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    The Spatiotemporal Interaction Effect of COVID-19 MESHD Transmission in the United States

    Authors: Lingbo Liu; Tao Hu; Shuming Bao; Hao Wu; Zhenghong Peng; Ru Wang

    doi:10.21203/rs.3.rs-143786/v1 Date: 2021-01-08 Source: ResearchSquare

    Background: Human mobility among geographic units is a possible cause of the widespread transmission of COVID-19 MESHD across regions. Due to the pressure of epidemic control and economic recovery, the states of the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 MESHD transmission among counties in each state is critical to formulating the epidemic policies.Methods: The study utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (except the District of Colombia) with the daily new cases at the county level from Jan 22, 2020, to August 20, 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu MESHD model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation and stepwise OLS regression with socioeconomic factors.Results: The K-means clustering divided the time-varying spatial autocorrelation curves of 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu MESHD were significantly positively correlated with median age, population density, and the proportion of international immigrants and the highly educated population, but negatively correlated with the birth rate. The voting rate for Donald Trump in the 2016 U.S. presidential election showed a weak negative correlation. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and the highly educated population proportion.Interpretation: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 MESHD transmission among counties. All states should provide more protection and support for the low-income population, high-density populated states need to strengthen regional mobility restrictions, and the highly educated population should reduce unnecessary regional movement and strengthen self-protection. 

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MeSH Disease
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SARS-CoV-2 Proteins


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