Public efforts to reduce disease transmission implied from a spatial game

TL;DR

This paper introduces a spatial game model linked to disease dynamics to analyze how public behavior influences disease transmission, calibrated with Covid-19 data, revealing effects of interventions, wealth, and perception delays.

Contribution

It presents a novel spatial infection-reducing game model equivalent to a neural network, calibrated with real Covid-19 data to study behavioral impacts on disease spread.

Findings

Behavioral delays can cause oscillations in infection rates.

Government interventions significantly alter spatial behavior patterns.

Wealth influences individual compliance with health measures.

Abstract

One approach to understand people's efforts to reduce disease transmission, is to consider the effect of behaviour on case rates. In this paper we present a spatial infection-reducing game model of public behaviour, formally equivalent to a Hopfield neural network coupled to SIRS disease dynamics. Behavioural game parameters can be precisely calibrated to geographical time series of Covid-19 active case numbers, giving an implied spatial history of behaviour. This is used to investigate the effects of government intervention, quantify behaviour area by area, and measure the effect of wealth on behaviour. We also demonstrate how a delay in people's perception of risk levels can induce behavioural instability, and oscillations in infection rates.