This article introduces epidemia, an R package for Bayesian, regression-oriented modeling of infectious diseases. The implemented models define a likelihood for all observed data while also explicitly modeling transmission dynamics: an approach often termed as semi-mechanistic. Infections are propagated over time using renewal equations. This approach is inspired by self-exciting, continuous-time point processes such as the Hawkes process. A variety of inferential tasks can be performed using the package. Key epidemiological quantities, including reproduction numbers and latent infections, may be estimated within the framework. The models may be used to evaluate the determinants of changes in transmission rates, including the effects of control measures. Epidemic dynamics may be simulated either from a fitted model or a prior model; allowing for prior/posterior predictive checks, experimentation, and forecasting.
翻译:本条引入了流行性贫血,这是巴伊西亚病的R包,以回归为导向的传染病模型。实施的模式界定了所有观测数据的可能性,同时明确模拟传播动态:一种通常称为半机械性的方法。感染是随着时间的推移使用更新方程式传播的。这一方法受到自刺激、连续时间点过程的启发,如霍克斯过程。使用这一包可以完成各种推论性任务。关键流行病学数量,包括生殖数和潜在感染,可以在框架内估算。这些模型可用于评估传播率变化的决定因素,包括控制措施的效果。流行病动态可以从一个合适的模型或一个以前的模型中模拟;允许事先/以后的预测检查、实验和预测。