Equity Model

Derek Meyer

George Vega Yon

2024-06-04

Source: vignettes/equity_model.Rmd

Example 3: Simulating a SEIR Equity 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

This example features the SEIR equity model. This model is unique because it accounts for demographic diversity in a population, such as race, gender, and age. This allows for the comparison of disease spread among different demographics. For example, the user can specify the proportions of different age groups to simulate an older, younger, or balanced population.

  • Model: SEIR equity model
  • Disease: COVID-19
  • % of population infected = 0.1
  • Probability of exposure = 0.05
  • Recovery probability = 0.14
  • Incubation days = 7
  • Simulation time = 100
  • % Hispanic = 0.3
  • % Female = 0.52
  • Age distribution <20 = 0.3
  • Age distribution <60, >20 = 0.3

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 based on the specified demographics 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 equity model. Notice the first section of output, displaying the total number of infected individuals in the Hispanic and non-Hispanic demographics. According to the simulation, more Hispanic agents were infected with COVID-19 than the non-Hispanic group. This figure further stratifies by sex and age group. Notice that there aren’t any significant differences in total number of infected among females and males, indicating that the risk of infection for females is about the same for males. When analyzing age group, it is clear that those 60 years or older had the most infections out of any other age group.

This model leads to the conclusion that agents who are Hispanic and 60 years or older have the highest occurrence of COVID-19 infections with very little distinction between sex. This information can be helpful to public health officials in informing public policy targeted towards benefiting select demographics.