Implementing Interventions with epiworldRShiny

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
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.

Derek Meyer