PDF(Pubmed)
Abstract：
During the SARS-CoV-2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are mis-specified. We introduce an SIR-type model with the infected population structured by \'infected age\', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly mis-specify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb-14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty. This manuscript was submitted as part of a theme issue on \"Modelling COVID-19 and Preparedness for Future Pandemics\".
摘要：
在SARS-CoV-2大流行期间，流行病模式一直是决策的核心。公共卫生对策是由基于模型的预测和推断形成的，特别是与各种非药物干预措施的影响有关。伴随着这一点的是对模型性能的审查，模型假设，以及不确定性被纳入和呈现的方式。这里我们考虑一个人口水平的模型，专注于如何模拟代表宿主传染性和感染至死亡时间的分布，特别是如果这些分布是错误指定的，则推断的流行病特征的影响。我们引入了一个SIR型模型，其感染人群由“感染年龄”构成，即自第一次被感染以来的天数，能够合并与临床数据一致的分布的配方。我们证明了基于更简单的模型的推断，没有感染年龄，隐式地错误指定这些分布，导致与决策相关的推断数量出现重大错误，例如繁殖数量和干预措施的影响。我们通过贝叶斯方法考虑不确定性量化，针对合成和真实数据实施此操作，重点是2020年2月15日至7月14日期间的英国数据，并强调误导忽视不确定性的情况。这份手稿是作为“COVID-19建模和未来流行病准备”主题的一部分提交的。
