table3_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.

鉴于目前尚无获批的潜在疫苗与有效治疗药物，非药物干预措施是遏制新冠病毒（COVID-19）传播的主要手段。准确评估各类非药物干预措施的防控潜力，并明确影响新冠疫情传播的风险因素，对于制定最有效的干预方案以遏制疫情扩散、降低病亡率至关重要。本研究针对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（University of Maryland）数据集（UMD数据）以及谷歌移动趋势数据，分别采用针对标量因子的基于加性模型的双变量因果发现方法，与针对时间序列因子的多变量格兰杰（Granger）因果检验，以评估社会-生物学因素、经济状况、谷歌移动指数以及病毒检测率对新冠新增确诊病例数与病亡人数的贡献。研究发现，每千人活跃病例数、工作场所活跃度、每千人检测量、输入性新冠病例、失业率、每千人失业申领数、居住地移动趋势（居住类）、零售业态以及检测能力，均为影响新冠新增确诊病例的显著通用风险因子；而每千人活跃病例数、工作场所活跃度、居住类移动趋势、失业率、输入性新冠病例、每千人失业申领数、交通站点活跃度、交通类移动趋势、每千人检测量、商超业态、检测能力、零售业态、消费变化率以及居家办公占比，则为影响新冠病亡人数的显著通用风险因子。进一步分析显示，佛罗里达州（FL）未出现任何可显著缓解新冠疫情的干预指标，而亚利桑那州（AZ）、纽约州（NY）与得克萨斯州（TX）仅少数指标可有效降低新冠新增确诊病例数。本研究结果表明，绝大多数非药物干预措施对延缓新冠病毒传播、降低病亡率具有显著效果，同时仍需依托卫生干预手段以进一步遏制疫情。