The outcome of an epidemic outbreak can be critically shaped by the collective behavioural response of the population. Likewise, individual decision-making is highly influenced by the overwhelming pressure of epidemic spreading. However, existing models lack the ability to capture this complex interdependence over the entire course of the epidemic. We introduce a novel parsimonious network model, grounded in evolutionary game theory, in which decision-making and epidemics co-evolve, shaped by an interplay of factors mapped onto a minimal set of model parameters ---including government-mandated interventions, socio-economic costs, perceived infection risks and social influences. This interplay gives rise to a range of characteristic phenomena that can be captured within this general framework, such as sustained periodic outbreaks, multiple epidemic waves, or prompt behavioural response ensuring a successful eradication of the disease. The model's potentialities are demonstrated by three case studies based on real-world gonorrhoea, 1918--19 Spanish flu and COVID-19.
翻译:同样,个别决策受到流行病蔓延的压倒性压力的极大影响;然而,现有的模型缺乏能力,无法捕捉整个流行病过程的这种复杂的相互依存关系;我们采用了基于进化游戏理论的新颖的混乱网络模式,在这个模式中,决策和流行病的共同变化是由一系列因素的相互作用所形成的,这些因素被映射在一套最低限度的模型参数上 -- -- 包括政府授权的干预措施、社会经济成本、已察觉的感染风险和社会影响 -- -- 包括政府授权的干预、社会经济成本、已觉察到的感染风险和社会影响。这种相互作用产生了一系列可在此总体框架内捕捉到的特有现象,例如持续的定期爆发、多重流行病波浪或迅速的行为反应,以确保成功地消灭这一疾病。基于现实世界淋病、1918-19年西班牙流感和COVID-19的三项案例研究证明了该模式的潜力。