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

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.

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

“…Regarding life style, change in social distancing [ 51 ], increase of space–time clusters [ 52 ], and different sets of neighborhood characteristics [ 53 ] could be identified as risk factors for ND and NC during the COVID-19 pandemic. As to environmental factors, a study indicated temperature and the columnar density of total atmospheric ozone had a strong association with the tendency of COVID-19 spreading in almost all states in the USA [ 54 ]. As for regulations mainly including mobility restrictions and other non-pharmacological interventions, ill-prepared work [ 55 ], facemask shortage [ 56 ], poor traveller screening [ 57 ], forgone care [ 58 ], and population migration [ 59 ] could lead to ineffective prevention and controlling COVID-19.…”

Gis And Remote Sensing: A Review Of Applications To The Study Of The Covid-19 Pandemic

“…This is of great importance for strictly implementing isolation or social distancing rules and early warning and prevention of future outbreaks. After COVID-19 rapidly spread across China and the rest of the world, many studies make use of GIS to detect the spatio-temporal changes in many countries, especially in the worst-affected countries such as the USA (Feng et al 2020;Rui et al 2021;Wang et al 2021), Italy (Giuliani et al 2020;Gross et al 2020;He et al 2020), England (Elson et al 2021;Sartorius et al 2021), South Korea (He et al 2020;Kim and Castro 2020;Lee et al 2020), Iran (He et al 2020), Brazil (Castro et al 2021), Russia (Kuznetsov et al 2020a) and most recently in India (Bag et al 2020;Bhunia et al 2021).…”

“…Feng et al (2020) revealed that GIS can help to effectively characterize spatio-temporal transmission of COVID-19 and its mitigation strategies. Most recently, when analyzing the spread of COVID-19 in the USA, Rui et al (2021) found that the spatio-temporal multivariate time-series model is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Using an endemic-epidemic multivariate timeseries mixed-effects generalized linear model for areal disease counts, Giuliani et al (2020) successfully modeled and predicted the spatio-temporal spread of COVID-19 in Italy.…”

Knowledge, attitudes and practices towards the spread of COVID-19: a health counseling initiative among potential entrepreneurs of FUUAST

“…In their opinion, risk perception and COVID-19 understanding are potential sources of health information in their societies (Alsoghair et al , 2020). In many USA states, the factors such as humidity, maximum temperature, the columnar density of total atmospheric ozone and cloud coverage percentage are strongly related COVID-19 pandemic (Rui et al , 2020). Besides, a tendency analysis demonstrates the mitigation of community-level transmission in the USA.…”

“…As a result, the domain literature demonstrates that the adaptation of knowledge, positive attitudes, precautionary measures, intention to stay at home, mask attitudes, awareness programs regarding the COVID-19 found as the robust measures to prevent the virus (Shah et al , 2020; Raza et al , 2021; Rui et al , 2020; Sumaedi et al , 2021). However, these constructs, i.e.…”