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Getting started with epiworldRShiny • epiworldRShiny + + + + + + + + + + + + +++ + + + + + + + diff --git a/articles/index.html b/articles/index.html new file mode 100644 index 0000000..646623b --- /dev/null +++ b/articles/index.html @@ -0,0 +1,88 @@ + ++ + + + + ++ + + + +++ + + +++ + + +Getting started with epiworldRShiny
+Derek +Meyer
+George Vega +Yon
+ +2024-03-26
+ + Source:vignettes/getting_started.Rmd++ +getting_started.RmdWelcome to epiworldRShiny, a powerful Shiny app designed to enhance +the functionality of the epiworldR package for simulating agent-based +models. The package offers a user-friendly interface that enables users +to simulate infectious diseases using nine different epidemiological +models. With epiworldRShiny, incorporating various interventions such as +vaccines, masking, and school closures into your simulations can be done +with ease. The package also provides intuitive visualization tools to +help interpret and analyze simulation results. Whether you’re a +researcher, healthcare professional, or student, epiworldRShiny +simplifies the process of simulating and understanding the dynamics of +infectious diseases.
+++Example 1: Simulating a SEIR Network Model +
+++Running the application +
+In order to run the epiworldRShiny application, first ensure that the +package is installed and loaded using the following code. To launch the +application, use call the function, epiworldRShiny().
+++
# install.packages("epiworldRShiny") +library(epiworldRShiny) +#> Loading required package: shiny +# epiworldRShiny()++Model set-up +
+After launching the application, notice the sidebar contains many +disease and model parameters which can be modified. Altering these +parameters will affect the spread of the infectious disease in the +simulated population. Name the disease of interest, specify the +parameters of choice (listed below), and select “run simulation”.
+-
+
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+Model: Network SEIR
+
+ -
+Disease: COVID-19
+
+ -
+% of population infected = 0.1
+
+ -
+Probability of exposure = 0.05
+
+ -
+Recovery probability = 0.14
+
+ -
+Incubation days = 7
+
+ -
+Simulation time = 100
+
+ - +Seed = 2023 +
++Running the model +
+
+The above graphic demonstrates launching the application and running +the model with COVID-19 as the disease. After running the simulation, +plots of the distributions of states and the disease’s reproductive +number over time, a model summary, and table of each state’s counts over +time are all displayed.
+++Results +
+In this example, the model of choice is a SEIR Network model. Notice +the day of peak infections occurs on day 12, maxing at about 18,000 +infections. After roughly 40 days, the state’s curves taper off, meaning +that the majority of the population has recovered from the disease. The +reproductive number plot demonstrates that the disease spread rapidly at +the beginning of the simulation, and drastically decreased over the +first 10 days. The model summary returns important information about the +simulation such as the model choice, population size, simulation speed, +disease(s) present, any tool(s) present, and model parameters. The +distribution table displays the counts for each state at baseline and +conclusion. The transition probabilities table displays the probability +of moving between states. For example, the probability that a +susceptible agent remains in the susceptible state is 0.62, with a +probability of moving to the exposed state 0.38. Lastly, the counts +table shows the state’s counts over time, marking the peak infection +count in bold font.
+Articles • epiworldRShiny + + +++ + + + + + + + diff --git a/articles/interventions.html b/articles/interventions.html new file mode 100644 index 0000000..8e9017b --- /dev/null +++ b/articles/interventions.html @@ -0,0 +1,221 @@ + + + + + + + ++ + + + ++ + +++++ +Articles
+++All vignettes
+ + +Implementing Interventions with epiworldRShiny • epiworldRShiny + + + + + + + + + + + + +++ + + + + + + + diff --git a/authors.html b/authors.html index 90e4b76..e6e41f8 100644 --- a/authors.html +++ b/authors.html @@ -25,6 +25,19 @@+ + + + + ++ + + + +++ + + +++ + + +Implementing Interventions with +epiworldRShiny
+Derek +Meyer
+George Vega +Yon
+ +2024-03-26
+ + Source:vignettes/interventions.Rmd++ +interventions.Rmd++Example 2: Implementing vaccine and school closure +
+++Running the application +
+To run the epiworldRShiny application, first ensure that the package +is installed and loaded using the following code. To launch the +application, use call the function, epiworldRShiny().
+++
# install.packages("epiworldRShiny") +library(epiworldRShiny) +#> Loading required package: shiny +# epiworldRShiny()++Model set-up +
+This example features the implementation of the vaccine and school +closure interventions to curb disease spread. All model output can be +interpreted using the same logic from example #1.
+-
+
- +Model: network SEIRD +
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+Disease: COVID-19
+
+ -
+% of population infected = 0.1
+
+ -
+Probability of exposure = 0.05
+
+ -
+Recovery probability = 0.14
+
+ -
+Incubation days = 7
+
+ -
+Simulation time = 100
+
+ - +Vaccine prevalence = 70% +
- +School closure prevalance = 50% +
- +Day of school closure implementation = 7 +
- +Seed = 2023 +
++Running the model +
+
+The above graphic demonstrates launching the application and running +the model with COVID-19 as the disease. To modify the intervention +parameters, scroll to the bottom of the application sidebar, select +“interventions”, and modify as desired. After running the simulation, +plots of the distributions of states and the disease’s reproductive +number over time, a model summary, and table of each state’s counts over +time are all displayed.
+++Results +
+In this example, the model of choice is a SEIRD Network model. Notice +the day of peak infections occurs on day 12, maxing out at 3,882 +infections. Notice in the SEIRD model plot, there are very few exposed, +infected, and deceased agents while the number of susceptible and +recovered agents over the course of the simulation changes rapidly. Due +to the vaccine, which decreases the probability of infection, and school +closures which decrease the probability of exposure to COVID-19, there +are a significantly decreased number of exposed, infected, and deceased +agents.
+++Comparison to absence of interventions +
+
+The above SEIRD model figures demonstrate the distribution of states +over time both with and without interventions present (left and right +figures respectively). With no measure to combat the spread of COVID-19, +the number of exposed, infected, and deceased individuals greatly +increase compared to the model with interventions. The peak number of +infections occurs earlier, on day 11, with a total of 17,616 infections +at the peak, compared to 3,882 infections on day 12 with interventions +present. This indicates that the vaccination and school closing measures +were effective in reducing the number of infections and deaths in this +simulated population.
+ -
+Model: Network SEIR
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