Summary Multi‐compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community‐level micromodel that enables high‐resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.

中文翻译：

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多室传染病模型的综述

摘要 自 20 世纪初以来，多室模型在传染病动态建模中一直发挥着核心作用。它们是一类广泛用于描述流行病演变机制的数学模型。与某些抽样方案相结合，此类机制模型可用于分析公共卫生监测数据，例如评估预防措施（例如社交距离和隔离）的有效性和预测疾病传播模式。本综述从全国宏观力学模型和相关统计分析开始，包括模型规范、估计、推理和预测。然后，它提出了一个社区级微观模型，可以对区域监测数据进行高分辨率分析，为当地政府和居民就重新开放当地商业和个人旅行做出决策提供有用的当前和未来风险信息。只要适当，就会提供软件和脚本来说明算法和计算的数值细节。本文使用密歇根州 2019 年冠状病毒病大流行监测数据作为说明。