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从空间博弈中推断出的减少疾病传播的公共努力。

Public efforts to reduce disease transmission implied from a spatial game.

作者信息

Burridge James, Gnacik Michał

机构信息

School of Mathematics and Physics, Lion Gate Building, Lion Terrace, University of Portsmouth, Portsmouth, United Kingdom.

出版信息

Physica A. 2022 Mar 1;589:126619. doi: 10.1016/j.physa.2021.126619. Epub 2021 Nov 25.

DOI:10.1016/j.physa.2021.126619
PMID:34848918
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8612759/
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.

摘要

理解人们为减少疾病传播所做努力的一种方法，是考虑行为对病例率的影响。在本文中，我们提出了一个公共行为的空间感染减少博弈模型，它在形式上等同于一个与SIRS疾病动态耦合的霍普菲尔德神经网络。行为博弈参数可以根据新冠疫情活跃病例数的地理时间序列进行精确校准，从而得出行为的隐含空间历史。这被用于研究政府干预的效果，逐区域量化行为，并衡量财富对行为的影响。我们还展示了人们对风险水平认知的延迟如何导致行为不稳定以及感染率的波动。

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