Exploring the risk factors of COVID‐19 Delta variant in the United States based on Bayesian spatio‐temporal analysis

Abstract

The transmission of coronavirus disease‐2019 (COVID‐19) epidemic is a global emergency, which is worsened by the genetic mutations of SARS‐CoV‐2. However, till date, few statistical studies have researched the COVID‐19 spread patterns in terms of the variant cases. Hence, this paper aims to explore the associated risk factors of Delta variant, the most contagious strain of COVID‐19. The study collected the state‐level COVID‐19 Delta variant cases in the United States during a 12‐week period and included potential environmental, socioeconomic, and public prevention factors as independent variables. Instead of regarding the covariate effects as constant, this paper proposes a flexible Bayesian hierarchical model with spatio‐temporally varying coefficients to account for data heterogeneity. The method enables us to cluster the states into distinctive groups based on the temporal trends of the coefficients and simultaneously identify significant risk factors for each cluster. The findings contribute novel insight into the dynamics of covariate effects on the COVID‐19 Delta variant over space and time, which could help the government develop targeted prevention measures for vulnerable regions based on the selected risk factors.

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia

“…The dynamics of COVID-19 are very complex and the literature shows a very wide range of different factors influencing them. For example, demographic factors such as ethnicity and immigration status, environmental factors such as air pollution, temperature and humidity, healthcare resources such as the number of medical practitioners and hospital beds or specific containment strategies such as lockdown have been identified as drivers of the pandemic 5 , 15 , 61 . Many of these indicators are not easily available in Catalonia, especially at the ABS level.…”

“…Since the development and introduction of COVID-19 vaccines, extensive work has been carried out to evaluate the vaccine’s efficacy and effectiveness and show its protective effect against COVID-19, particularly against severe disease 11 – 14 . Fewer studies have examined the role of COVID-19 vaccination at the local level using spatio-temporal models 15 , 16 . These studies find a significant impact of vaccination in the role of containing COVID-19 incidence at the local level.…”

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

“…Bayesian spatiotemporal models have been widely utilized for COVID-19 analysis and forecasting globally, including in the United States [100][101][102], England [103,104], Spain, Italy, Germany, Sweden [87,[105][106][107], Africa [108], and in some regional areas such as the West Java Province, Indonesia [109], and the Greater Seoul Area, Korea [110]. As an illustration, Nazia et al [111] applied a Bayesian hierarchical spatiotemporal model to assess the COVID-19 risk.…”