Dataset Information

The effects of increased pollution on COVID-19 cases and deaths.

ABSTRACT: The SARS-COV-2 virus, also known as the coronavirus, has spread around the world. A growing literature suggests that exposure to pollution can cause respiratory illness and increase deaths among the elderly. However, little is known about whether increases in pollution could cause additional or more severe infections from COVID-19, which typically manifests as a respiratory infection. During the pandemic, the Environmental Protection Agency (EPA) rolled back enforcement of environmental regulation, causing an increase in pollution in counties with more TRI sites. We use the variation in pollution and a difference in differences design to estimate the effects of increased pollution on county-level COVID-19 deaths and cases. We find that counties with more Toxic Release Inventory (TRI) sites saw a 11.8 percent increase in pollution on average following the EPA's rollback of enforcement, compared to counties with fewer TRI sites. We also find that these policy-induced increases in pollution are associated with a 53 percent increase in cases and a 10.6 percent increase in deaths from COVID-19.

SUBMITTER: Persico CL

PROVIDER: S-EPMC7899033 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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The effects of increased pollution on COVID-19 cases and deaths.

Persico Claudia L CL Johnson Kathryn R KR

Journal of environmental economics and management 20210222

The SARS-COV-2 virus, also known as the coronavirus, has spread around the world. A growing literature suggests that exposure to pollution can cause respiratory illness and increase deaths among the elderly. However, little is known about whether increases in pollution could cause additional or more severe infections from COVID-19, which typically manifests as a respiratory infection. During the pandemic, the Environmental Protection Agency (EPA) rolled back enforcement of environmental regulati ...[more]

PMID: 33642653

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Project description:BackgroundRecurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.MethodsWe used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.ResultsAt a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.DiscussionOur model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.

Dataset Information

The effects of increased pollution on COVID-19 cases and deaths.

Publications

The effects of increased pollution on COVID-19 cases and deaths.

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