Author: Burridge, James; Gnacik, Michal
Title: Implied infection-cutting behaviour from a spatial game Cord-id: xwfgx1c4 Document date: 2021_7_22
ID: xwfgx1c4
Snippet: One approach to understand how governmental actions affect people's efforts to cut disease transmission, is to consider the effect of behaviour on case rates. In this paper we present a spatial infection-cutting game, 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 disease cutting behaviour. This is used to in
Document: One approach to understand how governmental actions affect people's efforts to cut disease transmission, is to consider the effect of behaviour on case rates. In this paper we present a spatial infection-cutting game, 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 disease cutting behaviour. This is used to investigate the effects of government intervention, quantify behaviour area by area, and measure the effect of wealth on implied behaviour. We also demonstrate how a delay in people's perception of risk levels can induce behavioural instability, and oscillations in infection rates.
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