论文标题
COVID-19的个人水平建模19
Individual-level Modeling of COVID-19 Epidemic Risk
论文作者
论文摘要
持续的Covid-19大流行要求采取多方面的公共卫生反应，其中包括互补的干预措施，以控制疾病的传播，同时开发了疫苗和疗法。这些干预措施中的许多干预措施都需要通过流行病风险预测来告知，包括症状，接触模式和环境因素。在这里，我们提出了一种基于个体级别模型（ILM）的新型概率形式主义，该形式为个人级别的模型（ILM）提供了严格的公式，以供个人感染的可能性，可以通过适用于在人群水平定义的隔室模型上应用的最大似然估计（MLE）进行参数化。我们描述了一种方法，其中将实时收集的单个数据与总体案例计数集成在一起，以更新感染易感性作为个体风险因素的函数的预测指标。
The ongoing COVID-19 pandemic calls for a multi-faceted public health response comprising complementary interventions to control the spread of the disease while vaccines and therapies are developed. Many of these interventions need to be informed by epidemic risk predictions given available data, including symptoms, contact patterns, and environmental factors. Here we propose a novel probabilistic formalism based on Individual-Level Models (ILMs) that offers rigorous formulas for the probability of infection of individuals, which can be parameterised via Maximum Likelihood Estimation (MLE) applied on compartmental models defined at the population level. We describe an approach where individual data collected in real-time is integrated with overall case counts to update the a predictor of the susceptibility of infection as a function of individual risk factors.
