Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China.
Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
Front Public Health. 2023 Aug 3;11:1198973. doi: 10.3389/fpubh.2023.1198973. eCollection 2023.
Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.
鉴于 COVID-19 疫苗和其他疗法的有效性受到不断出现的变异株的极大限制，非药物干预措施已被作为全球抗击 COVID-19 大流行的主要控制策略。然而，长时间实施严格的干预措施不可避免地会对经济造成影响。许多国家都面临着如何在遏制 COVID-19 进一步传播的同时，采取适当的政策措施促进社会经济复苏的困境。为了解决这个多目标决策问题，我们通过向量自回归模型和 Toda-Yamamoto 格兰杰因果检验，研究了政策、流动性模式和病毒传播之间的潜在时变动态和关联。我们的研究结果揭示了各种传播和人类流动变量之间存在时间滞后效应和格兰杰因果关系。我们进一步评估了现有的 COVID-19 控制措施的有效性，并通过帕累托最优和遗传算法探索了美国各州在公共卫生和社会经济复苏之间取得平衡的潜在最优策略。结果突出了紧急状态声明、佩戴口罩和关闭酒吧的联合作用，并强调了针对不同州和不同流行病学传播阶段制定定制化策略的必要性。我们的框架使政策制定者能够制定更精细的 COVID-19 策略设计，并可以扩展到其他国家，以借鉴大流行应对方面的最佳实践。
