Please use this identifier to cite or link to this item:
https://biore.bio.bg.ac.rs/handle/123456789/4158
Title: | PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA | Authors: | Milicevic, Ognjen Salom, Igor Rodic, Andjela Marković Z. Sofija Tumbas Z. Marko Zigic, Dusan Djordjevic, Magdalena Djordjevic, Marko |
Keywords: | COVID-19 pollution dependence;Outdoor air pollutants;Basic reproduction number;Principal component analysis;Machine learning | Issue Date: | 24-Jun-2021 | Rank: | M21a | Journal: | Environmental Research | Volume: | 201 | Start page: | 111526 | Abstract: | Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R_0) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM_2.5 is a major predictor of R_0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R_0, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility. |
URI: | https://biore.bio.bg.ac.rs/handle/123456789/4158 | ISSN: | 0013-9351 | DOI: | 10.1016/j.envres.2021.111526 |
Appears in Collections: | Journal Article |
Files in This Item:
File | Description | Size | Format | Existing users please |
---|---|---|---|---|
1-s2.0-S0013935121008203-main.pdf | 3.01 MB | Adobe PDF | Request a copy |
SCOPUSTM
Citations
25
checked on Nov 1, 2024
Page view(s)
4
checked on Nov 4, 2024
Google ScholarTM
Check
Altmetric
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.