ArticleThe Science of the total environment2021
Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis.
Article in The Science of the total environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology.The Science of the total environment · 2022Pooled it
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach.European radiology · 2024Article
- Review
- Forecasting COVID-19 spreading through an ensemble of classical and machine learning models: Spain's case study.Scientific reports · 2023Article
- Environment and COVID-19 incidence: A critical review.Journal of environmental sciences (China) · 2023Review
- Socio-Economic, Demographic and Health Determinants of the COVID-19 Outbreak.Healthcare (Basel, Switzerland) · 2022Article
- A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution.Scientific data · 2022Article
- Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool.International journal of public health · 2022Article
- Geographical Appraisal of COVID-19 in West Bengal, India.GeoJournal · 2022Article
- Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19.Applied soft computing · 2021Article
- Analysis of Hazard Rate of Municipalities in Slovakia in Terms of COVID-19.International journal of environmental research and public health · 2021Article
- Space-Time Cluster's Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties.International journal of environmental research and public health · 2021Article
- Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States.Sustainable cities and society · 2021Article
- A systematic review on AI/ML approaches against COVID-19 outbreak.Complex & intelligent systems · 2021Article
- Interventions for the Current COVID-19 Pandemic: Frontline Workers' Intention to Use Personal Protective Equipment.Frontiers in public healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.
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