A Review of Hybrid Epidemiological and Machine Learning Models for COVID-19 Prediction under Uncertainty
Keywords:
Pandemic Forecasting, COVID-19, Machine Learning, Epidemiological Models, Uncertainty Quantification, LSTM, Hybrid Models, Decision Support Systems.Abstract
Pandemic forecasting has become a critical tool for public health planning, yet it is challenged by high levels of uncertainty and dynamic real-world conditions. This review paper presents a comprehensive analysis of existing approaches, including epidemiological models, machine learning techniques, and uncertainty-aware frameworks. Traditional compartmental models such as SEIR are examined alongside advanced hybrid models and deep learning methods like LSTM, highlighting their strengths and limitations. The study emphasizes the importance of integrating probabilistic and fuzzy techniques to address uncertainty in predictions. Additionally, decision-support systems and data integration strategies are discussed for effective policy formulation. The review identifies key research gaps and proposes the need for adaptive, interpretable, and hybrid frameworks to improve forecasting reliability. Overall, this work provides insights into developing robust pandemic prediction models for future healthcare challenges.
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