Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, B-3590 Diepenbeek, Belgium.
Epidemiol Infect. 2010 Jun;138(6):802-12. doi: 10.1017/S0950268809990781. Epub 2009 Sep 21.
The force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data.
感染率，描述的是易感者获得感染的速度，是用于评估传染病负担、传染病预防的有效性和成本效益的模型中的一个关键参数。自 1934 年 Muench 首次提出用当前状态数据来构建估计感染率的催化模型以来，整整 75 年过去了，几位作者通过更先进的统计方法来估计这一参数，同时将这些方法应用于血清流行率和报告的发病率/病例通报数据。在本文中，我们将进行历史回顾，讨论 Muench 工作的相关性，并通过对风疹和细小病毒 B19 两种空气传播感染的疫苗接种前血清学调查数据进行说明，解释一系列新方法。我们还提供了一些指导，说明在给定特定数据集的情况下，应使用哪种（或哪些）方法来估计感染率。
