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Integrating environmental clustering to enhance epidemic forecasting with machine learning models

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Bibliographic Details
Title: Integrating environmental clustering to enhance epidemic forecasting with machine learning models
Authors: Yosra Didi, Ahlam Walha, Ali Wali
Source: International Journal of Cognitive Computing in Engineering, Vol 6, Iss, Pp 628-642 (2025)
Publisher Information: Elsevier BV, 2025.
Publication Year: 2025
Subject Terms: COVID-19 forecasting, Electronic computers. Computer science, Science, Climatic variables, Air pollution features, Machine learning models, QA75.5-76.95, Prediction, Clustering
Description: The COVID-19 pandemic underscored the urgent need for more accurate and adaptive forecasting models to support public health decision-making and limit disease spread. However, many existing models overlook the influence of environmental and climatic factors that significantly affect transmission dynamics. This study addresses this gap with a novel forecasting framework that integrates environmental data into predictive modelling. Our key contributions are threefold: (1) we analyse the relationship between environmental variables (temperature, humidity, and air quality) and COVID-19 trends across countries; (2) we propose a two-stage approach combining K-means clustering to group countries based on environmental conditions, followed by region-specific machine learning models using Support Vector Regression (SVR), Prophet, and Long Short-Term Memory (LSTM) networks for both univariate and multivariate time series forecasting; and (3) we demonstrate that LSTM significantly outperforms other models, achieving superior accuracy for 30-day COVID-19 case predictions. Our results highlight the importance of incorporating environmental variables in epidemic modelling and offer a practical tool for more targeted and effective public health responses. This research provides actionable insights that can inform the design of climate-aware forecasting systems for future pandemic preparedness.
Document Type: Article
Language: English
ISSN: 2666-3074
DOI: 10.1016/j.ijcce.2025.06.001
Access URL: https://doaj.org/article/0c220924a38b4ca285dd225bf268a1db
Rights: CC BY NC ND
Accession Number: edsair.doi.dedup.....4158c116996ee02ef7501daea8e1af86
Database: OpenAIRE
Description
Abstract:The COVID-19 pandemic underscored the urgent need for more accurate and adaptive forecasting models to support public health decision-making and limit disease spread. However, many existing models overlook the influence of environmental and climatic factors that significantly affect transmission dynamics. This study addresses this gap with a novel forecasting framework that integrates environmental data into predictive modelling. Our key contributions are threefold: (1) we analyse the relationship between environmental variables (temperature, humidity, and air quality) and COVID-19 trends across countries; (2) we propose a two-stage approach combining K-means clustering to group countries based on environmental conditions, followed by region-specific machine learning models using Support Vector Regression (SVR), Prophet, and Long Short-Term Memory (LSTM) networks for both univariate and multivariate time series forecasting; and (3) we demonstrate that LSTM significantly outperforms other models, achieving superior accuracy for 30-day COVID-19 case predictions. Our results highlight the importance of incorporating environmental variables in epidemic modelling and offer a practical tool for more targeted and effective public health responses. This research provides actionable insights that can inform the design of climate-aware forecasting systems for future pandemic preparedness.
ISSN:26663074
DOI:10.1016/j.ijcce.2025.06.001