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

15 Pages Posted: 13 Jan 2021

See all articles by Lingbo Liu

Lingbo Liu

Wuhan University - School of Urban Design

Tao Hu

Harvard University

Shuming Bao

China Data Center

Hao Wu

Wuhan University - School of Urban Design

Zhenghong Peng

Wuhan University - School of Urban Design

Ru Wang

Wuhan University - School of Urban Design

Date Written: January 7, 2021

Abstract

Background: Human mobility among geographic units is a possible cause of the widespread transmission of COVID-19 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 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 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 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 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.

Note: Funding: National Key Research and Development Project (2019YFB2101803) National Natural Science Foundation of China (52078390) Wuhan University Experiment Technology Project Funding

Declaration of Interests: The authors declare that they have no known competing financial interests of personal relationship that could have appeared to influence the work reported in this paper.

Keywords: COVID-19; Moran’s I index; K-means clustering; Spatiotemporal interaction effects; Spatial Lag Model; SIR

JEL Classification: I14; I18

Suggested Citation

Liu, Lingbo and Hu, Tao and Bao, Shuming and Wu, Hao and Peng, Zhenghong and Wang, Ru, The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States (January 7, 2021). Available at SSRN: https://ssrn.com/abstract=3761556 or http://dx.doi.org/10.2139/ssrn.3761556

Lingbo Liu (Contact Author)

Wuhan University - School of Urban Design ( email )

SCHOOL OF URBAN DESIGN
WUHAN UNIVERSITY
WUHAN, Hubei 430072
China
68773062 (Phone)

Tao Hu

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Shuming Bao

China Data Center ( email )

330 Packard St
Ann Arbor, MI 48104
United States
734-647-9610 (Phone)
734-763-0335 (Fax)

HOME PAGE: http://chinadatacenter.org

Hao Wu

Wuhan University - School of Urban Design ( email )

SCHOOL OF URBAN DESIGN
WUHAN UNIVERSITY
WUHAN, Hubei 430072
China

Zhenghong Peng

Wuhan University - School of Urban Design ( email )

SCHOOL OF URBAN DESIGN
WUHAN UNIVERSITY
WUHAN, Hubei 430072
China

Ru Wang

Wuhan University - School of Urban Design ( email )

SCHOOL OF URBAN DESIGN
WUHAN UNIVERSITY
WUHAN, Hubei 430072
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
33
Abstract Views
426
PlumX Metrics