Université du Québec à Montréal, Département de mathématiques, Montréal H2X 3Y7, Québec, Canada.
Université du Québec à Montréal, Département de mathématiques, Montréal H2X 3Y7, Québec, Canada.
J Theor Biol. 2022 Sep 21;549:111210. doi: 10.1016/j.jtbi.2022.111210. Epub 2022 Jul 3.
In this paper, we propose an easy to implement generalized linear models (GLM) methodology for estimating the basic reproduction number, R, a major epidemic parameter for assessing the transmissibility of an infection. Our approach rests on well known qualitative properties of the classical SIR and SEIR systems for large populations. Moreover, we assume that information at the individual network level is not available. In inference we consider non homogeneous Poisson observation processes and mainly concentrate on epidemics that spread through a completely susceptible population. Further, we examine the performance of the estimator under various scenarios of relevance in practice, like partially observed data. We perform a detailed simulation study and illustrate our approach on Covid-19 Canadian data sets. Finally, we present extensions of our methodology and discuss its merits and practical limitations, in particular the challenges in estimating R when mitigation measures are applied.
在本文中，我们提出了一种易于实现的广义线性模型（GLM）方法，用于估计基本繁殖数 R，这是评估感染传播能力的一个主要传染病参数。我们的方法基于大人群中经典 SIR 和 SEIR 系统的众所周知的定性特性。此外，我们假设个体网络层面的信息不可用。在推断中，我们考虑非齐次泊松观测过程，主要集中在通过完全易感人群传播的传染病上。此外，我们还在各种与实际相关的场景下，如部分观测数据，检验了估计器的性能。我们进行了详细的模拟研究，并在加拿大新冠疫情数据集上展示了我们的方法。最后，我们提出了我们的方法的扩展，并讨论了它的优点和实际限制，特别是在应用缓解措施时估计 R 所面临的挑战。
