SIS Epidemic Model: Birth-and-Death Markov Chain Approach

TL;DR

This paper models the infected population in the SIS epidemic using a Birth-Death Markov process, deriving analytical and numerical insights into its distribution and dynamics, especially for large populations where it approximates a normal distribution.

Contribution

It introduces a birth-death Markov chain approach to analyze the SIS epidemic model, providing both analytical distribution results and numerical simulations.

Findings

Infected size follows a normal distribution for large populations.

Mean of infected size is proportional to (1 - 1/R) times population size.

Variance of infected size scales with population size divided by R.

Abstract

We are interested in describing the infected size of the SIS Epidemic model using Birth-Death Markov process. The Susceptible-Infected-Susceptible (SIS) model is defined within a population of constant size $M$; the size is kept constant by replacing each death with a newborn healthy individual. The life span of each individual in the population is modelled by an exponential distribution with parameter $\alpha$; and the disease spreads within the population is modelled by a Poisson process with a rate $\lambda_{I}$. $\lambda_{I}=\beta I(1-\frac{I}{M}) $ is similar to the instantaneous rate in the logistic population growth model. The analysis is focused on the disease outbreak, where the reproduction number $R=\frac{\beta}{\alpha} $ is greater than one. As methodology, we use both numerical and analytical approaches. The analysis relies on the stationary distribution for Birth and Death Markov process. The numerical approach creates sample path simulations into order show the infected size dynamics, and the relationship between infected size and $R$. As $M$ becomes large, some stable statistical characteristics of the infected size distribution can be deduced. And the infected size is shown analytically to follow a normal distribution with mean $(1-\frac{1}{R}) M$ and Variance $\frac{M}{R} $.