Public efforts to reduce disease transmission implied from a spatial game

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.

The code to generate all the figures is available at https://github.com/gnacikm/BSIRS_model

References in corpus (4)

• A SIDARTHE Model of COVID-19 Epidemic in Italy
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• COVID-19 and Social Distancing: Disparities in Mobility Adaptation between Income Groups
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