Abstract
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.
Highlights
As of August 25, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,727,107 and included 170,305, deaths (John Hopkins Coronavirus Resource Center, https://coronavirus.jhu.edu/MAP.HTML), causing a devastating public health and economic crisis
The scalar variables tested for causation of the new cases and deaths from COVID-19 in the US included the number of contact tracing workers per 100,000 people, percent of population above 60 years of age, median income, population density, percentage of African Americans, percentage of Hispanic Americans, percentage of males, employment density, number of points of interests for crowd gathering per 1,000 people, number of staffed hospital beds per 1,000 people, and number of ICU beds per 1,000 people
Employment density, Percentage of African American, population density and Percentage of males showed significant evidence of causing deaths due to COVID-19. percentage of Hispanic Americans
Summary
As of August 25, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,727,107 and included 170,305, deaths (John Hopkins Coronavirus Resource Center, https://coronavirus.jhu.edu/MAP.HTML), causing a devastating public health and economic crisis. Since the number of new cases in the US remains high (36,339 in the US on August 25, 2020) ((John Hopkins Coronavirus Resource Center, https://coronavirus.jhu.edu/MAP.HTML), curbing the spread of COVID-19 is urgently needed [1]. Identifying key factors that most contribute to the rapid spread of COVID-19, and accurately estimating the potential impact of different non-pharmaceutical measures for containing COVID-19 are crucial for planning the most effective interventions to curb the spread of COVID-19 [4]
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