Steyn Nicholas, Parag Kris V, Thompson Robin N, Donnelly Christl A
Department of Statistics, University of Oxford, Oxford, UK.
MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Stat Med. 2025 Aug;44(18-19):e70204. doi: 10.1002/sim.70204.
Renewal models are widely used in statistical epidemiology as semi-mechanistic models of disease transmission. While primarily used for estimating the instantaneous reproduction number, they can also be used for generating projections, estimating elimination probabilities, modeling the effect of interventions, and more. We demonstrate how simple sequential Monte Carlo methods (also known as particle filters) can be used to perform inference on these models. Our goal is to acquaint a reader who has a working knowledge of statistical inference with these methods and models and to provide a practical guide to their implementation. We focus on these methods' flexibility and their ability to handle multiple statistical and other biases simultaneously. We leverage this flexibility to unify existing methods for estimating the instantaneous reproduction number and generating projections. A companion website SMC and epidemic renewal models provides additional worked examples, self-contained code to reproduce the examples presented here, and additional materials.
更新模型作为疾病传播的半机制模型在统计流行病学中被广泛使用。虽然主要用于估计瞬时再生数，但它们也可用于生成预测、估计消除概率、模拟干预效果等。我们展示了如何使用简单的序贯蒙特卡罗方法（也称为粒子滤波器）对这些模型进行推断。我们的目标是让具有统计推断实用知识的读者熟悉这些方法和模型，并为其实现提供实用指南。我们关注这些方法的灵活性以及它们同时处理多种统计偏差和其他偏差的能力。我们利用这种灵活性来统一现有的估计瞬时再生数和生成预测的方法。一个配套网站“SMC与流行病更新模型”提供了更多的实例、用于重现此处示例的独立代码以及其他材料。
