA") { +if (pathogen %in% c("EBOLA","SARS")) { file.create("outbreaks.csv") } else { write_csv(outbreak_all, "outbreaks.csv") diff --git a/src/db_compilation/sars_models_fixing.csv b/src/db_compilation/sars_models_fixing.csv new file mode 100644 index 00000000..564f7d90 --- /dev/null +++ b/src/db_compilation/sars_models_fixing.csv @@ -0,0 +1,15 @@ +fixed,Covidence_ID,Name_data_entry,Article_ID,Model_type,Compartmental_type,Stoch_Deter,Theoretical_model,Interventions_type,Code_available,Transmission_route,Assumptions,access_model_id,Pathogen,num_rows,matching +TRUE,19,Christian,1,Other,Other compartmental,Deterministic,FALSE,NA,FALSE,Unspecified,Unspecified,1,SARS-CoV,1,0 +TRUE,435,Paula,3,Agent / Individual based,Not compartmental,Stochastic,FALSE,NA,FALSE,Airborne or close contact,Heterogenity in transmission rates - over time,1,SARS-CoV,1,0 +TRUE,646,Bethan Cracknell Daniels,7,Other,SEIR,Deterministic,TRUE,NA,FALSE,Unspecified,Heterogenity in transmission rates - over time,1,SARS-CoV,1,0 +TRUE,655,Bethan Cracknell Daniels,8,Compartmental,SEIR,Deterministic,FALSE,Other,FALSE,Unspecified,Heterogenity in transmission rates - over time;Latent period is same as incubation period,2,SARS-CoV,1,0 +TRUE,696,Dominic Dee,2,Other,SIS,Stochastic,TRUE,Other,FALSE,Unspecified,Heterogenity in transmission rates - between groups,1,SARS-CoV,1,0 +TRUE,706,Dominic Dee,3,Branching process,Not compartmental,Stochastic,TRUE,Unspecified,FALSE,Unspecified,Unspecified,2,SARS-CoV,1,0 +TRUE,965,Rob Johnson,4,Compartmental,Other compartmental,NA,TRUE,Hospitals;Quarantine,FALSE,Airborne or close contact;Human to human (direct contact),Heterogenity in transmission rates - between groups;Heterogenity in transmission rates - over time,2,SARS-CoV,1,0 +TRUE,1023,Kieran Drake,7,Other,Not compartmental,Deterministic,FALSE,NA,FALSE,Unspecified,Unspecified,1,SARS-CoV,1,0 +TRUE,1023,Kieran Drake,7,Compartmental,SIR,Deterministic,FALSE,NA,FALSE,Unspecified,Unspecified,1,SARS-CoV,1,0 +TRUE,1036,Richard Sheppard,5,Compartmental,Other compartmental,Deterministic,FALSE,Behaviour changes;Quarantine,FALSE,Unspecified,Heterogenity in transmission rates - over time,1,SARS-CoV,1,0 +TRUE,1672,Sangeeta,6,Compartmental,SEIR,Deterministic,TRUE,NA,FALSE,Human to human (direct contact),Homogeneous mixing,1,SARS-CoV,1,0 +TRUE,1906,Sequoia,1,Other,Other compartmental,Deterministic,TRUE,Quarantine;Other,FALSE,Unspecified,Heterogenity in transmission rates - between groups;Heterogenity in transmission rates - over time,1,SARS-CoV,1,0 +TRUE,1929,Christian,10,Compartmental,Other compartmental,Stochastic,FALSE,Behaviour changes;Hospitals;Other,FALSE,Airborne or close contact,Heterogenity in transmission rates - between groups;Heterogenity in transmission rates - over time,2,SARS-CoV,1,0 +TRUE,4837,Anna V,10,Agent / Individual based,Not compartmental,Deterministic,FALSE,Contact tracing;Quarantine,FALSE,Unspecified,Homogeneous mixing,2,SARS-CoV,1,0 diff --git a/src/db_compilation/sars_params_fixing.csv b/src/db_compilation/sars_params_fixing.csv new file mode 100644 index 00000000..c3af524a --- /dev/null +++ b/src/db_compilation/sars_params_fixing.csv @@ -0,0 +1,224 @@ +fixed,Covidence_ID,Name_data_entry,Article_ID,Parameter_type,Parameter value,Exponent,Parameter unit,Parameter lower bound,Parameter upper bound,Parameter value type,Parameter uncertainty - single value,Parameter uncertainty - singe type,Parameter uncertainty - lower value,Parameter uncertainty - upper value,Parameter uncertainty - type,CFR_IFR_numerator,CFR_IFR_denominator,Distribution_type,Distribution_par1_value,Distribution_par1_type,Distribution_par1_uncertainty,Distribution_par2_value,Distribution_par2_type,Distribution_par2_uncertainty,Method_from_supplement,Method_moment_value,CFR_IFR_Method,Method_R,Method_disaggregated_by,Method_disaggregated,Method_disaggregated_only,Riskfactor_outcome,Riskfactor_name,Riskfactor_occupation,Riskfactor_significant,Riskfactor_adjusted,Population_sex,Population_sample_type,Population_group,Population_age_min,Population_age_max,Population_sample_size,Population_country,Population_location,Population_study_start_day,Population_study_start_month,Population_study_start_year,Population_study_end_day,Population_study_end_month,Population_study_end_year,Genome_site,Genomic_sequence_available,Other_delay_start,Other_delay_end,Inverse_param,Parameter_FromFigure,access_param_id,Pathogen,R_pathway,num_rows,matching +TRUE,19,Christian,1,Growth rate (r),0.16,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Population based,General population,NA,NA,NA,China,Beijing,17,Mar-03,2003,15,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,19,Christian,1,Growth rate (r),0.09,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Population based,General population,NA,NA,NA,Hong Kong SAR; China,Hong Kong,17,Mar-03,2003,15,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,2,SARS-CoV,NA,1,0 +TRUE,19,Christian,1,Growth rate (r),0.12,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Population based,General population,NA,NA,NA,Singapore,Singapore,17,Mar-03,2003,15,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,19,Christian,1,Reproduction number (Basic R0),2.7,0,No units,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Growth rate,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Population based,General population,NA,NA,NA,Singapore,Singapore,17,Mar-03,2003,15,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,4,SARS-CoV,NA,1,0 +TRUE,19,Christian,1,Reproduction number (Basic R0),2.1,0,No units,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Growth rate,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Population based,General population,NA,NA,NA,Hong Kong SAR; China,Hong Kong,17,Mar-03,2003,15,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,19,Christian,1,Reproduction number (Basic R0),3.8,0,No units,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Growth rate,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Population based,General population,NA,NA,NA,China,Beijing,17,Mar-03,2003,15,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,60,Christian,2,Mutations – substitution rate,NA,-3,Substitutions/site/year,0.8,2.38,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Persons under investigation,NA,NA,11,Hong Kong SAR; China;Singapore,Hong Kong; Singapore; Other,14,Apr-04,2003,29,Aug-08,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,60,Christian,2,Mutations – mutation rate,NA,-3,Substitutions/site/year,0.42,2.38,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,Method;Region,TRUE,TRUE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Persons under investigation,NA,NA,16,China;Hong Kong SAR; China;Singapore,China; Hong Kong; Singapore; Other,14,Apr-04,2003,29,Aug-08,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,60,Christian,2,Mutations – substitution rate,NA,-3,Substitutions/site/year,0.81,9.22,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,Other,TRUE,TRUE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Persons under investigation,NA,NA,16,China;Hong Kong SAR; China;Singapore,China; Hong Kong; Singapore; Other,14,Apr-04,2003,29,Aug-08,2003,non-synonymous sites,FALSE,NA,NA,FALSE,FALSE,9,SARS-CoV,NA,1,0 +TRUE,60,Christian,2,Mutations – substitution rate,NA,-3,Substitutions/site/year,0,8.5,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,Other,TRUE,TRUE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Persons under investigation,NA,NA,16,China;Hong Kong SAR; China;Singapore,China; Hong Kong; Singapore; Other,14,Apr-04,2003,29,Aug-08,2003,synonymous sites,FALSE,NA,NA,FALSE,FALSE,10,SARS-CoV,NA,1,0 +TRUE,180,Christian,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Close contact;Household contact;Non-household contact;Occupation,Healthcare,Unspecified,Unspecified,NA,Community based,Persons under investigation,NA,NA,1112,China,Beijing,NA,NA,2003,NA,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,35,SARS-CoV,NA,1,0 +TRUE,180,Christian,6,Attack rate,NA,0,NA,6.3,28.8,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,TRUE,TRUE,NA,NA,NA,NA,NA,NA,Contact based,Persons under investigation,NA,NA,669,China,Beijing,NA,NA,2003,23,May-05,2023,NA,FALSE,NA,NA,FALSE,FALSE,33,SARS-CoV,NA,1,0 +TRUE,180,Christian,6,Attack rate,0,0,NA,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,TRUE,TRUE,NA,NA,NA,NA,NA,NA,Contact based,Persons under investigation,NA,NA,363,China,Beijing,NA,NA,2003,23,May-05,2023,NA,FALSE,NA,NA,FALSE,FALSE,34,SARS-CoV,NA,1,0 +TRUE,392,Christian,3,Severity - case fatality rate (CFR),3.8,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,55,1454,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Community based,General population,18,92,1454,China,Foshan municipality; Guangdong Province,16,Nov-11,2002,30,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,392,Christian,3,Severity - case fatality rate (CFR),12.7,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Community based,General population,65,92,NA,China,Foshan municipality; Guangdong Province,16,Nov-11,2002,30,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,392,Christian,3,Seroprevalence - IgG,13,0,Percentage (%),13,72.7,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Trade / business based,Animal workers,NA,NA,NA,China,Foshan municipality; Guangdong Province,16,Nov-11,2002,30,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,392,Christian,3,Seroprevalence - IgG,1.2,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Community based,General population,NA,NA,NA,China,Foshan municipality; Guangdong Province,16,Nov-11,2002,30,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,14,SARS-CoV,NA,1,0 +TRUE,392,Christian,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Infection,Other,NA,Significant,Unspecified,Both,Community based,General population,0,86,662,China,Foshan municipality; Guangdong Province,16,Nov-11,2002,30,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,430,Paula,6,Human delay - time in care (length of stay),23.1,0,Days,5,78,Mean,11.9,Other,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,General population,20,75,78,China,Guangdong,22,Dec-12,2002,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,10,SARS-CoV,NA,1,0 +TRUE,430,Paula,6,Human delay - incubation period,2.4,0,Days,1,5,Mean,1.5,Other,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Hospital based,General population,20,75,17,China,Guangdong,22,Dec-12,2002,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,4,SARS-CoV,NA,1,0 +TRUE,430,Thom,1,Severity - case fatality rate (CFR),9,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,7,78,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,General population,20,75,78,China,Guangdong,22,Dec-12,2002,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,430,Thom,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Severe disease,Age;Cormobidity,Unspecified,Significant,Adjusted,Both,Hospital based,General population,20,75,78,China,Guangdong,22,Dec-12,2002,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,4,SARS-CoV,NA,1,0 +TRUE,461,Paula,7,Human delay - incubation period,5.9,0,Days,1,20,Mean,3.5,Standard deviation (Sd),NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,NA,NA,96,China,Guangzhou,30,Jan-01,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,461,Paula,7,Human delay - symptom onset>admission to care,2.9,0,Days,1,10,Mean,2,Standard deviation (Sd),NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,NA,NA,96,China,Guangzhou,30,Jan-01,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,461,Paula,7,Human delay - time in care (length of stay),17.2,0,Days,8,47,Mean,8,Standard deviation (Sd),NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,NA,NA,96,China,Guangzhou,30,Jan-01,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,461,Thom,3,Severity - case fatality rate (CFR),1,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,1,96,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,NA,NA,96,China,Guangzhou,30,Jan-01,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,487,Thom,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Probable case,Contact with animal;Other,Unspecified,Not significant,Not adjusted,Both,Population based,General population,14,84,375,China,Beijing,28,Apr-04,2003,4,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,9,SARS-CoV,NA,1,0 +TRUE,487,Thom,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Probable case,Cormobidity;Other,Unspecified,Significant,Not adjusted,Both,Population based,General population,14,84,375,China,Beijing,28,Apr-04,2003,4,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,10,SARS-CoV,NA,1,0 +TRUE,487,Thom,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Probable case,Contact with animal;Cormobidity;Other,Unspecified,Significant,Adjusted,Both,Population based,General population,14,84,375,China,Beijing,28,Apr-04,2003,4,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,487,Thom,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Probable case,Other,Unspecified,Not significant,Adjusted,Both,Population based,General population,14,84,375,China,Beijing,28,Apr-04,2003,4,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,487,Thom,4,Seroprevalence - IgG,26,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,8,31,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Population based,General population,14,84,31,China,Beijing,28,Apr-04,2003,28,Aug-08,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,513,Kanchan,5,Seroprevalence - IFA,0,0,Percentage (%),NA,NA,NA,NA,NA,0,0.6,CI95%,0,29,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,NA,Healthcare workers,NA,NA,29,Hong Kong SAR; China,NA,22,May-05,2003,31,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,20,SARS-CoV,NA,1,0 +TRUE,513,Kanchan,5,Seroprevalence - IgG,5.1,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,29,574,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,NA,Healthcare workers,25,79,574,Hong Kong SAR; China,NA,22,May-05,2003,31,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,19,SARS-CoV,NA,1,0 +TRUE,522,Kanchan,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Superspreading,Close contact;Other,Unspecified,Significant,Not adjusted,Both,Hospital based,General population,NA,NA,124,China;Hong Kong SAR; China,Guangzhou; Hong-Kong,NA,Sep-09,2004,NA,Nov-11,2005,NA,FALSE,NA,NA,FALSE,FALSE,27,SARS-CoV,NA,1,0 +TRUE,522,Kanchan,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Superspreading,Cormobidity;Other,Unspecified,Not significant,Not adjusted,Both,Hospital based,General population,NA,NA,124,China;Hong Kong SAR; China,Guangzhou; Hong-Kong,NA,Sep-09,2004,NA,Nov-11,2005,NA,FALSE,NA,NA,FALSE,FALSE,28,SARS-CoV,NA,1,0 +TRUE,522,Kanchan,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Region,TRUE,FALSE,Superspreading,Close contact;Other,Unspecified,Significant,Adjusted,Both,Hospital based,General population,NA,NA,124,China;Hong Kong SAR; China,Guangzhou; Hong-Kong,NA,Sep-09,2004,NA,Nov-11,2005,NA,FALSE,NA,NA,FALSE,FALSE,29,SARS-CoV,NA,1,0 +TRUE,522,Thom Rawson,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Region,TRUE,FALSE,Superspreading,Other,Unspecified,Not significant,Adjusted,Both,Hospital based,General population,NA,NA,124,China;Hong Kong SAR; China,Guangzhou; Hong-Kong,NA,Sep-09,2004,NA,Nov-11,2005,NA,FALSE,NA,NA,FALSE,FALSE,19,SARS-CoV,NA,1,0 +TRUE,523,Kanchan,8,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Other,NA,Significant,Adjusted,Male,Hospital based,Persons under investigation,19,90,74,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,34,SARS-CoV,NA,1,0 +TRUE,523,Kelly M,1,Attack rate,40.5,0,Percentage (%),18.2,65,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other;Time,TRUE,FALSE,NA,NA,NA,NA,NA,Male,Hospital based,Persons under investigation,19,90,74,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,523,Kelly M,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Age;Cormobidity;Other,NA,Not significant,Adjusted,Male,Hospital based,Persons under investigation,19,90,74,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,526,Kanchan,9,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Other,NA,Significant,Adjusted,NA,Community based,Persons under investigation,NA,NA,187,Hong Kong SAR; China,Amoy Gardens,21,Mar-03,2003,1,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,36,SARS-CoV,NA,1,0 +TRUE,526,Kanchan,9,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Other,NA,Not significant,Adjusted,NA,Community based,Persons under investigation,NA,NA,187,Hong Kong SAR; China,Amoy Gardens,21,Mar-03,2003,1,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,37,SARS-CoV,NA,1,0 +TRUE,558,Kelly M,6,Severity - case fatality rate (CFR),12.5,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,1,8,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,8,Taiwan; China,NA,1,May-05,2003,20,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,10,SARS-CoV,NA,1,0 +TRUE,558,Kelly M,6,Severity - case fatality rate (CFR),5,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,1,20,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,20,China,NA,27,Mar-03,2003,4,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,558,Kelly M,6,Severity - case fatality rate (CFR),12.5,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,1,8,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,8,Canada,NA,17,Mar-03,2003,4,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,9,SARS-CoV,NA,1,0 +TRUE,572,Bethan Cracknell Daniels,3,Severity - case fatality rate (CFR),17,0,Percentage (%),15,25,Unspecified,NA,NA,NA,NA,NA,299,1755,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Unspecified,NA,Time,TRUE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Persons under investigation,NA,NA,1755,Hong Kong SAR; China,Hong Kong,12,Mar-03,2003,31,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,572,Bethan Cracknell Daniels,3,Severity - case fatality rate (CFR),8,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,191,2521,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Unspecified,NA,Time,TRUE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Persons under investigation,NA,NA,2521,China,Beijing,21,Apr-04,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,613,Kelly M,4,Mutations – mutation rate,NA,0,Mutations/genome/generation (U),0.11,0.07,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Mixed settings,Persons under investigation,NA,NA,10,Taiwan; China,Taiwan,NA,Mar-03,2003,NA,Jun-06,2003,whole genome,TRUE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Seroprevalence - IgG,92.7,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,38,41,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,24,87,41,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Human delay - symptom onset>admission to care,3,0,Days,NA,NA,Median,NA,NA,1,12,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,`,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,16,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Severity - case fatality rate (CFR),78.6,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,11,14,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Unspecified,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Other,24,87,14,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,19,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Severity - case fatality rate (CFR),6.5,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,4,62,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Unspecified,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Other,24,87,62,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,20,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Human delay - symptom onset>death,12,0,Days,NA,NA,Median,NA,NA,4,42,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,24,87,15,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,21,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Age;Cormobidity;Other;Sex,NA,Significant,Not adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,23,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Other,NA,Not significant,Not adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,25,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Age;Other,NA,Significant,Adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,27,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Cormobidity;Other;Sex,NA,Not significant,Adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,26,SARS-CoV,NA,1,0 +TRUE,641,Bethan Cracknell Daniels,4,Human delay - other human delay (go to section),10.3,0,Days,NA,NA,Unspecified,5.1,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Hospital based,Persons under investigation,NA,NA,62,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,Symptom Onset/Fever,Symptom Resolution,FALSE,FALSE,28,SARS-CoV,NA,1,0 +TRUE,641,Patrick,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Age;Cormobidity;Other,NA,Significant,Not adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,641,Patrick,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Other;Sex,NA,Not significant,Not adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,641,Patrick,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Cormobidity;Other,NA,Significant,Adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,641,Patrick,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Age;Other;Sex,NA,Not significant,Adjusted,Both,Hospital based,Persons under investigation,24,87,76,Taiwan; China,NTUH; Northern Taiwan,8,Mar-03,2003,15,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,655,Bethan Cracknell Daniels,8,Human delay - latent period,5,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,Persons under investigation,NA,NA,NA,China,Beijing,19,Apr-04,2003,21,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,31,SARS-CoV,NA,1,0 +TRUE,655,Bethan Cracknell Daniels,8,Reproduction number (Basic R0),2.37,0,No units,NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,Compartmental model,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,Persons under investigation,NA,NA,NA,China,Beijing,19,Apr-04,2003,21,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,32,SARS-CoV,Human-human,1,0 +TRUE,655,Bethan Cracknell Daniels,8,Reproduction number (Effective; Re),0.1,0,No units,NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,End outbreak,NA,Compartmental model,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,Persons under investigation,NA,NA,NA,China,Beijing,19,Apr-04,2003,21,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,33,SARS-CoV,Human-human,1,0 +TRUE,655,Bethan Cracknell Daniels,8,Human delay - infectious period,4,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,Persons under investigation,NA,NA,NA,China,Beijing,19,Apr-04,2003,21,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,34,SARS-CoV,NA,1,0 +TRUE,672,Dominic Dee,1,Seroprevalence - IgG,0.41,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,9,2197,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,End outbreak,NA,NA,Occupation;Sex,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Healthcare workers,NA,NA,2197,Taiwan; China,NA,1,Jul-07,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,672,Dominic Dee,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,End outbreak,NA,NA,NA,FALSE,FALSE,Serology,Occupation,Healthcare,Significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,2197,Taiwan; China,NA,1,Jul-07,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,2,SARS-CoV,NA,1,0 +TRUE,672,Dominic Dee,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,End outbreak,NA,NA,NA,FALSE,FALSE,Serology,Occupation;Sex,Healthcare,Not significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,2197,Taiwan; China,NA,1,Jul-07,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,672,Patrick,4,Seroprevalence - IgG,1.02,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,9,882,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,End outbreak,NA,NA,Occupation;Sex,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Contact based,Healthcare workers,NA,NA,882,Taiwan; China,NA,1,Jul-07,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,17,SARS-CoV,NA,1,0 +TRUE,672,Patrick,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,End outbreak,NA,NA,NA,FALSE,FALSE,Serology,Occupation,Healthcare,Significant,Not adjusted,Both,Contact based,Healthcare workers,NA,NA,882,Taiwan; China,NA,1,Jul-07,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,18,SARS-CoV,NA,1,0 +TRUE,672,Patrick,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,End outbreak,NA,NA,NA,FALSE,FALSE,Serology,Occupation;Sex,Healthcare,Not significant,Not adjusted,Both,Contact based,Healthcare workers,NA,NA,882,Taiwan; China,NA,1,Jul-07,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,19,SARS-CoV,NA,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),3.6,0,No units,NA,NA,Mean,NA,NA,3.1,4.2,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Start outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Hong Kong SAR,NA,NA,Feb-02,2003,11,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,Human-human,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),2.4,0,No units,NA,NA,Mean,NA,NA,1.8,3.1,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Start outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Vietnam,NA,NA,Feb-02,2003,11,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,Human-human,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),3.1,0,No units,NA,NA,Mean,NA,NA,2.3,4,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Start outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Singapore,NA,NA,Feb-02,2003,11,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,Human-human,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),2.7,0,No units,NA,NA,Mean,NA,NA,1.8,3.6,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Start outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Canada,NA,NA,Feb-02,2003,11,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,Human-human,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),0.7,0,No units,NA,NA,Mean,NA,NA,0.7,0.8,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Hong Kong SAR,NA,12,Mar-03,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,Human-human,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),0.3,0,No units,NA,NA,Mean,NA,NA,0.1,0.7,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Vietnam,NA,12,Mar-03,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,Human-human,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),0.7,0,No units,NA,NA,Mean,NA,NA,0.6,0.9,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Singapore,NA,12,Mar-03,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,Human-human,1,0 +TRUE,706,Dominic Dee,3,Reproduction number (Effective; Re),1,0,No units,NA,NA,Mean,NA,NA,0.9,1.2,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Renewal equations / Branching process,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Canada,NA,12,Mar-03,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,Human-human,1,0 +TRUE,718,Dominic Dee,4,Severity - case fatality rate (CFR),9.7,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,6,62,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,20,76,62,Vietnam,Hanoi,26,Feb-02,2003,NA,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,718,Dominic Dee,4,Human delay - time in care (length of stay),24.5,0,Days,NA,NA,Mean,7.4,Standard deviation (Sd),NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,20,76,62,Vietnam,Hanoi,26,Feb-02,2003,NA,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,718,Tristan Naidoo,1,Human delay - other human delay (go to section),10.5,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,20,76,62,Vietnam,Hanoi,26,Feb-02,2003,NA,May-05,2003,NA,FALSE,Symptom Onset/Fever,Other: Intubation,FALSE,TRUE,6,SARS-CoV,NA,1,0 +TRUE,718,Tristan Naidoo,1,Human delay - other human delay (go to section),12.7,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,20,76,62,Vietnam,Hanoi,26,Feb-02,2003,NA,May-05,2003,NA,FALSE,Symptom Onset/Fever,Symptom Resolution,FALSE,TRUE,7,SARS-CoV,NA,1,0 +TRUE,718,Tristan Naidoo,1,Human delay - symptom onset>death,18.8,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,20,76,62,Vietnam,Hanoi,26,Feb-02,2003,NA,May-05,2003,NA,FALSE,NA,NA,FALSE,TRUE,3,SARS-CoV,NA,1,0 +TRUE,718,Tristan Naidoo,1,Human delay - symptom onset>admission to care,4.3,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,20,76,62,Vietnam,Hanoi,26,Feb-02,2003,NA,May-05,2003,NA,FALSE,NA,NA,FALSE,TRUE,4,SARS-CoV,NA,1,0 +TRUE,718,Tristan Naidoo,1,Seroprevalence - Unspecified,98.4,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,61,62,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,20,76,62,Vietnam,Hanoi,26,Feb-02,2003,NA,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,727,Dominic Dee,5,Seroprevalence - IFA,0,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,0,127,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Symptoms,TRUE,FALSE,NA,NA,NA,NA,NA,Unspecified,Travel based,Persons under investigation,NA,NA,127,United States,Seven US-bound aircraft. Follow ups across USA,23,Feb-02,2003,NA,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,745,Dominic Dee,6,Severity - case fatality rate (CFR),17,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,302,1755,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,Naive,NA,Age;Occupation;Sex,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,1755,Hong Kong SAR; China,Hong Kong,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,14,SARS-CoV,NA,1,0 +TRUE,745,Dominic Dee,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,Death,Sex;Age;Ocupation,Healthcare,Significant,Not adjusted,Both,Hospital based,Mixed groups,NA,NA,1755,Hong Kong SAR; China,Hong Kong,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,745,Tristan Naidoo,3,Human delay - incubation period,3.7,0,Days,NA,NA,Mean,NA,NA,2.6,5.8,CRI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,NA,NA,302,Hong Kong SAR; China,NA,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,745,Tristan Naidoo,3,Human delay - incubation period,4.8,0,Days,NA,NA,Mean,NA,NA,4.2,5.5,CRI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,NA,NA,1453,Hong Kong SAR; China,NA,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,745,Tristan Naidoo,3,Human delay - incubation period,4.7,0,Days,NA,NA,Mean,NA,NA,4.1,5.4,CRI95%,NA,NA,Normal-Log,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,234,Hong Kong SAR; China,NA,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,745,Tristan Naidoo,3,Human delay - incubation period,4.6,0,Days,NA,NA,Standard Deviation,NA,NA,3.6,6,CRI95%,NA,NA,Normal-Log,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,234,Hong Kong SAR; China,NA,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,14,SARS-CoV,NA,1,0 +TRUE,745,Tristan Naidoo,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,Death,Other,NA,Not significant,Not adjusted,Both,Hospital based,Mixed groups,NA,NA,1755,Hong Kong SAR; China,NA,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,745,Tristan Naidoo,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,Death,Other,NA,Significant,Adjusted,Both,Hospital based,Persons under investigation,NA,NA,308,Hong Kong SAR; China,NA,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,16,SARS-CoV,NA,1,0 +TRUE,745,Tristan Naidoo,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,FALSE,Death,Other,NA,Significant,Adjusted,Both,Hospital based,Persons under investigation,NA,NA,234,Hong Kong SAR; China,NA,NA,Feb-02,2003,NA,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,17,SARS-CoV,NA,1,0 +TRUE,793,Rob Johnson,2,Mutations – mutation rate,5.7,-6,Unspecified,NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Unspecified,NA,NA,6,Singapore,NA,NA,NA,NA,NA,NA,2003,whole genome,TRUE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,793,Rob Johnson,2,Mutations – mutation rate,4.3,-6,Unspecified,NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Unspecified,NA,NA,12,Singapore,NA,NA,NA,NA,NA,NA,2003,whole genome,TRUE,NA,NA,FALSE,FALSE,9,SARS-CoV,NA,1,0 +TRUE,804,Tristan Naidoo,5,Human delay - incubation period,5,0,Days,NA,NA,Mean,NA,NA,2,10,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Persons under investigation,NA,NA,42,Canada,Toronto,14,Mar-03,2003,15,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,21,SARS-CoV,NA,1,0 +TRUE,804,Rob Johnson,1,Attack rate,NA,0,Percentage (%),10.3,60,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Occupation,TRUE,TRUE,NA,NA,NA,NA,NA,Both,Hospital based,Healthcare workers,NA,NA,NA,Canada,Toronto,14,Mar-03,2003,15,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,804,Tristan Naidoo,5,Human delay - incubation period,4,0,Days,NA,NA,Median,NA,NA,2,10,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Persons under investigation,NA,NA,42,Canada,Toronto,14,Mar-03,2003,15,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,20,SARS-CoV,NA,1,0 +TRUE,804,Rob Johnson,1,Severity - case fatality rate (CFR),13.3,0,Percentage (%),0,90,Unspecified,NA,NA,NA,NA,NA,17,128,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,Age;Other,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,1.75,86,128,Canada,Toronto,14,Mar-03,2003,10,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,804,Rob Johnson,1,Human delay - symptom onset>death,24.8,0,Days,NA,NA,Mean,NA,NA,9,57,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,38,86,17,Canada,Toronto,14,Mar-03,2003,15,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,882,Rebecca,2,Secondary attack rate,10.2,0,Percentage (%),NA,NA,Unspecified,NA,NA,6.7,23.5,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Household based,Persons under investigation,1,79,176,Canada,Toronto; York Region; Ontario,25,May-05,2003,31,Oct-10,2003,NA,FALSE,NA,NA,FALSE,FALSE,4,SARS-CoV,NA,1,0 +TRUE,882,Rebecca,2,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Age;Occupation;Other;Close contact,Healthcare,Significant,Not adjusted,Both,Household based,Persons under investigation,1,79,176,Canada,Toronto; York Region; Ontario,25,May-05,2003,31,Oct-10,2003,NA,FALSE,NA,NA,FALSE,FALSE,2,SARS-CoV,NA,1,0 +TRUE,882,Rebecca,2,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Other;Close contact,NA,Significant,Adjusted,Both,Household based,Persons under investigation,1,79,176,Canada,Toronto; York Region; Ontario,25,May-05,2003,31,Oct-10,2003,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,882,Rob Johnson,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Age;Occupation,Healthcare,Not significant,Not adjusted,Both,Household based,Persons under investigation,1,79,176,Canada,Toronto; York Region; Ontario,25,May-05,2003,31,Oct-10,2003,NA,FALSE,NA,NA,FALSE,FALSE,28,SARS-CoV,NA,1,0 +TRUE,882,Rob Johnson,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Occupation;Other,Healthcare,Not significant,Adjusted,Both,Household based,Persons under investigation,1,79,176,Canada,Toronto; York Region; Ontario,25,May-05,2003,31,Oct-10,2003,NA,FALSE,NA,NA,FALSE,FALSE,29,SARS-CoV,NA,1,0 +TRUE,893,Rob Johnson,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Age;Non-household contact;Other;Sex,NA,Not significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,45,Singapore,Tan Tock Seng Hospital; Singapore,NA,Apr-04,2003,NA,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,21,SARS-CoV,NA,1,0 +TRUE,893,Rob Johnson,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Other,NA,Significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,45,Singapore,Tan Tock Seng Hospital; Singapore,NA,Apr-04,2003,NA,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,22,SARS-CoV,NA,1,0 +TRUE,893,Rob Johnson,6,Attack rate,57,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Healthcare workers,19,64,80,Singapore,Tan Tock Seng Hospital; Singapore,NA,Apr-04,2003,NA,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,23,SARS-CoV,NA,1,0 +TRUE,893,Ruth,1,Seroprevalence - Unspecified,56,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,45,80,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Symptoms,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Healthcare workers,19,64,80,Singapore,Tan Tock Seng Hospital; Singapore,NA,Apr-04,2003,NA,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,948,Rob Johnson,5,Severity - case fatality rate (CFR),15,0,Percentage (%),10,17,Unspecified,NA,NA,NA,NA,NA,17,111,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,Age;Occupation;Other;Sex,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,16,82,111,China,Pingjin Hospital; Tianjin; China,NA,NA,2003,6,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,948,Rob Johnson,5,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Age;Other,NA,Significant,Adjusted,Both,Hospital based,Mixed groups,NA,NA,82,China,Pingjin Hospital; Tianjin; China,NA,NA,2003,NA,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,948,Rob Johnson,5,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Cormobidity;Occupation;Other;Sex,NA,Not significant,Adjusted,Both,Hospital based,Mixed groups,NA,NA,82,China,Pingjin Hospital; Tianjin; China,NA,NA,2003,NA,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,14,SARS-CoV,NA,1,0 +TRUE,948,Rob Johnson,5,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Age;Cormobidity;Occupation;Other;Sex,Healthcare,Significant,Not adjusted,Both,Hospital based,Mixed groups,16,77,90,China,Pingjin Hospital; Tianjin; China,NA,NA,2003,NA,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,948,Rob Johnson,5,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Other,NA,Not significant,Not adjusted,Both,Hospital based,Mixed groups,16,77,90,China,Pingjin Hospital; Tianjin; China,NA,NA,2003,NA,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,16,SARS-CoV,NA,1,0 +TRUE,999,Rob Johnson,3,Mutations – mutation rate,NA,0,Percentage (%),0.079,3.333,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,TRUE,TRUE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Unspecified,NA,NA,33,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,999,Ruth,4,Mutations – mutation rate,0.636,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,188,29751,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Unspecified,NA,NA,33,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,999,Ruth,4,Mutations – mutation rate,0.121,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,36,29751,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Unspecified,NA,NA,33,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,9,SARS-CoV,NA,1,0 +TRUE,1023,Kieran Drake,7,Reproduction number (Basic R0),NA,0,No units,2.21,2.53,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,Compartmental model,Time,TRUE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Unspecified,NA,NA,NA,Canada,Greater Toronto Area,23,Feb-02,2003,11,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,29,SARS-CoV,Unspecified,1,0 +TRUE,1023,Kieran Drake,7,Reproduction number (Basic R0),1.88,0,No units,NA,NA,Unspecified,NA,NA,1.85,1.91,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,Compartmental model,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Unspecified,Unspecified,NA,NA,NA,Taiwan; China,NA,25,Feb-02,2003,25,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,30,SARS-CoV,Unspecified,1,0 +TRUE,1036,Kieran Drake,8,Reproduction number (Basic R0),NA,0,NA,1.0698,3.2524,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,Compartmental model,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,China,Beijing,27,Apr-04,2003,10,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,31,SARS-CoV,Unspecified,1,0 +TRUE,1046,Kieran Drake,5,Attack rate,9.7,0,Percentage (%),2.5,47.7,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,TRUE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Mixed groups,NA,NA,1140,China,Pingjin Hospital; Tianjin,16,Apr-04,2003,12,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,16,SARS-CoV,NA,1,0 +TRUE,1046,Kieran Drake,5,Human delay - latent period,4,0,Days,NA,NA,Median,NA,NA,2,11,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Mixed groups,16,82,111,China,Pingjin Hospital; Tianjin,16,Apr-04,2003,12,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,18,SARS-CoV,NA,1,0 +TRUE,1046,Richard Sheppard,4,Severity - case fatality rate (CFR),11.7,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,13,111,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Unspecified,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,16,82,111,China,Pingjin Hospital; Tianjin,16,Apr-04,2003,12,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,1090,Richard Sheppard,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Other,Other,NA,Significant,Unspecified,Both,Hospital based,Persons under investigation,NA,NA,8378,Singapore,NA,13,Mar-03,2003,31,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,1090,Richard Sheppard,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Infection,Other,NA,Significant,Unspecified,Both,Hospital based,Persons under investigation,NA,NA,1018,Singapore,NA,13,Mar-03,2003,31,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,1102,Richard Sheppard,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Infection,Age;Close contact;Cormobidity;Occupation;Other,Healthcare,Not significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,86,Singapore,Communicable Disease Centre in Tan Tock Seng Hospital; Singapore,1,Mar-03,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,1102,Richard Sheppard,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Infection,Other;Sex,Healthcare,Not significant,Adjusted,Both,Hospital based,Healthcare workers,NA,NA,86,Singapore,Communicable Disease Centre in Tan Tock Seng Hospital; Singapore,1,Mar-03,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,1102,Richard Sheppard,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Infection,Close contact;Other,Healthcare,Significant,Adjusted,Both,Hospital based,Healthcare workers,NA,NA,86,Singapore,Communicable Disease Centre in Tan Tock Seng Hospital; Singapore,1,Mar-03,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,1102,Richard Sheppard,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Infection,Close contact;Other,Healthcare,Significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,86,Singapore,Communicable Disease Centre in Tan Tock Seng Hospital; Singapore,1,Mar-03,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,4,SARS-CoV,NA,1,0 +TRUE,1153,Anna V,1,Attack rate,10,0,Percentage (%),0,12.5,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,Other,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,21,90,70,Singapore,Singapore General Hospital,5,Apr-04,2023,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,1153,Anna V,1,Severity - case fatality rate (CFR),14,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,1,7,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Mixed groups,37,74,7,Singapore,Singapore General Hospital,5,Apr-04,2023,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,1183,Anna V,4,Human delay - incubation period,4.09,0,Days,NA,NA,Mean,9.65,Variance,0,14,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Persons under investigation,NA,NA,16,Hong Kong SAR; China,Prince of Wales Hospital,NA,NA,2003,NA,NA,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,1217,Kieran Drake,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Occupation;Other;Time,FALSE,TRUE,Infection,Hospitalisation;Household contact;Non-household contact;Other;Social gathering,Healthcare;Unspecified,Significant,Unspecified,Unspecified,Hospital based,Unspecified,0.4,99,225,Canada,Toronto,23,Feb-02,2003,1,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,14,SARS-CoV,NA,1,0 +TRUE,1217,Kieran Drake,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Occupation;Other;Time,FALSE,TRUE,Infection,Hospitalisation;Household contact;Non-household contact;Other;Social gathering,Healthcare;Unspecified,Not significant,Unspecified,Unspecified,Hospital based,Unspecified,0.4,99,225,Canada,Toronto,23,Feb-02,2003,1,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,1217,Kieran Drake,3,Attack rate,1,0,Percentage (%),NA,NA,NA,NA,NA,0.8,1.1,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,23103,Canada,Toronto,23,Feb-02,2003,1,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,4,SARS-CoV,NA,1,0 +TRUE,1217,Kieran Drake,3,Seroprevalence - Unspecified,96.1,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,124,129,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Unspecified,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,Persons under investigation,NA,NA,129,Canada,Toronto,23,Feb-02,2003,1,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,1217,Rebecca,3,Human delay - other human delay (go to section),3.9,0,Days,NA,NA,Mean,3.2,Standard deviation (Sd),NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,0,99,225,Canada,Toronto,23,Feb-02,2003,1,Jul-07,2003,NA,FALSE,Symptom Onset/Fever,Reporting,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,1217,Rebecca,3,Human delay - incubation period,4.7,0,Days,NA,NA,Mean,NA,NA,1,12,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,59,Canada,Toronto,23,Feb-02,2003,1,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,1217,Rebecca,3,Severity - case fatality rate (CFR),16.9,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,38,225,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,0,99,225,Canada,Toronto,23,Feb-02,2003,1,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,1236,Kieran Drake,6,Seroprevalence - IgG,98.41,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,124,126,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,126,Hong Kong SAR; China,Prince of Wales Hospital; Shatin; NT,11,Mar-03,2003,25,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,21,SARS-CoV,NA,1,0 +TRUE,1236,Kieran Drake,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Age;Other;Sex,Unspecified,Not significant,Not adjusted,Both,Hospital based,Persons under investigation,NA,NA,138,Hong Kong SAR; China,Prince of Wales Hospital; Shatin; NT,11,Mar-03,2003,25,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,23,SARS-CoV,NA,1,0 +TRUE,1236,Kieran Drake,6,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Other,Other,Unspecified,Significant,Not adjusted,Both,Hospital based,Persons under investigation,NA,NA,138,Hong Kong SAR; China,Prince of Wales Hospital; Shatin; NT,11,Mar-03,2003,25,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,24,SARS-CoV,NA,1,0 +TRUE,1236,Rebecca,4,Human delay - symptom onset>admission to care,3,0,Days,NA,NA,Median,NA,NA,0,11,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,138,Hong Kong SAR; China,Prince of Wales Hospital; Shatin; NT,11,Mar-03,2003,25,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,10,SARS-CoV,NA,1,0 +TRUE,1236,Rebecca,4,Severity - case fatality rate (CFR),10.9,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,15,138,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,138,Hong Kong SAR; China,Prince of Wales Hospital; Shatin; NT,11,Mar-03,2003,28,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,1365,Anna V,5,secondary attack rate,4.2,0,Percentage (%),NA,NA,NA,NA,NA,1.5,7,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Contact based,Mixed groups,0,82,212,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,9,SARS-CoV,NA,1,0 +TRUE,1365,Anna V,5,Attack rate,6.7,0,Percentage (%),NA,NA,NA,NA,NA,0.6,14,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Contact based,Mixed groups,0,82,212,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,1365,Joseph,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Age;Close contact;Cormobidity;Household contact;Non-household contact;Other;Sex,NA,Not significant,Not adjusted,Both,Contact based,Mixed groups,0,82,212,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,1365,Joseph,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Household contact;Other,NA,Significant,Adjusted,Both,Contact based,Mixed groups,0,82,212,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,1365,Joseph,1,Reproduction number (Basic R0),0.2,0,No units,NA,NA,Other,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,Empirical (contact tracing),NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Contact based,Mixed groups,0,82,212,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,Human-human,1,0 +TRUE,1365,Joseph,1,Human delay - symptom onset>admission to care,4,0,Days,NA,NA,Mean,NA,NA,0,13,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,45,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,4,SARS-CoV,NA,1,0 +TRUE,1365,Joseph,1,Human delay - symptom onset>admission to care,6,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Contact based,Persons under investigation,NA,NA,3,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,1365,Joseph,1,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Household contact;Other,NA,Significant,Not adjusted,Both,Contact based,Mixed groups,0,82,212,Vietnam,national,26,Feb-02,2003,28,Apr-04,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,1396,Joseph,2,Severity - case fatality rate (CFR),10.1,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,22,218,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,Adjusted,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,218,Hong Kong SAR; China,Princess Margaret Hospital,26,Feb-02,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,1396,Joseph,2,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Death,Age;Cormobidity;Other;Sex,NA,Significant,Not adjusted,Both,Hospital based,Persons under investigation,NA,NA,218,Hong Kong SAR; China,Princess Margaret Hospital,26,Feb-02,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,1396,Joseph,2,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Death,Cormobidity;Other,NA,Significant,Adjusted,Both,Hospital based,Persons under investigation,NA,NA,218,Hong Kong SAR; China,Princess Margaret Hospital,26,Feb-02,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,14,SARS-CoV,NA,1,0 +TRUE,1396,Joseph,2,Human delay - other human delay (go to section),4.4,0,Days,NA,NA,Mean,2.3,Standard deviation (Sd),NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,218,Hong Kong SAR; China,Princess Margaret Hospital,26,Feb-02,2003,31,Mar-03,2003,NA,FALSE,Symptom Onset/Fever,Other: Sample collection,FALSE,FALSE,9,SARS-CoV,NA,1,0 +TRUE,1396,Joseph,2,Seroprevalence - IFA,85.1,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,74,87,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Other,NA,NA,87,Hong Kong SAR; China,Princess Margaret Hospital,26,Feb-02,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,10,SARS-CoV,NA,1,0 +TRUE,1396,Joseph,2,Seroprevalence - IFA,75.8,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,25,33,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Other,NA,NA,33,Hong Kong SAR; China,Princess Margaret Hospital,26,Feb-02,2003,31,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,1401,Joseph,3,Human delay - incubation period,NA,0,Days,1,11,Other,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,FALSE,TRUE,NA,NA,NA,NA,NA,Both,Contact based,Persons under investigation,35,72,10,Hong Kong SAR; China,"Queen Mary Hospital; Kwong Wah Hospital; +and Pamela Youde Nethersole Eastern Hospital",22,Feb-02,2003,22,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,1401,Tristan Naidoo,6,Human delay - other human delay (go to section),5,0,Days,NA,NA,Median,NA,NA,3,7,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Contact based,Persons under investigation,35,72,10,Hong Kong SAR; China,"Queen Mary Hospital; Kwong Wah Hospital; +and Pamela Youde Nethersole Eastern Hospital",22,Feb-02,2003,22,Mar-03,2002,NA,FALSE,Symptom Onset/Fever,Other: dyspnea,FALSE,FALSE,26,SARS-CoV,NA,1,0 +TRUE,1401,Tristan Naidoo,6,Human delay - symptom onset>death,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,FALSE,TRUE,NA,NA,NA,NA,NA,Male,Contact based,Persons under investigation,53,64,2,Hong Kong SAR; China,NA,22,Feb-02,2003,22,Mar-03,2002,NA,FALSE,NA,NA,FALSE,TRUE,27,SARS-CoV,NA,1,0 +TRUE,1401,Tristan Naidoo,6,Human delay - other human delay (go to section),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,FALSE,TRUE,NA,NA,NA,NA,NA,Both,Contact based,Persons under investigation,35,72,10,Hong Kong SAR; China,NA,22,Feb-02,2003,22,Mar-03,2002,NA,FALSE,Symptom Onset/Fever,Other: treatment,FALSE,TRUE,28,SARS-CoV,NA,1,0 +TRUE,1408,Anna Vi,7,Seroprevalence - IgG,12.04,0,Percentage (%),0,33.17,Unspecified,NA,NA,10.11,14.18,CI95%,124,1030,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,Age,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Community based,General population,NA,NA,2854,Taiwan; China,San-Ming district of Kaoohsiung City,NA,Aug-08,2003,NA,Dec-12,2003,NA,FALSE,NA,NA,FALSE,FALSE,18,SARS-CoV,NA,1,0 +TRUE,1408,Anna Vi,7,Seroprevalence - IFA,0.19,0,Percentage (%),0,0.49,Unspecified,NA,NA,0.02,0.7,CI95%,2,1030,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,Age,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Community based,General population,NA,NA,2854,Taiwan; China,San-Ming district of Kaoohsiung City,NA,Aug-08,2003,NA,Dec-12,2003,NA,FALSE,NA,NA,FALSE,FALSE,19,SARS-CoV,NA,1,0 +TRUE,1510,Ettie Unwin,1,Seroprevalence - IgG,0.14,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,,1453,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Mixed settings,General population,NA,NA,1453,China;Hong Kong SAR; China,Guangdong Province; Hongkong; Macao; Peking; Guangzhou,NA,Jun-06,2003,NA,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,1510,Sequoia,3,Seroprevalence - IgG,NA,0,Percentage (%),39.8,100,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Time,FALSE,TRUE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Other,NA,NA,NA,China,Guangzhou and Peking,NA,Feb-02,2003,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,2,SARS-CoV,NA,1,0 +TRUE,1510,Sequoia,3,Seroprevalence - IFA,NA,0,Percentage (%),34.8,100,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Time,FALSE,TRUE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Other,NA,NA,NA,China,Guangzhou and Peking,NA,Feb-02,2003,NA,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,1530,Ettie Unwin,2,Secondary attack rate,NA,0,Percentage (%),18.5,66.7,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Other,TRUE,TRUE,NA,NA,NA,NA,NA,Unspecified,Contact based,Persons under investigation,NA,NA,77,China,Beijing,5,Feb-02,2003,4,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,5,SARS-CoV,NA,1,0 +TRUE,1530,Ettie Unwin,2,Severity - case fatality rate (CFR),20,0,Percentage (%),18,23,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,Unspecified,NA,Disease generation,TRUE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Persons under investigation,NA,NA,77,China,Beijing,5,Feb-02,2003,4,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,1530,Tristan Naidoo,7,Human delay - incubation period,5.7,0,Days,NA,NA,Mean,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,Close contact,NA,NA,NA,Unspecified,Hospital based,Persons under investigation,NA,NA,33,China,Beijing,5,Feb-02,2003,4,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,35,SARS-CoV,NA,1,0 +TRUE,1530,Tristan Naidoo,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Disease generation,FALSE,FALSE,Death,Age;Other,NA,Significant,Not adjusted,Unspecified,Hospital based,Persons under investigation,NA,NA,77,China,Beijing,5,Feb-02,2003,4,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,32,SARS-CoV,NA,1,0 +TRUE,1530,Tristan Naidoo,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Superspreading,Sex,NA,Not significant,Not adjusted,Unspecified,Hospital based,Persons under investigation,NA,NA,77,China,Beijing,5,Feb-02,2003,4,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,33,SARS-CoV,NA,1,0 +TRUE,1530,Tristan Naidoo,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Superspreading,Close contact,NA,Significant,Not adjusted,Unspecified,Hospital based,Persons under investigation,NA,NA,77,China,Beijing,5,Feb-02,2003,4,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,34,SARS-CoV,NA,1,0 +TRUE,1582,Dominic Dee,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Infection,Other,NA,Significant,Adjusted,Both,Hospital based,Healthcare workers,NA,NA,254,Hong Kong SAR; China,Hong Kong hospitals,15,Mar-03,2003,24,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,22,SARS-CoV,NA,1,0 +TRUE,1641,Dominic Dee,8,Severity - case fatality rate (CFR),0,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,0,398,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Population based,Persons under investigation,0,91,398,United States,United States (national surveillance),17,Mar-03,2003,30,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,25,SARS-CoV,NA,1,0 +TRUE,1641,Sangeeta,3,Human delay - time in care (length of stay),3,0,Days,NA,NA,Median,NA,NA,1,14,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,Persons under investigation,NA,NA,90,United States,United States (national surveillance),17,Mar-03,2003,30,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,2,SARS-CoV,NA,1,0 +TRUE,1672,Thom Rawson,9,Reproduction number (Basic R0),4.587436164,0,No units,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Start outbreak,NA,Next generation matrix,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Population based,General population,NA,NA,NA,Canada;Hong Kong SAR; China,Toronto; Hong Kong,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,28,SARS-CoV,Human-human,1,0 +TRUE,1778,Anne,1,Mutations – mutation rate,8,-6,Unspecified,NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,China,Guangzhou,NA,NA,2003,NA,NA,2004,Synonymous; multiple sites comprising 91.25% of whole genome,TRUE,NA,NA,FALSE,FALSE,1,SARS-CoV,NA,1,0 +TRUE,1929,Christian,10,Reproduction number (Basic R0),2.7,0,No units,2.7,3.4,Unspecified,NA,NA,2.2,3.7,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Start outbreak,NA,Compartmental model,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Mixed settings,Mixed groups,NA,NA,1512,Hong Kong SAR; China,Hong Kong,3,Mar-03,2003,6,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,41,SARS-CoV,Human-human,1,0 +TRUE,1929,Christian,10,Reproduction number (Effective; Re),NA,0,No units,0.14,1,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,Time,TRUE,TRUE,NA,NA,NA,NA,NA,NA,Mixed settings,Mixed groups,NA,NA,1512,Hong Kong SAR; China,Hong Kong,3,Mar-03,2003,6,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,42,SARS-CoV,NA,1,0 +TRUE,1929,Christian,10,beta - per capita contact rate per unit of time,0.062,0,Unspecified,NA,NA,Other,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,TRUE,Start outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Mixed settings,Mixed groups,NA,NA,1512,Hong Kong SAR; China,Hong Kong,3,Mar-03,2003,6,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,43,SARS-CoV,NA,1,0 +TRUE,1929,Anne,3,Human delay - generation time,10.6,0,Days,NA,NA,Other,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Mixed settings,Mixed groups,NA,NA,1512,Hong Kong SAR; China,Hong Kong,3,Mar-03,2003,6,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,6,SARS-CoV,NA,1,0 +TRUE,1929,Christian,10,Overdispersion,331,0,Max. nr. of cases superspreading (related to case),NA,NA,Other,NA,NA,295,331,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,TRUE,Mid outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,Housing estate based,NA,NA,NA,331,Hong Kong SAR; China,Amoy Gardens,18,Mar-03,2003,20,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,46,SARS-CoV,NA,1,0 +TRUE,1929,Anne,3,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,NA,NA,NA,NA,FALSE,FALSE,Other,Hospitalisation,NA,Unspecified,Unspecified,NA,Mixed settings,Mixed groups,NA,NA,1512,Hong Kong SAR; China,Hong Kong,3,Mar-03,2003,6,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,3,SARS-CoV,NA,1,0 +TRUE,1954,Anne,4,Secondary attack rate,18.7,0,Percentage (%),0,100,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,Occupation,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Healthcare workers,NA,NA,193,Vietnam,Hanoi,26,Feb-02,2003,24,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,8,SARS-CoV,NA,1,0 +TRUE,1954,Christian,9,Severity - case fatality rate (CFR),NA,0,Percentage (%),22,50,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,Unspecified,NA,Occupation,FALSE,TRUE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Healthcare workers,NA,NA,NA,Vietnam,Hanoi,26,Feb-02,2003,24,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,37,SARS-CoV,NA,1,0 +TRUE,1954,Christian,9,Seroprevalence - Unspecified,29,0,Percentage (%),0,100,Unspecified,NA,NA,NA,NA,NA,36,124,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,Occupation,TRUE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Healthcare workers,NA,NA,124,Vietnam,Hanoi,26,Feb-02,2003,24,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,38,SARS-CoV,NA,1,0 +TRUE,1954,Anne,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Close contact;Occupation,Healthcare;Other,Not significant,Not adjusted,Unspecified,Hospital based,Healthcare workers,NA,NA,193,Vietnam,Hanoi,26,Feb-02,2003,24,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,1954,Christian,9,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Close contact;Occupation;Other,Healthcare;Other,Significant,Not adjusted,Unspecified,Hospital based,Healthcare workers,NA,NA,193,Vietnam,Hanoi,26,Feb-02,2003,24,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,40,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Attack rate,0.7,0,Percentage (%),NA,NA,NA,NA,NA,0.54,0.9,CI95%,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,20,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Attack rate,NA,0,Percentage (%),0.42,1.68,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Age,TRUE,TRUE,NA,NA,NA,NA,NA,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,21,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Attack rate,NA,0,Percentage (%),0,10.36,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Level of exposure,TRUE,TRUE,NA,NA,NA,NA,NA,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,22,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Attack rate,NA,0,Percentage (%),0.68,0.72,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Sex,TRUE,TRUE,NA,NA,NA,NA,NA,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,23,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Attack rate,NA,0,Percentage (%),0.39,2.45,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Other,TRUE,TRUE,NA,NA,NA,NA,NA,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,24,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Attack rate,NA,0,Percentage (%),0.25,0.88,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,Time,TRUE,TRUE,NA,NA,NA,NA,NA,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,25,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Close contact;Other,NA,Significant,Adjusted,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,26,SARS-CoV,NA,1,0 +TRUE,2006,Christian,5,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Age;Sex,NA,Not significant,Adjusted,Both,Population based,General population,NA,NA,8662,Canada,Greater Toronto Area,23,Feb-02,2003,2,Jul-07,2003,NA,FALSE,NA,NA,FALSE,FALSE,27,SARS-CoV,NA,1,0 +TRUE,2043,Thom Rawson,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Other,Healthcare,Significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,624,Canada,Toronto,5,Mar-03,2003,12,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,20,SARS-CoV,NA,1,0 +TRUE,2043,Thom Rawson,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Age;Cormobidity;Other;Sex,Healthcare,Not significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,624,Canada,Toronto,5,Mar-03,2003,12,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,21,SARS-CoV,NA,1,0 +TRUE,2043,Thom Rawson,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,NA,FALSE,FALSE,Infection,Other,Healthcare,Significant,Adjusted,Both,Hospital based,Healthcare workers,NA,NA,624,Canada,Toronto,5,Mar-03,2003,12,Jun-06,2003,NA,FALSE,NA,NA,FALSE,FALSE,22,SARS-CoV,NA,1,0 +TRUE,2083,Anne,5,Seroprevalence - IgG,NA,0,Percentage (%),3,6.1,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,Age;Occupation;Symptoms,FALSE,TRUE,NA,NA,NA,NA,NA,Unspecified,Mixed settings,Mixed groups,NA,NA,NA,Hong Kong SAR; China,NA,NA,Mar-03,2003,NA,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,14,SARS-CoV,NA,1,0 +TRUE,2083,Anne,5,Seroprevalence - Unspecified,0.8,0,Percentage (%),NA,NA,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Mid outbreak,NA,NA,Age;Occupation;Symptoms,FALSE,TRUE,NA,NA,NA,NA,NA,Unspecified,Mixed settings,Mixed groups,NA,NA,NA,Hong Kong SAR; China,NA,NA,Mar-03,2003,NA,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,15,SARS-CoV,NA,1,0 +TRUE,2089,Anne,6,Severity - case fatality rate (CFR),15,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,8,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,NA,Taiwan; China,Taipei Veterans General Hospital,26,Mar-03,2003,25,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,16,SARS-CoV,NA,1,0 +TRUE,2089,Anne,6,Severity - case fatality rate (CFR),47,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,8,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,Naive,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,NA,Taiwan; China,Taipei Veterans General Hospital,26,Mar-03,2003,25,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,17,SARS-CoV,NA,1,0 +TRUE,2089,Christian,4,Human delay - admission to care>death,6,0,Days,1,30,Median,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,29,76,8,Taiwan; China,Taipei Veterans General Hospital,26,Mar-03,2003,25,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,17,SARS-CoV,NA,1,0 +TRUE,2089,Christian,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Death,Cormobidity;Other,NA,Unspecified,Unspecified,Both,Hospital based,Persons under investigation,29,76,8,Taiwan; China,Taipei Veterans General Hospital,26,Mar-03,2003,25,May-05,2003,NA,FALSE,NA,NA,FALSE,FALSE,19,SARS-CoV,NA,1,0 +TRUE,2099,Ettie Unwin,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,NA,FALSE,FALSE,Infection,Other,NA,Not significant,Not adjusted,Both,Hospital based,Healthcare workers,NA,NA,47,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,12,SARS-CoV,NA,1,0 +TRUE,2099,Ettie Unwin,4,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,NA,FALSE,FALSE,Infection,Close contact,NA,Significant,Unspecified,Both,Hospital based,Healthcare workers,NA,NA,47,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,11,SARS-CoV,NA,1,0 +TRUE,2099,Ettie Unwin,4,Attack rate,23.1,0,Percentage (%),5,37,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,Other,TRUE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Healthcare workers,NA,NA,65,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,13,SARS-CoV,NA,1,0 +TRUE,2099,Sequoia,5,Seroprevalence - IgG,100,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Both,Hospital based,Persons under investigation,NA,NA,15,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,7,SARS-CoV,NA,1,0 +TRUE,2099,Ettie Unwin,4,Human delay - incubation period,3,0,Days,NA,NA,Median,NA,NA,2,6,Range,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Post outbreak,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Persons under investigation,NA,NA,11,Hong Kong SAR; China,Prince of Wales Hospital,4,Mar-03,2003,10,Mar-03,2003,NA,FALSE,NA,NA,FALSE,FALSE,10,SARS-CoV,NA,1,0 +TRUE,2142,Christian,7,Secondary attack rate,19,0,Percentage (%),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Healthcare workers,NA,NA,31,Canada,Greater Toronto Area,23,Mar-03,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,28,SARS-CoV,NA,1,0 +TRUE,2142,Christian,7,Human delay - admission to care>discharge/recovery,19.5,0,Days,13,25,Median,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Healthcare workers,NA,NA,7,Canada,Greater Toronto Area,23,Mar-03,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,29,SARS-CoV,NA,1,0 +TRUE,2142,Christian,7,Human delay - incubation period,5,0,Days,3,8,Median,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,NA,NA,NA,NA,NA,Unspecified,Hospital based,Healthcare workers,NA,NA,7,Canada,Greater Toronto Area,23,Mar-03,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,30,SARS-CoV,NA,1,0 +TRUE,2142,Christian,7,Risk factors,NA,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,NA,NA,FALSE,FALSE,Infection,Close contact;Occupation,Healthcare,Significant,Unspecified,Unspecified,Hospital based,Healthcare workers,NA,NA,31,Canada,Greater Toronto Area,23,Mar-03,2003,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,31,SARS-CoV,NA,1,0 +TRUE,4837,Anna V,10,Reproduction number (Basic R0),NA,0,NA,2,4,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,Other,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Hong Kong SAR; China,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,22,SARS-CoV,NA,1,0 +TRUE,4837,Anna V,10,Human delay - infectious period,NA,0,Days,5,10,Unspecified,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,Unspecified,NA,Other,NA,FALSE,FALSE,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,Hong Kong SAR; China,NA,NA,NA,NA,NA,NA,NA,NA,FALSE,NA,NA,FALSE,FALSE,23,SARS-CoV,NA,1,0 diff --git a/src/db_compilation/sars_qa_fixing.csv b/src/db_compilation/sars_qa_fixing.csv new file mode 100644 index 00000000..384fd988 --- /dev/null +++ b/src/db_compilation/sars_qa_fixing.csv @@ -0,0 +1,56 @@ +fixed,Covidence_ID,Name_data_entry,QA_M1,QA_M2,QA_A3,QA_A4,QA_D5,QA_D6,QA_D7,num_rows,matching +TRUE,19,Christian,Yes,No,Yes,Yes,Yes,No,No,1,0 +TRUE,60,Paula,Yes,Yes,Yes,Yes,Yes,Yes,Yes,1,0 +TRUE,180,Christian,No,No,NA,NA,Yes,No,No,1,0 +TRUE,392,Kanchan,Yes,No,NA,NA,Yes,Yes,Yes,1,0 +TRUE,430,Paula,No,Yes,NA,NA,Yes,No,No,1,0 +TRUE,435,Thom,No,No,No,No,No,Yes,No,1,0 +TRUE,461,Paula,Yes,Yes,NA,NA,Yes,No,No,1,0 +TRUE,487,Bethan Cracknell Daniels,Yes,Yes,Yes,Yes,Yes,Yes,No,1,0 +TRUE,513,Kanchan,Yes,Yes,NA,Yes,Yes,Yes,No,1,0 +TRUE,522,Kanchan,Yes,Yes,Yes,Yes,No,Yes,No,1,0 +TRUE,523,Kelly M,Yes,Yes,NA,NA,Yes,No,No,1,0 +TRUE,526,Kanchan,No,Yes,Yes,Yes,Yes,No,No,1,0 +TRUE,558,Kelly M,No,No,No,No,No,No,No,1,0 +TRUE,572,Bethan Cracknell Daniels,Yes,Yes,No,No,No,No,No,1,0 +TRUE,613,Kelly M,No,No,No,No,Yes,No,No,1,0 +TRUE,641,Bethan Cracknell Daniels,Yes,Yes,NA,NA,Yes,No,No,1,0 +TRUE,646,Patrick,No,No,No,No,NA,NA,NA,1,0 +TRUE,655,Bethan Cracknell Daniels,No,No,No,No,Yes,No,No,1,0 +TRUE,672,Dominic Dee,Yes,Yes,NA,NA,No,No,No,1,0 +TRUE,696,Dominic Dee,No,No,No,No,NA,NA,NA,1,0 +TRUE,706,Dominic Dee,Yes,Yes,Yes,Yes,Yes,Yes,Yes,1,0 +TRUE,718,Dominic Dee,Yes,No,No,No,Yes,Yes,No,1,0 +TRUE,727,Dominic Dee,Yes,No,NA,NA,No,Yes,No,1,0 +TRUE,745,Dominic Dee,Yes,Yes,Yes,No,Yes,Yes,Yes,1,0 +TRUE,793,Rob Johnson,Yes,No,Yes,No,No,Yes,No,1,0 +TRUE,804,Rob Johnson,Yes,Yes,No,No,Yes,Yes,No,1,0 +TRUE,882,Rob Johnson,No,Yes,No,Yes,Yes,Yes,No,1,0 +TRUE,893,Rob Johnson,No,No,Yes,No,Yes,Yes,No,1,0 +TRUE,948,Rob Johnson,Yes,No,Yes,Yes,Yes,No,No,1,0 +TRUE,999,Rob Johnson,No,No,No,No,Yes,No,No,1,0 +TRUE,1036,Kieran Drake,Yes,Yes,Yes,Yes,Yes,Yes,Yes,1,0 +TRUE,1046,Kieran Drake,Yes,No,No,No,No,Yes,No,1,0 +TRUE,1090,Kieran Drake,No,No,No,No,Yes,No,No,1,0 +TRUE,1102,Kieran Drake,Yes,Yes,Yes,Yes,Yes,Yes,No,1,0 +TRUE,1153,Anna V,Yes,No,Yes,No,Yes,No,No,1,0 +TRUE,1183,Anna V,Yes,No,Yes,Yes,Yes,No,No,1,0 +TRUE,1217,Rebecca,Yes,Yes,Yes,Yes,No,Yes,No,1,0 +TRUE,1236,Rebecca,Yes,Yes,Yes,No,Yes,Yes,No,1,0 +TRUE,1365,Joseph,Yes,No,Yes,Yes,No,No,No,1,0 +TRUE,1396,Joseph,Yes,Yes,Yes,Yes,Yes,Yes,Yes,1,0 +TRUE,1401,Joseph,Yes,Yes,NA,NA,Yes,Yes,Yes,1,0 +TRUE,1408,Joseph,Yes,No,NA,NA,Yes,Yes,No,1,0 +TRUE,1510,Ettie Unwin,Yes,Yes,NA,NA,No,No,No,1,0 +TRUE,1530,Tristan Naidoo,Yes,Yes,Yes,No,No,Yes,No,1,0 +TRUE,1582,Dominic Dee,No,Yes,Yes,Yes,Yes,No,No,1,0 +TRUE,1641,Sangeeta,Yes,Yes,Yes,Yes,Yes,Yes,No,1,0 +TRUE,1672,Sangeeta,Yes,Yes,Yes,No,NA,NA,NA,1,0 +TRUE,1778,Anne,Yes,No,Yes,No,No,No,No,1,0 +TRUE,1954,Anne,Yes,No,Yes,Yes,Yes,Yes,No,1,0 +TRUE,2006,Sangeeta,Yes,Yes,Yes,Yes,Yes,Yes,No,1,0 +TRUE,2083,Anne,Yes,Yes,NA,NA,Yes,Yes,Yes,1,0 +TRUE,2089,Christian,Yes,No,NA,NA,Yes,No,No,1,0 +TRUE,2099,Ettie Unwin,Yes,Yes,Yes,Yes,Yes,Yes,No,1,0 +TRUE,2142,Christian,Yes,Yes,NA,NA,Yes,Yes,No,1,0 +TRUE,4837,Ettie Unwin,Yes,Yes,Yes,Yes,No,No,No,1,0 diff --git a/src/db_double/orderly.R b/src/db_double/orderly.R index 24f2edea..6c38f0b6 100644 --- a/src/db_double/orderly.R +++ b/src/db_double/orderly.R @@ -131,7 +131,7 @@ write_csv(model_match, "models_matching.csv") write_csv(model_discordant, "models_fixing.csv") # Empty outbreaks for Ebola - amend this for other pathogens -if (pathogen == 'EBOLA') { +if (pathogen %in% c('EBOLA','SARS')) { file.create("outbreaks_matching.csv") file.create("outbreaks_fixing.csv") } diff --git a/src/db_extraction/orderly.R b/src/db_extraction/orderly.R index 6e148b3f..373b7135 100644 --- a/src/db_extraction/orderly.R +++ b/src/db_extraction/orderly.R @@ -343,6 +343,7 @@ if (pathogen == "EBOLA") { #missing parameter types params$Parameter_type[params$Covidence_ID==2684¶ms$access_param_id==37] <- 'Risk factors' +} # Pathogen-specific cleaning articles <- clean_articles(articles) @@ -420,4 +421,4 @@ write_csv( write_csv( double_o, "double_extraction_outbreaks.csv" -) \ No newline at end of file +) diff --git a/src/sars_delays/orderly.R b/src/sars_delays/orderly.R new file mode 100644 index 00000000..27212e3c --- /dev/null +++ b/src/sars_delays/orderly.R @@ -0,0 +1,73 @@ +#task to create sars latex tables + +library(tidyverse) +library(stringr) +library(metafor) +library(meta) +library(estmeansd) +library(mixdist) +library(ggplot2) +library(ggsci) +library(sf) +library(ragg) +library(ggspatial) +library(ggforce) +library(png) +library(grid) +library(patchwork) +library(gridExtra) + +#orderly preparation +orderly_strict_mode() +orderly_parameters(pathogen = NULL) +orderly_dependency("db_compilation", "latest(parameter:pathogen == this:pathogen)", + c("articles.csv", "models.csv", "parameters.csv")) +orderly_shared_resource("lassa_functions.R" = "lassa_functions.R") +source("lassa_functions.R") +orderly_artefact("sars-specific tables",c("figure_delays.png")) + +################### +## DATA CURATION ## +################### + +articles <- read_csv("articles.csv") +models <- read_csv("models.csv") +parameters <- read_csv("parameters.csv") + +#dfs <- curation(articles,tibble(),models,parameters, adjust_for_exponents = FALSE ) +dfs <- data_curation(articles,tibble(),models,parameters, plotting = FALSE ) + +articles <- dfs$articles +models <- dfs$models +parameters <- dfs$parameters + + +# sample sd is the same as sd, so need to make this clear somehow / change param field to work for meta analysis + +d1 <- parameters %>% filter(parameter_type == 'Human delay - incubation period') +d2 <- parameters %>% filter(parameter_type == 'Human delay - symptom onset>admission to care') +d3 <- parameters %>% filter(parameter_type == 'Human delay - time in care (length of stay)' | + parameter_type == 'Human delay - admission to care>discharge/recovery' | + parameter_type == 'Human delay - admission to care>death') +d4 <- parameters %>% filter(parameter_type == 'Human delay - symptom onset>discharge/recovery' | + parameter_type == 'Human delay - symptom onset>death') +d5 <- parameters %>% filter(parameter_type == 'Human delay - serial interval') + +p1 <- forest_plot(d1,'Incubation Period (days)',"parameter_type",c(0,30)) + +#d1$article_label <- d1$refs +#p1_epireview <- epireview::forest_plot_incubation_period(d1,30,FALSE) + +p2 <- forest_plot(d5,'Serial Interval (days)',"parameter_type",c(0,20)) + + + +#meta-analysis of onset-admission delay +set.seed(42) +m5 <- metamean_wrap(dataframe = d2, estmeansd_method = "Cai", + plot_study = TRUE, digits = 2, lims = c(0,6), colour = "dodgerblue3", label = "Mean Onset-Admission Delay (days)", + width = 9500, height = 4500, resolution = 1000) + +#p5 <- m5$plot + +ggsave("figure_delays.png", plot = m5$plot, width = 12, height = 16) diff --git a/src/sars_latex_tables/orderly.R b/src/sars_latex_tables/orderly.R new file mode 100644 index 00000000..1fa320a4 --- /dev/null +++ b/src/sars_latex_tables/orderly.R @@ -0,0 +1,323 @@ +#task to create sars latex tables + +library(dplyr) +library(stringr) +library(tibble) +library(purrr) + +#orderly preparation +orderly_strict_mode() +orderly_parameters(pathogen = NULL) +orderly_dependency("db_compilation", "latest(parameter:pathogen == this:pathogen)", + c("articles.csv", "models.csv", "parameters.csv")) +orderly_shared_resource("data_curation.R" = "data_curation.R") +source("data_curation.R") +orderly_artefact("sars-specific tables",c("latex_models.csv", + "latex_transmission.csv","latex_delays.csv", + "latex_severity.csv","latex_seroprevalence.csv", + "latex_riskfactors.csv")) + +################### +## DATA CURATION ## +################### + +articles <- read_csv("articles.csv") +models <- read_csv("models.csv") +parameters <- read_csv("parameters.csv") + +dfs <- data_curation(articles,tibble(),models,parameters, plotting = FALSE ) + +articles <- dfs$articles +models <- dfs$models +parameters <- dfs$parameters + +#models +mods <- models %>% + filter(!is.na(model_type) & !is.na(covidence_id) | (!(is.na(model_type)&is.na(refs))) | !is.na(refs)) %>% + select(model_type,stoch_deter,transmission_route,assumptions, + compartmental_type,theoretical_model,interventions_type,refs,covidence_id) +mods$model_type <- paste(mods$model_type, mods$stoch_deter, sep = " - ") +mods$stoch_deter <- NULL +mods$model_type <- gsub("Branching process - Stochastic", "Branching Process", mods$model_type) +mods$transmission_route <- gsub("Human to human \\(direct contact\\)", "Human-Human", mods$transmission_route) +mods$transmission_route <- gsub("Airborne or close contact;Human to human \\(direct contact\\)","Airborne or close contact; Human-Human",mods$transmission_route) +mods$assumptions <- gsub("Homogeneous mixing", "", mods$assumptions) +mods$assumptions <- gsub("Latent period is same as incubation period", "", mods$assumptions) +mods$assumptions <- gsub("Heterogenity in transmission rates - over time", "Time", mods$assumptions) +mods$assumptions <- gsub("Heterogenity in transmission rates - between groups", "Groups", mods$assumptions) +mods$assumptions <- gsub("Age dependent susceptibility", "Age", mods$assumptions) +mods$assumptions <- gsub("^;|;$", "", mods$assumptions) +mods$compartmental_type <- gsub("Not compartmental", "", mods$compartmental_type) +mods$compartmental_type <- gsub("Other compartmental", "Other", mods$compartmental_type) +mods$theoretical_model <- gsub("FALSE", "Fitted", mods$theoretical_model) +mods$theoretical_model <- gsub("TRUE", "Theoretical", mods$theoretical_model) +mods$interventions_type <- gsub("changes", "Changes", mods$interventions_type) +mods$interventions_type <- gsub("tracing", "Tracing", mods$interventions_type) +mods$interventions_type <- gsub("Unspecified", "", mods$interventions_type) +# mods <- mods %>% mutate(assumptions = case_when( +# covidence_id %in% c(285,2617,2620) ~ gsub("Groups","Spatial",assumptions), +# covidence_id %in% c(4120,4371) ~ gsub("Groups","Community Hygiene",assumptions), +# covidence_id %in% c(4136,4251) ~ gsub("Groups","Socio-Economic Status",assumptions), +# covidence_id %in% c(4343) ~ gsub("Groups","Protective Behaviour",assumptions), +# covidence_id %in% c(3735) ~ gsub("Groups","Quarantine Status",assumptions), +# TRUE ~ assumptions)) +mods <- mods %>% select(-c("covidence_id")) +mods$transmission_route <- factor(mods$transmission_route, + levels = c("Human-Human","Airborne or close contact; Human-Human")) +mods$compartmental_type <- factor(mods$compartmental_type, + levels = c("","SIR","SEIR","Other","Other;SIR","SIR;SIS","SIS")) +mods <- mods[order(mods$model_type,mods$transmission_route,mods$assumptions, + mods$compartmental_type),] +mods$transmission_route <- as.character(mods$transmission_route) +mods$compartmental_type <- as.character(mods$compartmental_type) + +mods$transmission_route[is.na(mods$transmission_route)] <- "" +mods$compartmental_type[is.na(mods$compartmental_type)] <- "" +mods$assumptions[is.na(mods$assumptions)] <- "" +mods$interventions_type[is.na(mods$interventions_type)] <- "" +mods$refs[is.na(mods$refs)] <- "" + +mods <- insert_blank_rows(mods,"model_type") +write.table(mods, file = "latex_models.csv", sep = ",", + row.names = FALSE, col.names = FALSE, quote = FALSE) + +#parameters +parameters <- mutate_at(parameters, + vars(parameter_value, parameter_lower_bound, parameter_upper_bound, + parameter_uncertainty_lower_value, parameter_uncertainty_upper_value, + parameter_uncertainty_single_value, distribution_par1_value, distribution_par2_value), + ~sub("\\.?0+$", "", sprintf("%.10f", round(., 10)))) +## +parameters <- parameters %>% + mutate(parameter_value= + ifelse(parameter_value!="NA", + parameter_value, + ifelse(parameter_lower_bound!="NA" & parameter_upper_bound!="NA", + paste(parameter_lower_bound, parameter_upper_bound, sep = " - "), ""))) +# +parameters$parameter_unit <- gsub("Substitutions/site/year", "s/s/y", parameters$parameter_unit) +parameters$parameter_unit <- gsub("Mutations/genome/generation \\(U\\)", "m/g/g", parameters$parameter_unit) +parameters$parameter_unit <- gsub("No units", "", parameters$parameter_unit) +parameters$parameter_unit <- gsub("Per day", "per day", parameters$parameter_unit) +parameters$parameter_unit <- gsub("Per hour", "per hour", parameters$parameter_unit) +parameters$parameter_unit <- gsub("Percentage \\(%\\)", "\\\\%", parameters$parameter_unit) +parameters$parameter_unit <- gsub("Days", "days", parameters$parameter_unit) +parameters$parameter_unit <- gsub("Weeks", "weeks", parameters$parameter_unit) +parameters$parameter_unit <- gsub("Max. nr. of cases superspreading \\(related to case\\)", "mnc", parameters$parameter_unit) + +parameters <- parameters %>% mutate(parameter_unit = case_when( + exponent == 0 ~ parameter_unit, + exponent == -2 & parameter_unit == "" ~ "\\\\%", + exponent == -3 & parameter_unit == "" ~ "per 1000", + exponent == -4 & parameter_unit == "" ~ "per 10k", + TRUE ~ paste(parameter_unit, sprintf("$10^{%d}$",exponent), sep=" "))) +# new bit +parameters <- parameters %>% mutate(parameter_value = case_when( + (parameter_class %in% c("Severity","Seroprevalence") | parameter_unit == "") ~ parameter_value, + TRUE ~ paste(parameter_value, parameter_unit, sep = " "))) +# parameters <- parameters %>% +# mutate(parameter_unit = ifelse(parameter_unit == "Percentage (%)", "%", parameter_unit), +# values = paste0(values, parameter_unit, sep = " ")) +parameters <- parameters %>% + mutate(unc_type= + ifelse(!is.na(distribution_par2_type), + paste(distribution_type, distribution_par2_type, sep = " "), + ifelse(!is.na(parameter_uncertainty_type), + paste(parameter_uncertainty_type), + ifelse(!is.na(parameter_uncertainty_singe_type), + paste(parameter_uncertainty_singe_type),"")))) +parameters$unc_type <- gsub("Inter Quartile Range \\(IQR\\)", "IQR", parameters$unc_type) +parameters$unc_type <- gsub("IQR \\[Estimator\\]", "IQR_e", parameters$unc_type) +parameters$unc_type <- gsub("IQR \\[Sample\\]", "IQR_s", parameters$unc_type) +parameters$unc_type <- gsub("Range \\[Estimator\\]", "Range_e", parameters$unc_type) +parameters$unc_type <- gsub("Range \\[Sample\\]", "Range_s", parameters$unc_type) +parameters$unc_type <- gsub("Standard Error \\(SE\\)", "SE", parameters$unc_type) +parameters$unc_type <- gsub("Gamma Standard deviation", "Gamma SD", parameters$unc_type) +parameters$unc_type <- gsub("Normal Standard deviation", "Normal SD", parameters$unc_type) +parameters$unc_type <- gsub("Normal-Log Variance", "Normal-Log var", parameters$unc_type) +parameters$unc_type <- gsub("Standard deviation \\(Sd\\)", "SD", parameters$unc_type) +parameters$unc_type <- gsub("SD \\[Estimator\\]", "SD_e", parameters$unc_type) +parameters$unc_type <- gsub("SD \\[Sample\\]", "SD_s", parameters$unc_type) +parameters$unc_type <- gsub("Highest Posterior Density Interval 95%", "HPDI95%", parameters$unc_type) +parameters$unc_type <- gsub("CRI95%", "CrI95%", parameters$unc_type) +parameters$unc_type <- gsub("%", "\\\\%", parameters$unc_type) +parameters$unc_type <- gsub("_", "\\\\_", parameters$unc_type) +# +parameters <- parameters %>% + mutate(uncertainty= + ifelse(distribution_par2_value!="NA", + paste(distribution_par2_value), + ifelse(parameter_uncertainty_lower_value!="NA" & parameter_uncertainty_upper_value!="NA", + paste(parameter_uncertainty_lower_value, parameter_uncertainty_upper_value, sep = "-"), + ifelse(parameter_uncertainty_single_value!="NA", + paste(parameter_uncertainty_single_value), + "")))) +# new bit +parameters <- parameters %>% mutate(unc_type = case_when( + unc_type %in% c("Unspecified","") ~ "", + TRUE ~ paste(unc_type, ": ", uncertainty, sep = ""))) +## +#parameters$cfr_ifr_denominator[is.na(parameters$cfr_ifr_denominator)] <- +# parameters$population_sample_size[is.na(parameters$cfr_ifr_denominator)] +parameters$population_country <- gsub(",", "", parameters$population_country) +#new as well +parameters <- parameters %>% + mutate(population_study_start_month = substr(population_study_start_month, 1, 3), + population_study_end_month = substr(population_study_end_month, 1, 3)) %>% + mutate(dates = paste(population_study_start_day,population_study_start_month,population_study_start_year,"-", + population_study_end_day,population_study_end_month,population_study_end_year), + dates = gsub("NA","",dates), + dates = trimws(gsub("\\s{2,}"," ",dates)), + dates = gsub("\\b(\\d{4})\\s*-\\s*\\1\\b","\\1",dates,perl = TRUE), + dates = ifelse(dates == "-","",dates)) %>% + mutate(population_study_start_day = coalesce(population_study_start_day,1), + population_study_start_month = ifelse(is.na(population_study_start_month),"Jan",population_study_start_month), + population_study_start_year = coalesce(population_study_start_year,0), + sdate = as.Date(paste(population_study_start_day,population_study_start_month, + population_study_start_year, sep = "-"), format = "%d-%b-%Y")) %>% + mutate(population_study_end_day = coalesce(population_study_end_day,28), + population_study_end_month = ifelse(is.na(population_study_end_month),"Dec",population_study_end_month), + population_study_end_year = coalesce(population_study_end_year,2024), + edate = as.Date(paste(population_study_end_day,population_study_end_month, + population_study_end_year, sep = "-"), format = "%d-%b-%Y")) +# +parameters$population_sample_type <- gsub("\\s.*", "", parameters$population_sample_type) +parameters$population_group <- str_to_title(parameters$population_group) +parameters$method_disaggregated_by <- gsub("Disease generation","Disease Generation",parameters$method_disaggregated_by) +parameters$method_disaggregated_by <- gsub("Level of exposure","Level of Exposure",parameters$method_disaggregated_by) +parameters <- parameters %>% mutate_all(~ ifelse(is.na(.), "", .)) + +#parameters - transmission +trns_params <- parameters %>% + filter(grepl("Mutations|Attack|Growth rate|Reproduction|beta - per capita contact rate per unit of time|Overdispersion", parameter_type, ignore.case = TRUE)) %>% + select(parameter_type, parameter_value, unc_type, + method_disaggregated_by, + genome_site, method_r, + population_sample_size, + population_country, dates, + population_sample_type, population_group, refs, central) +trns_params$parameter_type <- gsub("beta - per capita contact rate per unit of time", "beta", trns_params$parameter_type) +trns_params$parameter_type <- gsub("Mutations - ", "", trns_params$parameter_type) +trns_params$parameter_type <- gsub("Mutations – ", "", trns_params$parameter_type) +trns_params$parameter_type <- gsub("Attack rate", "Attack Rate", trns_params$parameter_type) +trns_params$parameter_type <- gsub("Growth rate \\(r\\)", "Growth Rate", trns_params$parameter_type) +trns_params$parameter_type <- gsub("\\(Basic R0\\)", "R0", trns_params$parameter_type) +trns_params$parameter_type <- gsub("\\(Effective, Re\\)", "Re", trns_params$parameter_type) +trns_params$parameter_type <- str_trim(str_to_title(trns_params$parameter_type)) +trns_params$parameter_type <- factor(trns_params$parameter_type, + levels = c("Reproduction Number R0", "Reproduction Number Re", + "Growth Rate", "Overdispersion","Beta", + "Attack Rate", "Secondary Attack Rate", + "Mutation Rate","Substitution Rate")) +trns_params$method_r <- str_to_title(trns_params$method_r) +trns_params <- trns_params[order(trns_params$parameter_type,trns_params$genome_site,as.numeric(trns_params$central)),] +trns_params <- trns_params %>% select(-central) + +trns_params <- insert_blank_rows(trns_params,"parameter_type") +write.table(trns_params, file = "latex_transmission.csv", sep = ",", + row.names = FALSE, col.names = FALSE, quote = FALSE) + +#parameters - human delays +hdel_params <- parameters %>% + filter(grepl("Human delay", parameter_type, ignore.case = TRUE)) %>% + select(parameter_type, other_delay_start, other_delay_end, + parameter_value, parameter_value_type, unc_type, + method_disaggregated_by, + population_sample_size, + population_country, dates, + population_sample_type, population_group, refs, central) +hdel_params$parameter_type <- sub("^.* - ", "", hdel_params$parameter_type) +hdel_params$parameter_type <- sub(">", " - ", hdel_params$parameter_type) +hdel_params$parameter_type <- str_to_title(hdel_params$parameter_type) +hdel_params$parameter_type <- gsub("\\(.*?\\)", "", hdel_params$parameter_type) +hdel_params <- hdel_params %>% mutate(parameter_type = case_when( + (other_delay_start =="Symptom Onset/Fever" & other_delay_end == "Symptom Resolution") ~ "Symptomatic Period", + (other_delay_start =="Symptom Onset/Fever" & other_delay_end == "Specimen Collection") ~ "Onset - Testing", + (other_delay_start =="Specimen Collection" & other_delay_end == "Test Result") ~ "Testing - Test Result", + (other_delay_start =="Admission to Care/Hospitalisation" & other_delay_end == "Symptom Resolution") ~ "Admission - Symptom Resolution", + (other_delay_start =="Symptom Onset/Fever" & other_delay_end == "Other: Sample collection/Diagnosis") ~ "Onset - Testing", + (other_delay_start =="Symptom Onset/Fever" & other_delay_end == "Other: Start of ribavirin treatment") ~ "Onset - Start of Treatment", + (other_delay_start =="Other: Start of ribavirin treatment" & other_delay_end == "Other: End of ribavirin treatment") ~ "Duration of Antiviral Treatment", + (other_delay_start =="Other: Start of oxygen therapy" & other_delay_end == "Other: End of oxygen therapy") ~ "Duration of Oxygen Therapy", + (other_delay_start =="Other: Start of antibacterial therapy" & other_delay_end == "Other: End of antibacterial therapy") ~ "Duration of Antibacterial Therapy", + TRUE ~ parameter_type)) +hdel_params <- hdel_params %>% select(-c("other_delay_start","other_delay_end")) +hdel_params$parameter_type <- sub("Symptom Onset", "Onset", hdel_params$parameter_type) +hdel_params$parameter_type <- sub("Admission To Care", "Admission", hdel_params$parameter_type) +hdel_params$parameter_type <- sub("Discharge/Recovery", "Recovery", hdel_params$parameter_type) +hdel_params$parameter_type <- factor(hdel_params$parameter_type, + levels = c("Incubation Period", "Latent Period", "Infectious Period", + "Onset - Admission", "Symptomatic Period", + "Other Human Delay ", "Generation Time","Serial Interval", + "Admission - Recovery","Admission - Death","Time In Care ", + "Onset - Recovery","Onset - Death")) +hdel_params <- hdel_params[order(hdel_params$parameter_type,as.numeric(hdel_params$central)),] +hdel_params <- hdel_params %>% select(-central) +hdel_params <- insert_blank_rows(hdel_params,"parameter_type") +write.table(hdel_params, file = "latex_delays.csv", sep = ",", + row.names = FALSE, col.names = FALSE, quote = FALSE) + +#parameters - CFRs +cfrs_params <- parameters %>% + filter(grepl("Severity - case fatality rate", parameter_type, ignore.case = TRUE)) %>% + select(parameter_value, unc_type, + method_disaggregated_by, + cfr_ifr_method, cfr_ifr_numerator,cfr_ifr_denominator, + population_country, dates, + population_sample_type, population_group, refs, central) +cfrs_params$population_country <- gsub(";", "\\, ", cfrs_params$population_country) +cfrs_params <- cfrs_params %>% arrange(population_country,as.numeric(central)) +cfrs_params <- cfrs_params %>% select(-central) +cfrs_params <- insert_blank_rows(cfrs_params,"population_country") +write.table(cfrs_params, file = "latex_severity.csv", sep = ",", + row.names = FALSE, col.names = FALSE, quote = FALSE) + +#parameters - seroprevalence +sero_params <- parameters %>% + filter(grepl("Seroprevalence", parameter_type, ignore.case = TRUE)) %>% + select(parameter_value, unc_type, + method_disaggregated_by, + parameter_type,cfr_ifr_numerator,cfr_ifr_denominator, + population_country, dates, + population_sample_type, population_group, refs, central) +sero_params$parameter_type <- sub("^.* - ", "", sero_params$parameter_type) +sero_params$population_country <- gsub(";", "\\, ", sero_params$population_country) +sero_params$population_country[sero_params$population_country==""] <- "Unspecified" +sero_params <- sero_params %>% arrange(population_country, parameter_type, as.numeric(central)) +sero_params <- sero_params %>% select(-central) +sero_params <- insert_blank_rows(sero_params,"population_country") +write.table(sero_params, file = "latex_seroprevalence.csv", sep = ",", + row.names = FALSE, col.names = FALSE, quote = FALSE) + +#parameters - risk factors +risk_params <- parameters %>% + filter(grepl("Risk factors", parameter_type, ignore.case = TRUE)) %>% + select(riskfactor_outcome, + riskfactor_name, riskfactor_significant, + riskfactor_adjusted, population_sample_size, + population_country, dates, + population_sample_type, population_group, refs) +risk_params$riskfactor_outcome <- factor(risk_params$riskfactor_outcome, + levels = c("Infection", "Severe disease", "Probable case", "Superspreading", + "Other", "Death","Serology", "Attack Rate", "Incidence", + "Mortality", "Recovery", "Fever & being febrile", + "Incubation Period", "Admission-Death Delay", + "Admission-Discharge Delay", "Incidence Rate")) +risk_params$riskfactor_significant <- str_to_title(risk_params$riskfactor_significant) +risk_params$riskfactor_significant <- factor(risk_params$riskfactor_significant, + levels = c("Significant","Not Significant","Unspecified")) + +risk_params$riskfactor_adjusted <- str_to_title(risk_params$riskfactor_adjusted) +risk_params <- risk_params %>% mutate(riskfactor_name = gsub("Contact with animal","Contact with Animal",riskfactor_name), + riskfactor_name = gsub("Household contact","Household Contact",riskfactor_name), + riskfactor_name = gsub("Close contact","Close Contact",riskfactor_name), + riskfactor_name = gsub("Non-household contact","Non-household Contact",riskfactor_name)) + +risk_params <- risk_params[order(risk_params$riskfactor_outcome,risk_params$riskfactor_significant, + risk_params$riskfactor_name,risk_params$riskfactor_adjusted),] +risk_params$riskfactor_significant <- as.character(risk_params$riskfactor_significant) +risk_params$riskfactor_significant[is.na(risk_params$riskfactor_significant)] <- "NA" +risk_params <- insert_blank_rows(risk_params,"riskfactor_outcome") +write.table(risk_params, file = "latex_riskfactors.csv", sep = ",", + row.names = FALSE, col.names = FALSE, quote = FALSE) + diff --git a/src/sars_summary/orderly.R b/src/sars_summary/orderly.R new file mode 100644 index 00000000..e1458702 --- /dev/null +++ b/src/sars_summary/orderly.R @@ -0,0 +1,336 @@ +#task to plot sars-specific figures (mostly in SI) + +library(orderly2) +library(tidyverse) +library(stringr) +library(metafor) +library(meta) +library(estmeansd) +library(mixdist) +library(ggplot2) +library(ggsci) +library(sf) +library(ragg) +library(ggspatial) +library(ggforce) +library(png) +library(grid) +library(patchwork) +library(gridExtra) +library(ggbreak) + +#orderly preparation +orderly_strict_mode() +orderly_parameters(pathogen = "SARS") +orderly_dependency("db_compilation", "latest(parameter:pathogen == this:pathogen)", + c("articles.csv", "models.csv", "parameters.csv")) +# orderly_shared_resource("Africa_Boundaries-shp" = "Africa_Boundaries-shp", +# "wca_admbnda_adm0_ocha" = "wca_admbnda_adm0_ocha", +# "wca_admbnda_adm1_ocha" = "wca_admbnda_adm1_ocha", +# "rivers_africa_37333" = "rivers_africa_37333") +orderly_shared_resource("lassa_functions.R" = "lassa_functions.R") +source("lassa_functions.R") + +orderly_artefact("sars-specific figures", + c("figure_S1.png","figure_S2.png","figure_S3.png")) + +################### +## DATA CURATION ## +################### + +articles <- read_csv("articles.csv") +models <- read_csv("models.csv") +parameters <- read_csv("parameters.csv") + +dfs <- data_curation(articles,as_tibble(NULL),models,parameters,plotting=FALSE) + +articles <- dfs$articles +outbreaks <- dfs$outbreaks +models <- dfs$models +parameters <- dfs$parameters + +############## +## ARTICLES ## +############## + +quality <- articles %>% + mutate_at(vars(starts_with("qa")), funs(replace(., . == "Yes", 1))) %>% + mutate_at(vars(starts_with("qa")), funs(replace(., . == "No", 0))) %>% + mutate_at(vars(starts_with("qa")), as.numeric) +quality <- quality %>% rowwise() %>% mutate(score = 100*mean(c(qa_m1,qa_m2,qa_a3,qa_a4,qa_d5,qa_d6,qa_d7),na.rm=TRUE)) +quality <- quality %>% mutate(category = ifelse(covidence_id %in% models$covidence_id, "Modelling Studies", "Non-Modelling Studies")) +quality$category <- factor(quality$category, levels = c("Non-Modelling Studies", "Modelling Studies")) + +answers <- quality %>% + filter(!is.na(year_publication) & !is.na(pathogen)) %>% + dplyr::select(covidence_id,qa_m1,qa_m2,qa_a3,qa_a4,qa_d5,qa_d6,qa_d7) %>% + pivot_longer(-covidence_id,names_to = "Question",values_to= "Assessment") %>% + mutate(Assessment=as.factor(as.character(Assessment)), + Assessment=case_when(Assessment == '1' ~ 'Yes', + Assessment == '0' ~ 'No')) %>% + mutate(Question=case_when(Question=="qa_m1"~"Q1 Method: \nClear & \nReproducible", + Question=="qa_m2"~"Q2 Method: \nRobust & \nAppropriate", + Question=="qa_a3"~"Q3 Assumptions: \nClear & \nReproducible", + Question=="qa_a4"~"Q4 Assumptions: \nRobust & \nAppropriate", + Question=="qa_d5"~"Q5 Data: \nClear & \nReproducible", + Question=="qa_d6"~"Q6 Data: \nIssues \nAcknowledged", + Question=="qa_d7"~"Q7 Data: \nIssues \nAccounted For")) + +answers$Question <- factor(answers$Question, levels=rev(c("Q1 Method: \nClear & \nReproducible", + "Q2 Method: \nRobust & \nAppropriate", + "Q3 Assumptions: \nClear & \nReproducible", + "Q4 Assumptions: \nRobust & \nAppropriate", + "Q5 Data: \nClear & \nReproducible", + "Q6 Data: \nIssues \nAcknowledged", + "Q7 Data: \nIssues \nAccounted For"))) + +answers$Assessment[is.na(answers$Assessment)] <- "NA" +answers$Assessment <- factor(answers$Assessment,levels=c("NA","No","Yes")) + +p1 <- ggplot(data=articles, aes(x = year_publication)) + + geom_histogram(binwidth = 1, fill = "steelblue4", color = "black", alpha = 0.7) + + scale_x_continuous(limits = c(2000,2025), breaks = seq(2000, 2023, by = 5), expand = c(0, 0)) + + scale_y_continuous(limits = c(0,75), expand = c(0, 0)) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA)) + + labs(x = "Year of Publication", y = "Article Count") + +p2 <- ggplot() + + geom_histogram(data=quality, aes(x = score), binwidth = 20, boundary = 0, fill = "steelblue4", color = "black", alpha = 0.7) + + scale_x_continuous(limits = c(0,100), breaks = seq(0, 100, by = 20)) + + scale_y_continuous(limits = c(0,100), expand = c(0, 0)) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA)) + + labs(x = "Quality Assessment Score (%)", y = "Article Count") + +p3 <- ggplot() + + geom_point(data = quality, aes(x=year_publication,y=score,color=category)) + + geom_smooth(data = subset(quality, category == "Non-Modelling Studies"), aes(x=year_publication,y=score), span = 2, color = "springgreen3", fill = "springgreen3") + + geom_smooth(data = subset(quality, category == "Modelling Studies"), aes(x=year_publication,y=score), span = 2, color = "red", fill = "red") + + scale_x_continuous(limits = c(2000,2025), breaks = seq(2000, 2023, by=5), expand = c(0, 0)) + + scale_y_continuous(limits = c(0,100), breaks = seq(0, 100, by = 20)) + + xlab("Year of Publication") + ylab("Quality Assessment Score (%)") + + scale_color_manual(values = c("Non-Modelling Studies" = "springgreen3","Modelling Studies" = "red"), name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = 'bottom') + +p4 <- answers %>% + group_by(Question,Assessment) %>% summarize(count=n()) %>% ungroup() %>% + ggplot(aes(fill=Assessment, y=count, x=Question)) + + geom_bar(position="stack", stat="identity") + theme_bw() + + scale_y_continuous(limits = c(0,300), breaks = seq(0,300,by=40), expand = c(0, 0)) + + scale_fill_manual(values = c("darkolivegreen2","coral1","grey70"),aesthetics = "fill",name="",breaks=c('Yes', 'No','NA')) + + xlab("") + ylab("Article Count") + + coord_flip() + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA)) + + theme(legend.position = 'bottom') + +patchwork <- (p1 + p2 + p3 + p4) + plot_layout(ncol = 2, widths = c(1,1)) +patchwork <- patchwork + plot_annotation(tag_levels = 'A') +ggsave("figure_S1.png", plot = patchwork, width = 12, height = 12) + +############ +## MODELS ## +############ + +models <- models %>% mutate(model_type = str_replace_all(model_type,"Branching process","Branching Process"), + assumptions = str_replace_all(assumptions,";Latent period is same as incubation period",""), + compartmental_type = str_replace_all(compartmental_type,";SIR",""), + interventions_type = str_replace_all(interventions_type,"Unspecified","Other")) + +p1 <- ggplot() + + geom_bar(data = models, aes(x = model_type, fill = stoch_deter), color = "black") + + scale_y_continuous(limits = c(0,30), breaks = seq(0,30,by = 5), expand = c(0,0)) + + xlab("Model Type") + ylab("Model Count") + + scale_fill_manual(values = c("Deterministic" = "steelblue4","Stochastic" = "red"), name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = c(0,1), legend.justification = c(0,1), legend.box.just = "left") + +p2 <- ggplot() + + geom_bar(data = models, aes(x = factor(transmission_route, levels = c("Vector/Animal to human", + "Human to human (direct contact)", + "Human to human (direct contact);Vector/Animal to human", + "Airborne or close contact;Human to human (direct contact);Vector/Animal to human")), fill = model_type), color = "black") + + scale_x_discrete(labels = c("Vector/Animal to human" = "Rodent-Human Only", + "Human to human (direct contact);Vector/Animal to human" = "Rodent-Human\n& Human-Human", + "Human to human (direct contact)" = "Human-Human Only", + "Airborne or close contact;Human to human (direct contact);Vector/Animal to human" = "Rodent-Human,\nHuman-Human\n& Airborne")) + + scale_y_continuous(limits = c(0,30), breaks = seq(0,30,by = 5), expand = c(0,0)) + + xlab("Transmission Route(s)") + ylab("Model Count") + + scale_fill_manual(values = c("Branching Process" = "purple","Compartmental" = "orange", "Other" = "springgreen3"), name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = c(0,1), legend.justification = c(0,1), legend.box.just = "left") + +p3 <- ggplot() + + geom_bar(data = models, aes(x = factor(assumptions, levels = c("Homogeneous mixing", + "Heterogenity in transmission rates - over time", + "Heterogenity in transmission rates - between groups", + "Age dependent susceptibility", + "Heterogenity in transmission rates - between groups;Heterogenity in transmission rates - over time")), fill = model_type), color = "black") + + scale_x_discrete(labels = c("Homogeneous mixing" = "Homogeneous", + "Heterogenity in transmission rates - over time" = "Time-\nHeterogeneous", + "Heterogenity in transmission rates - between groups" = "Subgroup-\nHeterogeneous", + "Age dependent susceptibility" = "Age-\nHeterogeneous", + "Heterogenity in transmission rates - between groups;Heterogenity in transmission rates - over time" = "Time- & Subgroup-\nHeterogeneous")) + + scale_y_continuous(limits = c(0,30), breaks = seq(0,30,by = 5), expand = c(0,0)) + + xlab("Human Transmission Heterogeneity") + ylab("Model Count") + + scale_fill_manual(values = c("Branching Process" = "purple","Compartmental" = "orange", "Other" = "springgreen3"), name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = "none") + +p4 <- ggplot() + + geom_bar(data = models, aes(x = factor(compartmental_type, levels = c("SIR", + "SEIR", + "Other compartmental", + "Not compartmental")), fill = model_type), color = "black") + + scale_x_discrete(labels = c("Other compartmental" = "Other", + "Not compartmental" = "N/A")) + + scale_y_continuous(limits = c(0,30), breaks = seq(0,30,by = 5), expand = c(0,0)) + + xlab("Human Compartments") + ylab("Model Count") + + scale_fill_manual(values = c("Branching Process" = "purple","Compartmental" = "orange", "Other" = "springgreen3"), name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = "none") + +p5 <- ggplot() + + geom_bar(data = models, aes(x = factor(theoretical_model, levels = c("FALSE", + "TRUE")), fill = model_type), color = "black") + + scale_x_discrete(labels = c("FALSE" = "Fitted to Data", + "TRUE" = "Theoretical")) + + scale_y_continuous(limits = c(0,30), breaks = seq(0,30,by = 5), expand = c(0,0)) + + xlab("Model Calibration") + ylab("Model Count") + + scale_fill_manual(values = c("Branching Process" = "purple","Compartmental" = "orange", "Other" = "springgreen3"), name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = "none") + +p6 <- ggplot() + + geom_bar(data = models %>% separate_rows(interventions_type, sep = ";"), + aes(x = factor(interventions_type, levels = c("Vector/Animal control","Behaviour changes","Quarantine", + "Contact tracing","Treatment","Hospitals", + "Vaccination","Other")), fill = model_type), color = "black") + + scale_x_discrete(labels = c("Vector/Animal control" = "Rodent\nControl", + "Behaviour changes" = "Behaviour\nChanges", + "Contact tracing" = "Contact\nTracing", + "Other" = "Other &\nUnspecified")) + + scale_y_continuous(limits = c(0,30), breaks = seq(0,30,by = 5), expand = c(0,0)) + + xlab("Interventions") + ylab("Model Count") + + scale_fill_manual(values = c("Branching Process" = "purple","Compartmental" = "orange", "Other" = "springgreen3"), name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = "none") + +patchwork <- (p1 + p2 + p3 + p4 + p5 + p6) + plot_layout(ncol = 2, widths = c(1,1)) +patchwork <- patchwork + plot_annotation(tag_levels = 'A') +ggsave("figure_S2.png", plot = patchwork, width = 12, height = 16) + +################ +## PARAMETERS ## +################ + +parameters <- parameters %>% mutate(parameter_class = case_when( + parameter_class == "Reproduction number" ~ "Reproduction Numbers", + parameter_class %in% c("Relative contribution","Attack rate","Growth rate") ~ "Other Transmission Parameters", + parameter_class == "Human delay" ~ "Delays", + parameter_class == "Risk factors" ~ "Risk Factors", + TRUE ~ parameter_class), + parameter_type = case_when( + parameter_type == "Attack rate" ~ "Attack Rate", + parameter_type == "Growth rate (r)" ~ "Growth Rate", + parameter_type == "Human delay - admission to care>death" ~ "Time Admission to Outcome", + parameter_type == "Human delay - admission to care>discharge/recovery" ~ "Time Admission to Outcome", + parameter_type == "Human delay - incubation period" ~ "Incubation Period", + parameter_type == "Human delay - other human delay (go to section)" ~ "Other Delay", + parameter_type == "Human delay - symptom onset>admission to care" ~ "Time Onset to Admission", + parameter_type == "Human delay - symptom onset>death" ~ "Time Onset to Outcome", + parameter_type == "Human delay - symptom onset>discharge/recovery" ~ "Time Onset to Outcome", + parameter_type == "Human delay - time in care (length of stay)" ~ "Time Onset to Outcome", + parameter_type == "Mutations - evolutionary rate" ~ "Evolutionary Rate", + parameter_type == "Mutations – substitution rate" ~ "Substitution Rate", + parameter_type == "Relative contribution - human to human" ~ "Relative Transmission Contribution", + parameter_type == "Reproduction number (Basic R0)" ~ "Basic (R0)", + parameter_type == "Reproduction number (Effective, Re)" ~ "Effective (Re)", + parameter_type == "Risk factors" ~ "Risk Factors", + parameter_type == "Seroprevalence - IFA" ~ "IFA", + parameter_type == "Seroprevalence - IgG" ~ "IgG", + parameter_type == "Seroprevalence - IgM" ~ "IgM", + parameter_type == "Seroprevalence - PRNT" ~ "PRNT", + parameter_type == "Seroprevalence - Unspecified" ~ "Unspecified", + parameter_type == "Severity - case fatality rate (CFR)" ~ "Case Fatality Ratio (CFR)", + TRUE ~ parameter_type), + population_country = ifelse(is.na(population_country),"Unspecified",population_country), + study_midyear = ifelse(!is.na(population_study_start_year) & !is.na(population_study_end_year), + round((population_study_start_year + population_study_end_year) / 2), + population_study_start_year), + study_midyear_cat = case_when( + study_midyear %in% 2000:2004 ~ "2000-2004", + study_midyear %in% 2005:2009 ~ "2005-2009", + study_midyear %in% 2010:2014 ~ "2010-2014", + study_midyear %in% 2015:2019 ~ "2015-2019", + study_midyear %in% 2020:2029 ~ "2020-Present", + TRUE ~ "Unspecified"), + population_sample_type = case_when( + population_sample_type == "Mixed settings" ~ "Mixed Settings", + population_sample_type == "Trade / business based" ~ "Trade/Business-Based", + is.na(population_sample_type) ~ "Unspecified", + TRUE ~ str_replace_all(population_sample_type, " based", "-Based"))) %>% + mutate(parameter_type = factor(parameter_type, levels = unique(parameter_type[order(parameter_class,parameter_type)]))) + +p1 <- ggplot() + + geom_bar(data = parameters, + aes(x = reorder(parameter_type, parameter_class), fill = parameter_class), color = "black") + + scale_x_discrete(limits = rev) + + scale_y_break(c(125,240), scales = 0.1) + + scale_y_continuous(limits = c(0,270), breaks = seq(0,270,by = 30), expand = c(0,0)) + + xlab("Parameter Type") + ylab("Parameter Count") + + scale_fill_viridis_d(option = "magma", begin=0.15, end=0.95, name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = c(1,1), legend.justification = c(1,1), legend.box.just = "left") + + coord_flip() + +p2 <- ggplot() + + geom_bar(data = parameters %>% separate_rows(population_country, sep = ";") %>% + mutate(population_country = str_trim(population_country)), + aes(x = population_country, fill = parameter_class), color = "black") + + scale_x_discrete(limits = rev) + + scale_y_continuous(limits = c(0,180), breaks = seq(0,180,by = 30), expand = c(0,0)) + + xlab("Study Country") + ylab("Parameter Count") + + scale_fill_viridis_d(option = "magma", begin=0.15, end=0.95, name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = "none") + + coord_flip() + +p3 <- ggplot() + + geom_bar(data = parameters, aes(x = factor(study_midyear_cat, levels = c("2000-2004", "2005-2009", "2010-2014", + "2015-2019", "2020-Present",'Unspecified')), fill = parameter_class), color = "black") + + scale_x_discrete(limits = rev) + + scale_y_continuous(limits = c(0,180), breaks = seq(0,180,by = 30), expand = c(0,0)) + + xlab("Study Year") + ylab("Parameter Count") + + scale_fill_viridis_d(option = "magma", begin=0.15, end=0.95, name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = "none") + + coord_flip() + +p4 <- ggplot() + + geom_bar(data = parameters, aes(x = population_sample_type, fill = parameter_class), color = "black") + + scale_x_discrete(limits = rev) + + scale_y_continuous(limits = c(0,200), breaks = seq(0,180,by = 30), expand = c(0,0)) + + xlab("Study Setting") + ylab("Parameter Count") + + scale_fill_viridis_d(option = "magma", begin=0.15, end=0.95, name = NULL) + + theme_minimal() + + theme(panel.border = element_rect(color = "black", size = 1.25, fill = NA), + legend.position = "none") + + coord_flip() + +patchwork <- (p1 + p2 + p3 + p4) + plot_layout(ncol = 1, heights = c(1.1,1.7,0.5,0.7)) +patchwork <- patchwork + plot_annotation(tag_levels = 'A') +ggsave("figure_S3.png", plot = patchwork, width = 12, height = 16) + diff --git a/src/sars_transmission/orderly.R b/src/sars_transmission/orderly.R new file mode 100644 index 00000000..cd124f91 --- /dev/null +++ b/src/sars_transmission/orderly.R @@ -0,0 +1,123 @@ +#task to plot SARS transmission figures + +library(orderly2) +library(tidyverse) +library(stringr) +library(metafor) +library(meta) +library(estmeansd) +library(mixdist) +library(ggplot2) +library(ggsci) +library(sf) +library(ragg) +library(ggspatial) +library(ggforce) +library(png) +library(grid) +library(patchwork) +library(gridExtra) + +#orderly preparation +orderly_strict_mode() +orderly_parameters(pathogen = NULL) +orderly_dependency("db_compilation", "latest(parameter:pathogen == this:pathogen)", + c("articles.csv", "outbreaks.csv", "models.csv", "parameters.csv")) +orderly_shared_resource("lassa_functions.R" = "lassa_functions.R") +source("lassa_functions.R") +orderly_artefact("sars-specific figures",c("figure_5.png")) + +################### +## DATA CURATION ## +################### + +articles <- read_csv("articles.csv") +outbreaks <- read_csv("outbreaks.csv") +models <- read_csv("models.csv") +parameters <- read_csv("parameters.csv") + +dfs <- data_curation(articles,outbreaks,models,parameters, plotting = TRUE) + +articles <- dfs$articles +outbreaks <- dfs$outbreaks +models <- dfs$models +parameters <- dfs$parameters + +################## +## TRANSMISSION ## +################## + +#extract data to be plotted +# +# NEED TO SPLIT ATTACK RATES INTO ATTACK RATES + SECONDARY ATTACK RATES +d1 <- parameters %>% filter(parameter_type == 'Mutations - evolutionary rate') +d2 <- parameters %>% filter(parameter_type == 'Mutations – substitution rate') +d3 <- parameters %>% filter(parameter_class == 'Overdispersion') +d4 <- parameters %>% filter(parameter_class == 'Attack rate') +d5 <- parameters %>% filter(parameter_class == 'Growth rate') +d6 <- parameters %>% filter(parameter_class == 'Reproduction number') + +#arrange data and format for plotting +d1 <- d1 %>% mutate(across(c(parameter_value,parameter_lower_bound, parameter_upper_bound, + parameter_uncertainty_lower_value, parameter_uncertainty_upper_value), + ~. * 10^4)) #multiply by 10^4 +d2 <- d2 %>% mutate(across(c(parameter_value,parameter_lower_bound, parameter_upper_bound, + parameter_uncertainty_lower_value, parameter_uncertainty_upper_value), + ~. * 10^4)) #multiply by 10^4 +d4 <- d4 %>% mutate_at(vars(c("parameter_value","parameter_lower_bound","parameter_upper_bound", + "parameter_uncertainty_lower_value","parameter_uncertainty_upper_value")), + list(~ ifelse(parameter_unit == "No units", . * 100, .))) %>% + mutate(parameter_unit = ifelse(parameter_unit == "No units", "Percentage (%)", parameter_unit)) + +d1 <- d1 %>% arrange(genome_site,-central) +d2 <- d2 %>% arrange(genome_site,-central) +d3 <- d3 %>% arrange(-central) +d4 <- d4 %>% mutate(arate=c("Primary","Primary","Secondary")) %>% + arrange(arate,-central) +d5 <- d5 %>% arrange(-central) +d6 <- d6 %>% arrange(parameter_type,-central) + +d2 <- d2 %>% mutate(genome_site = factor(genome_site, + levels = unique(genome_site), + labels = c("L","S"))) +d6 <- d6 %>% mutate(parameter_type = factor(parameter_type, + levels = unique(parameter_type), + labels = c("Basic (R0)","Effective (Re)")), + parameter_value = coalesce(parameter_value,central), + parameter_unit = replace_na(parameter_unit, "No units"), + parameter_value_type = replace_na(parameter_value_type, "Unspecified"), + population_country = replace_na(population_country,"Unspecified"), + population_country_v2 = case_when(!str_detect(population_country,",")~population_country, + population_country=='Hong Kong SAR, China'~'Greater China', + str_detect(population_country,'Canada, Hong Kong')|str_detect(population_country,'Canada;Singapore')~'Multi Region', + str_detect(population_country,'Taiwan, China')~'Taiwan + China', + TRUE ~ population_country), + population_sample_type = replace_na(population_sample_type, "Unspecified")) %>% + filter(!(parameter_value>10|(!is.na(parameter_upper_bound) & parameter_upper_bound>10))) # exclude Kwok (2007) and Moser (2015) due to extreme values + +#call forest plot function +p1 <- forest_plot(d1,expression(Evolutionary~Rate~(s/s/y ~10^{-4})),"genome_site",c(5,30)) + guides(color = guide_legend(title = "Gene", order = 1)) +p2 <- forest_plot(d2,expression(Substitution~Rate~(s/s/y ~10^{-4})),"genome_site",c(5,100)) + guides(color = guide_legend(title = "Segment", order = 1)) + + +# overdispersion +## Convert k params into the same type to be consistent between models. +## need to get betas for covidence 3685: parameters %>% filter(covidence_id==3685), will need to get N & S for this from paper +d3 + + +p4 <- forest_plot(d4,'Attack Rate (%)',"arate",c(-0.01,1)) +p5 <- forest_plot(d5,'Growth Rate (per day)',"pathogen",c(0,1.25)) + +# make colours across plots the same to simplify legend!!! + add in consideration for setting +# meta-analysis ?? how to deal with lack of sample size especially for CI rows etc? --> double check 3468, 5722, 1023 to see if we can get sample sizes! +# sd = sqrt(N) * (upper - lower ) / 3.92 +p6 <- forest_plot(d6,'Reproduction Number',"parameter_type",c(0,10)) +p6_1 <- forest_plot(d6|>filter(str_detect(parameter_type,('Basic'))),'Basic Reproduction Number',"population_country_v2",c(0,10)) +p6_2 <- forest_plot(d6|>filter(str_detect(parameter_type,('Effective'))),'Effective Reproduction Number',"population_country_v2",c(0,10)) + +p6_1 + p6_2 + +patchwork <- (p6 + p5 + p4 + p3 + p1 + p2) + plot_layout(ncol = 2, widths = c(1,1)) +patchwork <- patchwork + plot_annotation(tag_levels = 'A') #+ plot_layout(guides = 'collect') +ggsave("figure_5.png", plot = patchwork, width = 12, height = 10) \ No newline at end of file diff --git a/src/sars_world_map/orderly.R b/src/sars_world_map/orderly.R new file mode 100644 index 00000000..495943ce --- /dev/null +++ b/src/sars_world_map/orderly.R @@ -0,0 +1,122 @@ +#task to create sars maps + +library(tidyverse) +library(readxl) +library(harrypotter) +library(ggplot2) +library(grid) +library(rnaturalearth) +library(rnaturalearthdata) +library(ggspatial) +library(ggrepel) +library(patchwork) +library(gt) +library(png) + +#orderly preparation +orderly_strict_mode() +orderly_parameters(pathogen = NULL) +orderly_dependency("db_compilation", "latest(parameter:pathogen == this:pathogen)", + c("articles.csv", "models.csv", "parameters.csv")) +orderly_shared_resource("world_cases_table.xlsx" = "world_cases_table.xlsx") +orderly_shared_resource("lassa_functions.R" = "lassa_functions.R") +source("lassa_functions.R") + +orderly_artefact("sars-specific tables",c("world_map.png")) + +################### +## DATA CURATION ## +################### + +articles <- read_csv("articles.csv") +models <- read_csv("models.csv") +parameters <- read_csv("parameters.csv") + +dfs <- data_curation(articles,tibble(),models,parameters, plotting = TRUE ) + +articles <- dfs$articles +models <- dfs$models +parameters <- dfs$parameters + + +create_inset_png <- function(data=non_imported_cnts_short,location='SGP') +{ + location_label_map <- list(SGP='Singapore', + HKG='Hong Kong', + CAN='Canada', + CHN='China', + TWN='Taiwan', + VNM='Vietnam') + + loc_in <- location_label_map[[location]] + + LOC <- data %>% filter(ISO3==location) %>% + dplyr::select(-ISO3) %>% + rename('Onset 1st case'=onset_first_probable_case,'Onset last case'=onset_last_probable_case,'Imported %'=pct_imported,'CFR %'=CFR) + + LOC <- as_tibble(cbind(loc_name = names(LOC), t(LOC))) %>% + rename(' '=V2,!!loc_in:=loc_name) %>% + gt() %>% + cols_label(!!loc_in:= md(paste0('_**',loc_in ,'**_'))) %>% + tab_style( + style = list(cell_borders(sides = "all", color = "white", style = "solid", weight = px(1))), + location = cells_body() + ) %>% + tab_style( + style = list( cell_text(weight = "bold")), + locations = cells_body( columns = !!loc_in ) ) %>% + gtsave(paste0(tolower(location),'_info.png')) +} + +world <- ne_countries(scale = "medium", returnclass = "sf") +sars_data <- read_excel("world_cases_table.xlsx") %>% filter(!is.na(ISO3)) +non_imported_cnts <- sars_data %>% filter(pct_imported < .5 & ISO3 != 'RUS') + +f <- data.frame(map_point = c("Beijing", + "Guangzhou", + "Singapore", + "Toronto"), + latitude = c(39.9042,23.1291,1.290270,43.6532), + longitude = c(116.4074,113.2644,103.851959,-79.3832), + type = c("Outbreak Location","Outbreak Location","Outbreak Location","Outbreak Location")) + +world <- world %>% left_join(sars_data,by=c('iso_a3'='ISO3')) +world_local_transmission <- world %>% filter(iso_a3 %in% non_imported_cnts$ISO3) + +world_map <- ggplot() + + geom_sf(data = world, lwd = 0.3, col = "grey70", aes(fill = Total)) + + geom_sf(data = world_local_transmission, lwd = 1, col = "darkred", fill = NA) + + geom_point(data = f, aes(x = longitude, y = latitude, color = "Outbreak Location"), shape = 8, size = 1.5, stroke = 1.5) + + theme_light() + + scale_color_manual(values = c("Outbreak Location" = "green2")) + + geom_label_repel(data = world_local_transmission,aes(x=label_x,y=label_y,label=name),size = 2, fontface = "bold") + + scale_fill_hp(option = "NewtScamander",na.value='grey90',trans = 'log') + + scale_alpha_continuous(trans='log1p', limits=c(1,6000)) + + theme(panel.grid.minor = element_line(color = gray(0.5), linetype = "dashed", + size = 0.3), panel.background = element_rect(fill = "aliceblue"), + legend.position = 'none') + + ylab('Latitude') + xlab('Longitude') + +# use inset_elements for location specific data we want to show for 6 outbreak regions with local transmission +non_imported_cnts_short <- non_imported_cnts %>% + dplyr::select(ISO3,Total,Median_age,CFR,pct_imported,onset_first_probable_case,onset_last_probable_case) %>% + mutate(CFR=round(CFR*100,2), + pct_imported = round(pct_imported*100,2)) + +for(iso3 in non_imported_cnts_short$ISO3) + create_inset_png(non_imported_cnts_short,iso3) + +# cut out Antarctica + +inset_width <- 0.05 +inset_hight <- 0.125 +test <- world_map + patchwork::inset_element(grid::rasterGrob(png::readPNG("sgp_info.png")), 0.725,0.325,0.725+inset_width,.325+inset_hight) + + patchwork::inset_element(grid::rasterGrob(png::readPNG("hkg_info.png")), 0.81,0.45,0.81+inset_width,.45+inset_hight) + + patchwork::inset_element(grid::rasterGrob(png::readPNG("twn_info.png")), 0.85,0.575,0.85+inset_width,.575+inset_hight) + + patchwork::inset_element(grid::rasterGrob(png::readPNG("chn_info.png")), 0.75,0.75,0.75+inset_width,0.75+inset_hight) + + patchwork::inset_element(grid::rasterGrob(png::readPNG("can_info.png")), 0.1,0.7,0.1+inset_width,.7+inset_hight) + + patchwork::inset_element(grid::rasterGrob(png::readPNG("vnm_info.png")), 0.65,0.575,0.65+inset_width,.575+inset_hight) + + + +ggsave("world_map.png", plot = test, width = 18, height = 12)