Modifying the Network-Based Stochastic SEIR Model to Account for Quarantine

TL;DR

This paper introduces a modified network-based stochastic SEIR model that incorporates quarantine effects and asymptomatic cases, providing insights into epidemic dynamics through simulation of key parameters.

Contribution

It presents a novel modification to the SEIR model to include quarantine and asymptomatic individuals, enhancing epidemic modeling accuracy.

Findings

Transmission rate and infectious period significantly influence epidemic severity.

Asymptomatic proportion has a lesser impact on epidemic outcomes.

Model simulations identify key parameters affecting disease spread.

Abstract

In this article, we present a modification to the network-based stochastic SEIR epidemic model which allows for modifications to the underlying contact network to account for the effects of quarantine. We also discuss the changes needed to the model to incorporate situations where some proportion of the individuals who are infected remain asymptomatic throughout the course of the disease. Using a generic network model where every potential contact exists with the same common probability, we conduct a simulation study in which we vary four key model parameters (transmission rate, probability of remaining asymptomatic, and the mean lengths of time spent in the Exposed and Infectious disease states) and examine the resulting impacts on various metrics of epidemic severity, including the effective reproduction number. We find that the mean length of time spent in the Infectious state and the transmission rate are the most important model parameters, while the mean length of time spent in the Exposed state and the probability of remaining asymptomatic are less important.