Regional policy coordination of pandemic responses using an iterative mobility-driven algorithm
研究提出一种基于人口流动数据的迭代社区检测算法，用于识别多级政策协调区域，以美国为例证明其比现有行政区划更利于疫情准备。
The COVID-19 experience has shown that horizontal cooperation of non-pharmaceutical interventions across jurisdictions is crucial to combat pandemics. However, the question of how to construct policy coordination regions has not yet received enough attention. In this study, we develop an iterative mobility-driven community detection algorithm based on the modularity function to identify multilevel public policy coordination regions. As a case study, we use movement of people in the United States to identify regions of interconnected locations at different levels. We argue that pre-emptively designed community structures based on mobility can be more appropriate to meet critical preparedness goals than existing jurisdictions.