Climate-Driven Dengue Prediction Models In Southeast Asia: A Scoping Review For Public Health Application
Keywords:
Keywords : Dengue Prediction Models, Climate, Public Health Applications, Southeast Asia, Early Warning Systems, Risk Mapping, Long-Term Forecasting, Simulation ModelsAbstract
Introduction:
Dengue remains a major public health threat in Southeast Asia, with climate factors playing a significant role in its transmission. Climate-driven dengue modelling offers a valuable tool for outbreak forecasting and risk assessment. However, methodological rigour, predictive accuracy, and applicability vary across studies. This review aimed to map climate-driven dengue modelling approaches in Southeast Asia and evaluate their public health applicability.
Methods:
A scoping review was conducted in accordance with the JBI framework and PRISMA-ScR guidelines. Studies focusing on climate-driven dengue prediction models were identified from PubMed, Scopus, and Web of Science. A total of 20 studies (N = 20) met the eligibility criteria. Eligible studies included those conducted in Southeast Asia that incorporated at least one climatic predictor into a dengue model and were published in English. Data extraction included model types, predictors, and model performance.
Results:
The studies were included and categorized into four themes, Short-Term Forecasting, Long-Term Forecasting, Risk Mapping, and Climate–Transmission Simulation Models. Temperature, rainfall, and humidity were the most used climatic variables. Short-term models demonstrated high predictive accuracy (80%–95%). Long-term and simulation models offered insights into seasonal dynamics and intervention scenarios. Risk mapping enhanced spatial targeting, particularly when integrating land-use or mobility data. Across all themes, reliance on internal validation and inconsistent performance reporting limited generalisability.
Conclusion:
These models showed significant potential to improve surveillance and outbreak preparedness in Southeast Asia. Strengthening external validation, integrating socio-environmental predictors, and fostering regional collaboration through platforms such as the ASEAN Centre for Public Health Emergencies and Emerging Diseases (ACPHEED) are essential to scaling model use for public health decision-making.
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