Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and sound policy. Metapopulation systems, which consist of a collection of sub-populations at various locations, pose technical challenges in statistical inference due to nonlinear, stochastic interactions. Difficulties encountered in these methodological issues can obstruct the core scientific questions concerning the link between the mathematical models and the data. Progress in statistically efficient simulation-based inference for partially observed stochastic dynamic systems has enabled the development of statistically rigorous approaches to the analysis of nonlinear but low-dimensional systems. Recently, an algorithm has been developed which enables comparable inference for higher-dimensional models arising in metapopulation systems. The COVID-19 pandemic provides a situation where mathematical models and their policy implications were widely visible, and we revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model limitations, and leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on January 23, 2020 in China was more effective than previously thought. We proceed to recommend statistical analysis standards for future metapopulation system modeling.

翻译：生态学和流行病学中的数学模型必须与观测数据一致，以产生可靠的知识和合理的政策。集合种群系统由不同位置的多个亚种群组成，由于非线性、随机相互作用，在统计推断中面临技术挑战。这些方法论问题中遇到的困难可能阻碍核心科学问题，即关于数学模型与数据之间联系的研究。针对部分观测的随机动态系统，基于模拟的统计高效推断方法取得了进展，使得对非线性但低维系统的分析能够采用统计严谨的方法。最近，一种算法被开发出来，能够对集合种群系统中出现的高维模型进行类似的推断。COVID-19大流行提供了一个数学模型及其政策影响广受关注的场景，我们重新审视了一个在大流行早期用于指导基本流行病学理解的有影响力的集合种群模型。我们的方法支持自我批判性数据分析，使我们能够识别并解决模型局限性，并提出一个统计拟合度和参数可辨识性显著提升的新模型。我们的结果表明，中国于2020年1月23日启动的封锁措施比此前认为的更为有效。最后，我们为未来集合种群系统建模推荐了统计分析标准。