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-  "intel-corporation.oneapi-analysis-configurator.binary-path": "/home/george/Documents/development/epiworld/tests/01c.o"
+  "intel-corporation.oneapi-analysis-configurator.binary-path": "/home/george/Documents/development/epiworld/tests/01c.o",
+  "grammarly.selectors": [
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 }

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+paper.docx: paper.qmd
+	quarto render paper.qmd --to docx
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+	quarto preview paper.qmd
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+.PHONY: preview

paper/paper.md

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+# epiworld: A fast and flexible ABM framework for epidemiological
+simulations
+George G. Vega Yon, Ph.D.
+2024-04-05
+
+`epiworld` is a fast and flexible agent-based modeling (ABM) framework
+for epidemiological simulations. Designed in C++, it can simulate large
+populations with complex interactions. The framework is designed to be
+modular, allowing users to extend and modify the model to suit their
+needs quickly.
+
+## Features
+
+**Header-only**: epiworld is a header-only template library, making it
+easy to integrate into existing projects. It is distributed as a
+collection of files and as <a
+href="https://github.com/UofUEpiBio/epiworld/blob/master/epiworld.hpp"
+target="_blank">a single header file</a>.
+
+**A framework**: epiworld is designed to be a flexible framework for
+building epidemiological simulations. It provides a set of core
+components that can be easily extended and modified to suit the user’s
+needs.
+
+**Fast**: epiworld is designed for speed. It is implemented in C++ and
+uses efficient data structures and algorithms to ensure simulations
+dash. Furthermore, epiworld is designed to take advantage of multi-core
+processors, allowing simulations to be run in parallel (see the
+[benchmark section](#speed-benchmark) for more details).
+
+**Complex disease dynamics**: epiworld supports complex disease
+dynamics, including the evolution of the disease over time. For
+instance, diseases can accumulate mutations.
+
+**Open-source**: With funding from the Centers for Disease Control and
+Prevention \[CDC\], epiworld is open-source and available under the MIT
+license, meaning it is entirely free. The source code is available on
+[GitHub](https://github.com/UofUEpiBio/epiworld).
+
+## Example use cases
+
+As a framework, `epiworld` can simulate various epidemiological
+scenarios. This section provides some possible use cases for the
+package:
+
+- **Geographically informed models**: With the ability to model complex
+  interactions between agents, the library can simulate geographically
+  informed models featuring multiple regions with different populations
+  and individuals moving between regions.
+
+- **Non-pharmaceutical interventions (NPIs)**: The library has been used
+  to simulate NPIs featuring masking and social distancing. Furthermore,
+  its architecture allows for deploying interventions that are reactive
+  to the model’s state, such as a mask mandate that only activates when
+  the prevalence of the disease reaches a certain threshold.
+
+- **Vaccination strategies**: The library can simulate different
+  vaccination strategies, including prioritizing certain groups for
+  vaccination, varying the vaccination rate, and modeling the impact of
+  vaccine hesitancy.
+
+- **Disease evolution**: The library can simulate the evolution of the
+  disease over time, including the emergence of new variants and the
+  impact of these variants on the spread of the disease.
+
+- **Population comorbidities**: The library can model the impact of
+  population comorbidities on the spread of the disease, including how
+  different comorbidities affect the transmission and severity of the
+  disease.
+
+# Appendix
+
+## Code example
+
+The following code snippet shows a simple example of how to use epiworld
+to simulate an epidemic, particularly the Susceptible-Infected-Recovered
+model \[SIR\]:
+
+``` cpp
+#include "epiworld.hpp"
+
+using namespace epiworld;
+
+int main()
+{
+
+    // epiworld already comes with a couple
+    // of models, like the SIR
+    epimodels::ModelSIR<> hello(
+        "COVID-19", // Name of the virus
+        0.01,        // Initial prevalence
+        0.9,        // Transmission probability
+        0.3         // Recovery probability
+        );
+
+    // We can simulate agents using a small-world network
+    // with 100,000 individuals, in this case
+    hello.agents_smallworld(100000, 4L, false, .01);
+
+    // Running the model and printing the results
+    // Setting the number of days (100) and seed (122)
+    hello.run(100, 122);
+    hello.print();
+
+    return 0;
+
+}
+```
+
+The output could look something like the following:
+
+``` bash
+_________________________________________________________________________
+Running the model...
+||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
+ done.
+________________________________________________________________________________
+________________________________________________________________________________
+SIMULATION STUDY
+
+Name of the model   : Susceptible-Infected-Recovered (SIR)
+Population size     : 100000
+Agents' data        : (none)
+Number of entities  : 0
+Days (duration)     : 100 (of 100)
+Number of viruses   : 1
+Last run elapsed t  : 103.00ms
+Last run speed      : 96.34 million agents x day / second
+Rewiring            : off
+
+Global events:
+ (none)
+
+Virus(es):
+ - COVID-19 (baseline prevalence: 1.00%)
+
+Tool(s):
+ (none)
+
+Model parameters:
+ - Recovery rate     : 0.3000
+ - Transmission rate : 0.9000
+
+Distribution of the population at time 100:
+  - (0) Susceptible :  99000 -> 2565
+  - (1) Infected    :   1000 -> 366
+  - (2) Recovered   :      0 -> 97069
+
+Transition Probabilities:
+ - Susceptible  0.96  0.04  0.00
+ - Infected     0.00  0.70  0.30
+ - Recovered    0.00  0.00  1.00
+```
+
+## Speed benchmark
+
+![](benchmark.png)
+
+## Implementation
+
+![](building-models.png)

paper/paper.qmd

@@ -0,0 +1,128 @@
+---
+title: 'epiworld: A fast and flexible ABM framework for epidemiological simulations'
+format:
+  gfm: default
+  docx: default
+author: 'George G. Vega Yon, Ph.D.'
+date: April 5, 2024
+---
+
+`epiworld` is a fast and flexible agent-based modeling (ABM) framework for epidemiological simulations. Designed in C++, it can simulate large populations with complex interactions. The framework is designed to be modular, allowing users to extend and modify the model to suit their needs quickly.
+
+## Features
+
+**Header-only**: epiworld is a header-only template library, making it easy to integrate into existing projects. It is distributed as a collection of files and as [a single header file](https://github.com/UofUEpiBio/epiworld/blob/master/epiworld.hpp){target="_blank"}.
+
+**A framework**: epiworld is designed to be a flexible framework for building epidemiological simulations. It provides a set of core components that can be easily extended and modified to suit the user's needs.
+
+**Fast**: epiworld is designed for speed. It is implemented in C++ and uses efficient data structures and algorithms to ensure simulations dash. Furthermore, epiworld is designed to take advantage of multi-core processors, allowing simulations to be run in parallel (see the [benchmark section](#speed-benchmark) for more details).
+
+**Complex disease dynamics**: epiworld supports complex disease dynamics, including the evolution of the disease over time. For instance, diseases can accumulate mutations.
+
+**Open-source**: With funding from the Centers for Disease Control and Prevention [CDC], epiworld is open-source and available under the MIT license, meaning it is entirely free. The source code is available on [GitHub](https://github.com/UofUEpiBio/epiworld).
+
+## Example use cases
+
+As a framework, `epiworld` can simulate various epidemiological scenarios. This section provides some possible use cases for the package:
+
+- **Geographically informed models**: With the ability to model complex interactions between agents, the library can simulate geographically informed models featuring multiple regions with different populations and individuals moving between regions.
+
+- **Non-pharmaceutical interventions (NPIs)**: The library has been used to simulate NPIs featuring masking and social distancing. Furthermore, its architecture allows for deploying interventions that are reactive to the model's state, such as a mask mandate that only activates when the prevalence of the disease reaches a certain threshold.
+
+- **Vaccination strategies**: The library can simulate different vaccination strategies, including prioritizing certain groups for vaccination, varying the vaccination rate, and modeling the impact of vaccine hesitancy.
+
+- **Disease evolution**: The library can simulate the evolution of the disease over time, including the emergence of new variants and the impact of these variants on the spread of the disease.
+
+- **Population comorbidities**: The library can model the impact of population comorbidities on the spread of the disease, including how different comorbidities affect the transmission and severity of the disease.
+
+
+# Appendix
+
+## Code example
+
+The following code snippet shows a simple example of how to use epiworld to simulate an epidemic, particularly the Susceptible-Infected-Recovered model [SIR]:
+
+```cpp
+#include "epiworld.hpp"
+
+using namespace epiworld;
+
+int main()
+{
+
+    // epiworld already comes with a couple
+    // of models, like the SIR
+    epimodels::ModelSIR<> hello(
+        "COVID-19", // Name of the virus
+        0.01,        // Initial prevalence
+        0.9,        // Transmission probability
+        0.3         // Recovery probability
+        );
+
+    // We can simulate agents using a small-world network
+    // with 100,000 individuals, in this case
+    hello.agents_smallworld(100000, 4L, false, .01);
+
+    // Running the model and printing the results
+    // Setting the number of days (100) and seed (122)
+    hello.run(100, 122);
+    hello.print();
+
+    return 0;
+
+}
+```
+
+The output could look something like the following:
+
+```bash
+_________________________________________________________________________
+Running the model...
+||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
+ done.
+________________________________________________________________________________
+________________________________________________________________________________
+SIMULATION STUDY
+
+Name of the model   : Susceptible-Infected-Recovered (SIR)
+Population size     : 100000
+Agents' data        : (none)
+Number of entities  : 0
+Days (duration)     : 100 (of 100)
+Number of viruses   : 1
+Last run elapsed t  : 103.00ms
+Last run speed      : 96.34 million agents x day / second
+Rewiring            : off
+
+Global events:
+ (none)
+
+Virus(es):
+ - COVID-19 (baseline prevalence: 1.00%)
+
+Tool(s):
+ (none)
+
+Model parameters:
+ - Recovery rate     : 0.3000
+ - Transmission rate : 0.9000
+
+Distribution of the population at time 100:
+  - (0) Susceptible :  99000 -> 2565
+  - (1) Infected    :   1000 -> 366
+  - (2) Recovered   :      0 -> 97069
+
+Transition Probabilities:
+ - Susceptible  0.96  0.04  0.00
+ - Infected     0.00  0.70  0.30
+ - Recovered    0.00  0.00  1.00
+```
+
+## Speed benchmark
+
+![](benchmark.png)
+
+## Implementation
+
+![](building-models.png)
+