table4_Causal Analysis of Health Interventions and Environments for Influencing the Spread of COVID-19 in the United States of America.xlsx

Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1,000 people, workplaces, tests done/1,000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1,000 people, mobility trends for places of residence (residential), retail and test capacity were the popular significant risk factor for the new cases of COVID-19, and that active cases/1,000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1,000 people, transit stations, mobility trends (transit), tests done/1,000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the popular significant risk factor for the deaths of COVID-19. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

鉴于目前尚无有效疫苗与特效治疗药物，非药物干预措施（non-pharmaceutical interventions）是遏制新冠病毒（COVID-19）传播的核心手段。准确评估各类非药物干预措施的遏制效果、识别影响新冠传播的风险因素，对于制定最优干预方案以遏制病毒传播、降低病亡率至关重要。本研究针对标量因子采用基于加性模型的双变量因果发现方法，针对时间序列因子采用多元格兰杰因果检验（multivariate Granger causality tests），对2020年3月5日至2020年8月25日期间的美国实验室确诊新冠病例监测数据、马里兰大学数据集（University of Maryland Data, UMD）以及谷歌移动性数据（Google mobility data）开展分析，以评估社会-生物因素、经济状况、谷歌移动性指数以及病毒检测率对新冠新增病例数与病亡数的贡献程度。研究发现，每千人活跃病例数、工作场所人流量、每千人检测量、输入性新冠病例数、失业率、每千人失业申领数、居住地移动趋势（residential）、零售业相关指标及检测能力，是影响新冠新增病例的主要显著风险因素；而每千人活跃病例数、工作场所人流量、居住地移动趋势、失业率、输入性新冠病例数、每千人失业申领数、公共交通站点人流量、公共交通移动趋势（transit）、每千人检测量、杂货店相关指标、检测能力、零售业相关指标、消费变化率及居家办公比例，则是影响新冠病亡数的主要显著风险因素。研究观察到，在佛罗里达州（FL），暂无任何指标被证实可有效缓解新冠疫情；而在亚利桑那州（AZ）、纽约州（NY）与德克萨斯州（TX），仅有少量指标被证实可降低新冠新增病例数。本研究结果显示，绝大多数非药物干预措施对减缓病毒传播、降低病亡率具有显著作用，且仍需落实卫生干预措施以进一步遏制新冠疫情的扩散。