什么推动了缓解政策？基于网络的美国各州缓解策略纵向分析
What Motivated Mitigation Policies? A Network-Based Longitudinal Analysis of State-Level Mitigation Strategies
AI总结：
本研究通过构建基于政策、政治、地理和病例数据的网络，分析美国各州疫情缓解政策的驱动因素，发现综合政治归属是主要预测指标，且其影响随时间变化。
摘要：
理解哪些因素影响了疫情应对，有助于对美国各州如何处理危机形成更细致的视角。为此，我们构建了多种网络，将各州基于其在缓解政策、政治、地理邻近性和COVID-19病例数据上的相似性联系起来。我们利用这些网络分析了2020年1月至2022年3月期间疫情政策与政治、地理位置和病例负荷之间的相关性。我们表明，一个州应对措施的最佳预测指标是综合政治归属，而非如他人所示仅凭州长归属。此外，我们说明政治归属在2020年6月至2021年6月期间与政策强度高度相关，但在2021年6月之后对政策影响甚微。相比之下，地理邻近性和每日发病率与各州采取相似缓解政策之间并无持续的相关性。
英文摘要：
Understanding which factors informed pandemic response can help create a more nuanced perspective on how each state of the United States handled the crisis. To this end, we create various networks linking states together based on their similarity in mitigation policies, politics, geographic proximity and COVID-19 case data. We use these networks to analyze the correlation between pandemic policies and politics, location and case-load from January 2020 through March 2022. We show that the best predictors of a state's response is an aggregate political affiliation rather than solely governor affiliation as others have shown. Further, we illustrate that political affiliation is heavily correlated with policy intensity from June 2020 through June 2021, but has little impact on policy after June 2021. In contrast, geographic proximity and daily incidence are not consistently correlated with state's having similar mitigation policies.
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