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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rongxiang Rui & Maozai Tian & Man-Lai Tang & George To-Sum Ho & Chun-Ho Wu, 2021. "Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model," IJERPH, MDPI, vol. 18(2), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:774-:d:482317
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    References listed on IDEAS

    as
    1. Jeffrey E. Harris, 2020. "Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults," Review of Economics of the Household, Springer, vol. 18(4), pages 1019-1037, December.
    2. Thomas Kneib & Ludwig Fahrmeir, 2007. "A Mixed Model Approach for Geoadditive Hazard Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 207-228, March.
    3. Jeffrey E. Harris, 2020. "Correction to: Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults," Review of Economics of the Household, Springer, vol. 18(4), pages 1039-1039, December.
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    Cited by:

    1. Peter Congdon, 2022. "A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic," IJERPH, MDPI, vol. 19(11), pages 1-17, May.
    2. Peter Congdon, 2022. "A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates," Journal of Geographical Systems, Springer, vol. 24(4), pages 583-610, October.

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