Department of Mathematics, Stockholm University, Stockholm, Sweden.
Department of Mathematics, University of Rome Tor Vergata, Roma, Italy.
J R Soc Interface. 2022 Jun;19(191):20220128. doi: 10.1098/rsif.2022.0128. Epub 2022 Jun 15.
We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.
我们提出了一个随机传染病模型，以研究各种预防措施（如减少接触和传播的均匀性、接种疫苗、隔离、筛查和接触者追踪）对均质混合社区疾病爆发的影响。该模型基于感染过程，我们通过随机接触和传染性过程来定义，以便每个个体都有独立的感染特征。特别是，我们监测繁殖数和世代时间分布的变化。我们表明，一些干预措施，即均匀减少和接种疫苗，会影响前者而不改变后者，而其他干预措施，如隔离、筛查和接触者追踪，会影响这两个数量。我们提供了对这些数量变化的理论分析，并表明在实践中，世代时间分布的变化可能非常显著，并可能导致繁殖数估计的偏差。该框架因其通用性而捕捉了许多传染病的特性，但特别强调了 COVID-19，我们提供了针对 COVID-19 的数值结果。
