Calculating the Final Size of an Epidemic in R

Overview

The R package finalsize

What Is the Final Epidemic Size?

In an epidemic, we often want to know: How many people will eventually get infected?

This is called the final epidemic size. Final size focuses on cumulative impact, not timing.

Why is the Final Epidemic Size Important?

The finalsize package

finalsize implements methods that: Take an estimate of \(R_0\) then return the proportion infected in each group over the epidemic

What exactly does the package do?

How does changing R₀ affect final epidemic size?

We will compare two scenarios:

• Scenario A: R₀ = 1.3

• Scenario B: R₀ = 2.0

Everything else stays the same.

Scenario A: Lower transmission (\(R_0 = 1.3\))

library(finalsize)

fs_low <- final_size(1.3)
fs_low

    demo_grp   susc_grp susceptibility p_infected
1 demo_grp_1 susc_grp_1              1  0.4229714

Scenario B (Higher R₀)

library(finalsize)
fs_high <- final_size(2.0)
fs_high

    demo_grp   susc_grp susceptibility p_infected
1 demo_grp_1 susc_grp_1              1  0.7968124

Comparing Scenario A and Scenario B

• Scenario A (R₀ = 1.3):
  About 42.3% infected

• Scenario B (R₀ = 2.0):
  About 79.7% infected

• A relatively small increase in R₀ leads to a much larger final epidemic size.

Comparing the R₀

The End! Thanks!