Howard S C, Donnelly C A
Wellcome Trust Centre for the Epidemiology of Infectious Disease, Department of Zoology, University of Oxford, UK.
J Epidemiol Biostat. 2000;5(3):161-8.
A method was developed for stochastically reconstructing the pattern of infection from observed epidemic data. This allowed for estimation of a time-dependent force of infection, or transmission rate, during an epidemic.
A discrete-time mechanistic model was used to describe the spread of infection and a stochastic procedure, which utilised the latent and infectious period distributions, was used to reconstruct the dates of infection, becoming infectious and removal from the given data. The four equations describing the model were then solved to obtain least squares estimates of the transmission rate and the basic reproduction number (R0) throughout the epidemic. This process was repeated in order to assess the variability in these key epidemiological parameters. The stochastic epidemic reconstruction procedure was developed to account for changes in the distribution of the survival period over the course of the epidemic and adapted for application to epidemic data where not all infected individuals have yet been observed as cases.
The method was applied to a set of epidemic data from an outbreak of classical swine fever in Pakistan. Constant and time-varying estimates of the transmission rate were derived and compared. There was some evidence to suggest that the force of infection varied over time.
The method described can be applied to data from epidemics where observations are incomplete. The confidence limits obtained for the estimated force of infection provide a means of assessing the evidence for time variation in this parameter.
开发了一种从观察到的疫情数据中随机重建感染模式的方法。这使得能够估计疫情期间随时间变化的感染力或传播率。
使用离散时间机制模型描述感染传播，并采用一种利用潜伏期和传染期分布的随机程序，从给定数据中重建感染日期、具有传染性的日期以及清除日期。然后求解描述该模型的四个方程，以获得整个疫情期间传播率和基本再生数（R0）的最小二乘估计值。重复此过程以评估这些关键流行病学参数的变异性。开发随机疫情重建程序是为了考虑疫情过程中存活期分布的变化，并适用于并非所有感染个体都已作为病例被观察到的疫情数据。
该方法应用于巴基斯坦一次经典猪瘟疫情的一组疫情数据。得出并比较了传播率的固定估计值和随时间变化的估计值。有证据表明感染力随时间变化。
所描述的方法可应用于观察不完整的疫情数据。为估计的感染力获得的置信限提供了一种评估该参数随时间变化证据的方法。
