Chinese Center for Disease Control and Prevention, National Institute of Environmental Health, Beijing, 10021, China.
Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, Hefei, 230032, China.
Environ Sci Pollut Res Int. 2023 Jul;30(32):79512-79524. doi: 10.1007/s11356-023-27320-7. Epub 2023 Jun 8.
Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.
环境中不同来源的因素可以通过影响病毒传播的扩散来影响 COVID-19 的传播，但这些因素的综合影响几乎没有被考虑到。本研究旨在利用机器学习算法评估气象变量、人口统计因素和政府应对措施对全球城市层面 COVID-19 每日病例的联合影响。随机森林回归模型表明，人口密度是 COVID-19 传播的最关键决定因素，其次是气象变量和应对措施。紫外线辐射和温度是主导气象因素，但与每日病例的关联在不同气候区有所不同。政策应对措施在控制疫情发展方面具有滞后效应，实施更严格的应对措施更有效地控制了疫情，但普遍措施可能不适用于所有气候条件。本研究探讨了人口统计因素、气象变量和政策应对措施在 COVID-19 传播中的作用，为政策制定者提供了证据，即制定预防和准备未来大流行的适当政策应基于当地气候条件、人口特征和社会活动特征。未来的工作应侧重于识别影响 COVID-19 传播的众多因素之间的相互作用。
