Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AI
This study aims to holistically comprehend the intricate dynamics between air pollution, socio-demographics, and COVID-19 outcomes in India. The primary objective centers on deploying explainable AI (XAI) methodologies to elucidate the intricate pathways and latent mechanisms governing these associa...
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Elsevier
2025-03-01
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| Series: | Hygiene and Environmental Health Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773049225000029 |
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| author | Mohmmed Talib Kripabandhu Ghosh Gopala Krishna Darbha |
| author_facet | Mohmmed Talib Kripabandhu Ghosh Gopala Krishna Darbha |
| author_sort | Mohmmed Talib |
| collection | DOAJ |
| description | This study aims to holistically comprehend the intricate dynamics between air pollution, socio-demographics, and COVID-19 outcomes in India. The primary objective centers on deploying explainable AI (XAI) methodologies to elucidate the intricate pathways and latent mechanisms governing these associations.A multi-faceted approach was employed integrating ecological study, hybrid-ML, and XAI techniques to characterize the underlying dependencies and interactions driving the pandemic's spatiotemporal evolution and system dynamics. The ecological study analyzed the association between air pollution levels and COVID-19 case fatality rates (CFRs) across distinct pandemic phases. We utilized a Negative Binomial model for interpretability and implemented a stacked ensemble framework to enhance predictive performance. This stacked model was further leveraged to provide deeper insights into the underlying patterns through XAI techniques.The ecological study identified distinct patterns in CFR across different pandemic phases of the pandemic, with higher pollution levels monotonically associated with increased CFRs. Furthermore, the stacked ensemble model consistently outperformed its base models, demonstrating the benefits of combining multiple models. Additionally, the XAI analysis identified NO2 as a key driver of COVID-19 cases and mortalities, while PM10 was found to be particularly influential on mortalities. The study concluded distinct COVID-19 transmission patterns across regions and pandemic phases, highlighting the influence of non-pharmaceutical interventions, viral strains, and socio-demographics in driving the pandemic.The findings highlight the need for strong pollution controls to mitigate air pollution's impact on health. The developed hybrid model can aid in predicting COVID-19 outcomes in future respiratory outbreaks, supporting public health planning and targeted interventions. |
| format | Article |
| id | doaj-art-7e2ec63b76d14e88863a668868658c99 |
| institution | Kabale University |
| issn | 2773-0492 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Hygiene and Environmental Health Advances |
| spelling | doaj-art-7e2ec63b76d14e88863a668868658c992025-02-08T05:01:46ZengElsevierHygiene and Environmental Health Advances2773-04922025-03-0113100119Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AIMohmmed Talib0Kripabandhu Ghosh1Gopala Krishna Darbha2Environmental Nanoscience Laboratory, Department of Earth Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India; Corresponding author at: Environmental Nanoscience Laboratory, Department of Earth Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, IndiaDepartment of Computational and Data Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, IndiaEnvironmental Nanoscience Laboratory, Department of Earth Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India; Centre for Climate and Environmental Studies, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, IndiaThis study aims to holistically comprehend the intricate dynamics between air pollution, socio-demographics, and COVID-19 outcomes in India. The primary objective centers on deploying explainable AI (XAI) methodologies to elucidate the intricate pathways and latent mechanisms governing these associations.A multi-faceted approach was employed integrating ecological study, hybrid-ML, and XAI techniques to characterize the underlying dependencies and interactions driving the pandemic's spatiotemporal evolution and system dynamics. The ecological study analyzed the association between air pollution levels and COVID-19 case fatality rates (CFRs) across distinct pandemic phases. We utilized a Negative Binomial model for interpretability and implemented a stacked ensemble framework to enhance predictive performance. This stacked model was further leveraged to provide deeper insights into the underlying patterns through XAI techniques.The ecological study identified distinct patterns in CFR across different pandemic phases of the pandemic, with higher pollution levels monotonically associated with increased CFRs. Furthermore, the stacked ensemble model consistently outperformed its base models, demonstrating the benefits of combining multiple models. Additionally, the XAI analysis identified NO2 as a key driver of COVID-19 cases and mortalities, while PM10 was found to be particularly influential on mortalities. The study concluded distinct COVID-19 transmission patterns across regions and pandemic phases, highlighting the influence of non-pharmaceutical interventions, viral strains, and socio-demographics in driving the pandemic.The findings highlight the need for strong pollution controls to mitigate air pollution's impact on health. The developed hybrid model can aid in predicting COVID-19 outcomes in future respiratory outbreaks, supporting public health planning and targeted interventions.http://www.sciencedirect.com/science/article/pii/S2773049225000029Air pollutionCOVID-19Machine learningExplainable AI (XAI)Health management |
| spellingShingle | Mohmmed Talib Kripabandhu Ghosh Gopala Krishna Darbha Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AI Hygiene and Environmental Health Advances Air pollution COVID-19 Machine learning Explainable AI (XAI) Health management |
| title | Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AI |
| title_full | Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AI |
| title_fullStr | Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AI |
| title_full_unstemmed | Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AI |
| title_short | Retrospectively understanding the multifaceted interplay of COVID-19 outbreak, air pollution, and sociodemographic factors through explainable AI |
| title_sort | retrospectively understanding the multifaceted interplay of covid 19 outbreak air pollution and sociodemographic factors through explainable ai |
| topic | Air pollution COVID-19 Machine learning Explainable AI (XAI) Health management |
| url | http://www.sciencedirect.com/science/article/pii/S2773049225000029 |
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