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

Chun-Ho Wu,Maozai Tian,George To-Sum Ho,Man-Lai Tang,Rongxiang Rui

doi:10.3390/ijerph18020774

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.

Highlights

IntroductionThe pandemic due to the coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1], was the most disastrous incident in 2020, causing millions of deaths and resulting in economic activity worldwide falling sharply
We are mainly interested in the cumulative positive cases, as this will be used for the calculation of the number of daily increased COVID-19 cases in the USA
We find that maximum temperature (MaT), minimum temperature (MiT), and columnar density of total atmospheric ozone (CDTAO) have statistically significant associations with daily confirmed cases in almost all the states in America, based on both the Kendall and Spearman-rank correlation tests

Summary

Introduction

The pandemic due to the coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1], was the most disastrous incident in 2020, causing millions of deaths and resulting in economic activity worldwide falling sharply. According to the latest report of the World Health Organization (WHO), the cumulative cases around the world reached 28,637,952 and the cumulative deaths were 917,417 as of 13 September 2020 The World Bank suggested that most countries would be expected to suffer 4.0/).

Results

Discussion

Conclusion