tetano
Editor, Senior Moderator
J Agric Biol Environ Stat
. 2022 Jan 28;1-21.
doi: 10.1007/s13253-022-00487-1. Online ahead of print.
A Spatiotemporal Analytical Outlook of the Exposure to Air Pollution and COVID-19 Mortality in the USA
Sounak Chakraborty[SUP] #[/SUP][SUP] 1 [/SUP], Tanujit Dey[SUP] #[/SUP][SUP] 2 [/SUP], Yoonbae Jun[SUP] #[/SUP][SUP] 3 [/SUP], Chae Young Lim[SUP] #[/SUP][SUP] 3 [/SUP], Anish Mukherjee[SUP] 4 [/SUP], Francesca Dominici[SUP] 5 [/SUP]
Affiliations
Abstract
The world is experiencing a pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The USA is also suffering from a catastrophic death toll from COVID-19. Several studies are providing preliminary evidence that short- and long-term exposure to air pollution might increase the severity of COVID-19 outcomes, including a higher risk of death. In this study, we develop a spatiotemporal model to estimate the association between exposure to fine particulate matter PM2.5 and mortality accounting for several social and environmental factors. More specifically, we implement a Bayesian zero-inflated negative binomial regression model with random effects that vary in time and space. Our goal is to estimate the association between air pollution and mortality accounting for the spatiotemporal variability that remained unexplained by the measured confounders. We applied our model to four regions of the USA with weekly data available for each county within each region. We analyze the data separately for each region because each region shows a different disease spread pattern. We found a positive association between long-term exposure to PM2.5 and the mortality from the COVID-19 disease for all four regions with three of four being statistically significant. Data and code are available at our GitHub repository. Supplementary materials accompanying this paper appear on-line.
Supplementary information: The online version contains supplementary material available at 10.1007/s13253-022-00487-1.
Keywords: Air pollution; Bayesian inference; COVID-19; Markov Chain Monte Carlo; Negative binomial model; Spatial; Spatiotemporal; Zero inflation.
. 2022 Jan 28;1-21.
doi: 10.1007/s13253-022-00487-1. Online ahead of print.
A Spatiotemporal Analytical Outlook of the Exposure to Air Pollution and COVID-19 Mortality in the USA
Sounak Chakraborty[SUP] #[/SUP][SUP] 1 [/SUP], Tanujit Dey[SUP] #[/SUP][SUP] 2 [/SUP], Yoonbae Jun[SUP] #[/SUP][SUP] 3 [/SUP], Chae Young Lim[SUP] #[/SUP][SUP] 3 [/SUP], Anish Mukherjee[SUP] 4 [/SUP], Francesca Dominici[SUP] 5 [/SUP]
Affiliations
- PMID: 35106052
- PMCID: PMC8795746
- DOI: 10.1007/s13253-022-00487-1
Abstract
The world is experiencing a pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), also known as COVID-19. The USA is also suffering from a catastrophic death toll from COVID-19. Several studies are providing preliminary evidence that short- and long-term exposure to air pollution might increase the severity of COVID-19 outcomes, including a higher risk of death. In this study, we develop a spatiotemporal model to estimate the association between exposure to fine particulate matter PM2.5 and mortality accounting for several social and environmental factors. More specifically, we implement a Bayesian zero-inflated negative binomial regression model with random effects that vary in time and space. Our goal is to estimate the association between air pollution and mortality accounting for the spatiotemporal variability that remained unexplained by the measured confounders. We applied our model to four regions of the USA with weekly data available for each county within each region. We analyze the data separately for each region because each region shows a different disease spread pattern. We found a positive association between long-term exposure to PM2.5 and the mortality from the COVID-19 disease for all four regions with three of four being statistically significant. Data and code are available at our GitHub repository. Supplementary materials accompanying this paper appear on-line.
Supplementary information: The online version contains supplementary material available at 10.1007/s13253-022-00487-1.
Keywords: Air pollution; Bayesian inference; COVID-19; Markov Chain Monte Carlo; Negative binomial model; Spatial; Spatiotemporal; Zero inflation.