Epidemic Modeling(DES)
Introduction
One of the things I have been trying to play recently with is Discrete Event Simulation(DES). I think it is a powerful tool for validating ideas. In this post, we will look at a toy epidemic model to simulate SIS/SIR models.
In Epidemic modeling, there are two classic models - SIS and SIR models. The models divide the population into different categories corresponding to different stages of the epidemic.
- Susceptible(S): Susceptible individuals can contract the disease.
- Infected(I): Infected individuals have already been contracted the disease.
- Recovered(R): Recovered individuals are recovered from the disease and can not be infected again.
SIS Model
In case of SIS, the main assumption is that an infected person can get infected again after recovering. The state transition diagram looks like:

- $\beta$ is the probability of transitioning from
Susceptible(S)toInfected(I) - $\mu$ is the probability of transitioning from
Infected(I)toSusceptible(S)
SIR Model
In case of SIR, the main assumption is that an infected person can not get infected again. The state transition diagram looks like:

- $\beta$ is the probability of transitioning from
Susceptible(S)toInfected(I) - $\mu$ is the probability of transitioning from
Infected(I)toRecovered(R)
SIS Simulation
We will have a generic Simulation class Continue reading











