Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Arabinda Maiti; Qi Zhang; Srikanta Sannigrahi; Suvamoy Pramanik; Suman Chakraborti; Artemi Cerda; Francesco Pilla

doi:10.1016/j.scs.2021.102784

Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States

Sustain Cities Soc. 2021 May:68:102784. doi: 10.1016/j.scs.2021.102784. Epub 2021 Feb 19.

Authors

Arabinda Maiti¹, Qi Zhang^{2

3}, Srikanta Sannigrahi⁴, Suvamoy Pramanik⁵, Suman Chakraborti⁵, Artemi Cerda⁶, Francesco Pilla⁴

Affiliations

¹ Geography and Environment Management, Vidyasagar University, West Bengal, India.
² Department of Earth and Environment, Boston University, Boston, MA, 02215, USA.
³ Frederick S. Pardee Center for the Study of the Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA, 02215, USA.
⁴ School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland.
⁵ Center for the Study of Regional Development, Jawaharlal Nehru University, New Delhi, Delhi, 110067, India.
⁶ Soil Erosion and Degradation Research Group, Department of Geography, Valencia University, Blasco Ibàñez, 28, 46010, Valencia, Spain.

Abstract

Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R² values (for cases, R² = 0.961; for deaths, R² = 0.962), compared to GWR's Adj. R² values (for cases, R² = 0.954; for deaths, R² = 0.954). To inform policy-makers at the nation and state levels, understanding the place-based characteristics of the explanatory forces and related spatial patterns of the driving factors is of paramount importance. Since it is not the first time humans are facing public health emergency, the findings of the present research on COVID-19 therefore can be used as a reference for policy designing and effective decision making.

Keywords: COVID-19; Confounding factors; Income; Migration; Spatial regression; Temporal change.