Abstract
This study aims to find the association between short-term exposure to air pollutants, such as particulate matters and ground-level ozone, and SARS-CoV-2 confirmed cases. Generalized linear models (GLM), a typical choice for ecological modeling, have well-established limitations. These limitations include apriori assumptions, inability to handle multicollinearity, and considering differential effects as the fixed effect. We propose an Ensemble-based Dynamic Emission Model (EDEM) to address these limitations. EDEM is developed at the intersection of network science and ensemble learning, i.e., a specialized approach of machine learning. Generalized Additive Model (GAM), i.e., a variant of GLM, and EDEM are tested in Los Angeles and Ventura counties of California, which is one of the biggest SARS-CoV-2 clusters in the US. GAM depicts that a 1 μg/m 3 , 1 μg/m 3 , and 1 ppm increase (lag 0-7) in PM 2.5, PM 10, and O3 is associated with 4.51% (CI: 7.01 to -2.00) decrease, 1.62% (CI: 2.23 to -1.022) decrease, and 4.66% (CI: 0.85 to 8.47) increase in daily SARS-CoV-2 cases, respectively. Subsequent increment in lag resulted in the negative association between pollutants and SARS-CoV-2 cases. EDEM results in an R2 score of 90.96% and 79.16% on training and testing datasets, respectively. EDEM confirmed the negative association between particulates and SARS-CoV-2 cases; whereas, the O3 depicts a positive association; however, the positive association observed through GAM is not statistically significant. In addition, the county-level analysis of pollutant concentration interactions suggests that increased emissions from other counties positively affect SARS-CoV-2 cases in adjoining counties as well. The results reiterate the significance of uniformly adhering to air pollution mitigation strategies, especially related to ground-level ozone.
Keywords: Air pollution; COVID-19; California; Centrality measures; Ensemble learning; Machine learning; Network science.
【저자키워드】 COVID-19, machine learning, Air pollution, network science, California, Centrality measures, Ensemble learning, 【초록키워드】 SARS-CoV-2, machine learning, variant, learning, Model, Cluster, Air pollution, network, GLM, Air pollutants, association, Interaction, Ozone, Concentration, Analysis, Ensemble learning, exposure to, Los Angeles, particulate matters, PPM, Additive Model, confirmed cases, limitation, emission, Intersection, machine, datasets, dynamic, positive, multicollinearity, Linear model, Effect, Affect, limitations, approach, decrease, particulates, pollutant, Additive, tested, include, addition, increase in, statistically significant, fixed, ecological, Generalized, Los Angele, 【제목키워드】 United State,