Peak Infection Time for a Networked SIR Epidemic with Opinion Dynamics

TL;DR

This paper introduces a coupled SIR epidemic and opinion dynamics model on networks, providing a threshold condition for peak infection time and analyzing how opinions influence epidemic spread and recovery levels.

Contribution

It presents a novel coupled model of epidemic and opinion dynamics with an analytical threshold for peak infection time, supported by simulations.

Findings

Opinions influence the epidemic's peak and recovery levels.

The model's threshold accurately predicts the peak infection time.

Opinions reflect recovered levels post-epidemic.

Abstract

We propose an SIR epidemic model coupled with opinion dynamics to study an epidemic and opinions spreading in a network of communities. Our model couples networked SIR epidemic dynamics with opinions towards the severity of the epidemic, and vice versa. We develop an epidemic-opinion based threshold condition to capture the moment when a weighted average of the epidemic states starts to decrease exponentially fast over the network, namely the peak infection time. We define an effective reproduction number to characterize the behavior of the model through the peak infection time. We use both analytical and simulation-based results to illustrate that the opinions reflect the recovered levels within the communities after the epidemic dies out.