Sayers Bruce McA, Angulo Juan
Imperial College, London, UK.
Scand J Infect Dis. 2005;37(1):55-60. doi: 10.1080/00365540410026077.
A new explanatory model for epidemic analysis is presented; it has a knowledge based component and a probabilistic computational component. The former assembles details of household characteristics, social networks and connectivity in the community--'knowledge'--which is used to determine the structure of the computational component. The latter links individuals and households through statistically-defined opportunities for contacts and, by repeated trials, determines an average longitudinal time course (epidemic curve) of the simulated infection as it spreads through the community from inception to extinction of the epidemic. The model thus aims to describe the epidemic itself, rather than any abstraction of it. In application to a 1955-56, self-contained epidemic of an SIR disease, variola minor, the model generates 1 dominant longitudinal pattern that matches closely the epidemic curve of observed daily case rates; it is suggested that other patterns indicate different ways in which the epidemic might have evolved. The model can be used to show how differing community characteristics would affect the simulated epidemic.
提出了一种用于疫情分析的新解释模型；它有一个基于知识的组件和一个概率计算组件。前者收集家庭特征、社会网络和社区连通性的细节——“知识”——用于确定计算组件的结构。后者通过统计定义的接触机会将个体和家庭联系起来，并通过反复试验，确定模拟感染从疫情开始到结束在社区中传播时的平均纵向时间进程（疫情曲线）。该模型旨在描述疫情本身，而非其任何抽象形式。在应用于1955 - 1956年一种SIR疾病——轻型天花的自包含疫情时，该模型生成了1种占主导地位的纵向模式，与观察到的每日病例率疫情曲线紧密匹配；有人认为其他模式表明了疫情可能演变的不同方式。该模型可用于展示不同的社区特征将如何影响模拟疫情。
