论文标题
建模社会混合和行为对传染病传播的影响：应用于SARS-COV-2
Modelling the impact of social mixing and behaviour on infectious disease transmission: application to SARS-CoV-2
论文作者
论文摘要
关于传染病，社会经济决定因素与差异暴露和易感性密切相关，但是很少有人用标准分室传染病模型来解释它们。这里通过一种新型的分室传染病模型探索了这些关联，该模型按剥夺和年龄分层，说明了人口水平的行为，包括社会混合模式。作为使用完全贝叶斯方法的示例，如果需要，我们的模型可实时地拟合到来自英格兰的UKHSA Covid-19社区测试案例数据。从后样品中估算了包括繁殖数和每日病例发病率的预测的指标。从这个UKHSA数据集中，可以观察到，在大流行的最初阶段，最贫困的群体报告了最多的情况，但是在2021年夏天之后，这种趋势反转。基于合适模型的前向模拟实验表明，这种反转可以通过包括社交混合和测试的个人在内的人口级别的差异来解释，但它却不能解释deplition，而不是deplition decuse sust files。在将来的流行病中，此处概述的方法的重点是社会经济因素，可以确定最有风险的群体，以帮助决策者更好地针对他们的支持。
In regard to infectious diseases socioeconomic determinants are strongly associated with differential exposure and susceptibility however they are seldom accounted for by standard compartmental infectious disease models. These associations are explored here with a novel compartmental infectious disease model which, stratified by deprivation and age, accounts for population-level behaviour including social mixing patterns. As an exemplar using a fully Bayesian approach our model is fitted, in real-time if required, to the UKHSA COVID-19 community testing case data from England. Metrics including reproduction number and forecasts of daily case incidence are estimated from the posterior samples. From this UKHSA dataset it is observed that during the initial period of the pandemic the most deprived groups reported the most cases however this trend reversed after the summer of 2021. Forward simulation experiments based on the fitted model demonstrate that this reversal can be accounted for by differential changes in population level behaviours including social mixing and testing behaviour, but it is not explained by the depletion of susceptible individuals. In future epidemics, with a focus on socioeconomic factors the approach outlined here provides the possibility of identifying those groups most at risk with a view to helping policy-makers better target their support.
