Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

Rongxiang Rui, Maozai Tian, Man-Lai Tang, George To-Sum Ho and Chun-Ho Wu
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Rongxiang Rui: School of Statistics, Renmin University of China, Beijing 100872, China
Maozai Tian: College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi 830011, China
Man-Lai Tang: Department of Mathematics, Statistics and Insurance, Hang Seng University of Hong Kong, Hong Kong, China
George To-Sum Ho: Department of Supply Chain and Information Management, Hang Seng University of Hong Kong, Hong Kong, China
Chun-Ho Wu: Department of Supply Chain and Information Management, Hang Seng University of Hong Kong, Hong Kong, China

IJERPH, 2021, vol. 18, issue 2, 1-18

Abstract: With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

Keywords: columnar density of total atmospheric ozone; COVID-19; maximum temperature; minimum temperature; spatio-temporal multivariate time-series analysis; USA (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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