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Preprint em Inglês | medRxiv | ID: ppmedrxiv-20203505

RESUMO

As of August 27, 2020, the number of cumulative cases of COVID-19 in the US exceeded 5,863,363 and included 180,595 deaths, thus causing a serious public health crisis. Curbing the spread of Covid-19 is still urgently needed. 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/1000 people, workplaces, tests done/1000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1000 people, mobility trends for places of residence (residential), retail and test capacity were the most significant risk factor for the new cases of COVID-19 in 23, 7, 6, 5, 4, 2, 1 and 1 states, respectively, and that active cases/1000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1000 people, transit stations, mobility trends (transit) , tests done/1000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the most significant risk factor for the deaths of COVID-19 in 17, 10, 4, 4, 3, 2, 2, 2, 1, 1, 1, 1 states, respectively. 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.

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