Environmental health perspectives · 2025
A State-of-the-Science Review of Long-Term Predictions of Climate Change Impacts on Dengue Transmission Risk.
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Islam Jahirul, Frentiu Francesca D, Devine Gregor J, Bambrick Hilary, Hu Wenbiao
Аннотация
BACKGROUND: Climate change is predicted to profoundly impact dengue transmission risk, yet a thorough review of evidence is necessary to refine understanding of climate scenarios, projection periods, spatial resolutions, and modeling approaches.
OBJECTIVES: We conducted a state-of-the-science review to comprehensively understand long-term dengue risk predictions under climate change, identify research gaps, and provide evidence-based guidelines for future studies.
METHODS: We searched three medical databases (PubMed, Embase, and Web of Science) up to 5 December 2024 to extract relevant modeling studies. An a priori search strategy, predefined eligibility criteria, and systematic data extraction procedures were implemented to identify and evaluate studies.
RESULTS: Of 5,035 studies retrieved, 57 met inclusion criteria. Prediction for dengue risk ranged from 1950 to 2115, and 52.63% (n=30) of all studies used Representative Concentration Pathways (RCPs). Specifically, RCP 8.5 (34.94%; n=29), Shared Socioeconomic Pathways (SSPs) 2 (32.35%; n=11), and the Special Report on Emissions Scenarios (SRES) A1 (58.33%; n=7) were utilized the most among all the RCPs, SSPs, and SRES climate change scenarios. Most studies (57.89%; n=33) used only climatic variables for the prediction, and 21.05% (n=12) of studies employed fine spatial resolution (≈1km) for the climate data. We identified that correlative approach was used mostly across the studies for modeling the future risk (61.40%; n=35). Among mechanistic models, 35% (n=7) lacked outcome validation, and 75% (n=15) did not report model evaluation metrics.
DISCUSSION: We identified the urgent need to strengthen dengue databases, use finer spatial resolutions to integrate big data, and incorporate potential socioenvironmental factors such as human movement, vegetation, microclimate, and vector control efficacy in modeling. Utilizing appropriate spatiotemporal models and validation techniques will be crucial for developing functional climate-driven early warning systems for dengue fever. https://doi.org/10.1289/EHP14463.
OBJECTIVES: We conducted a state-of-the-science review to comprehensively understand long-term dengue risk predictions under climate change, identify research gaps, and provide evidence-based guidelines for future studies.
METHODS: We searched three medical databases (PubMed, Embase, and Web of Science) up to 5 December 2024 to extract relevant modeling studies. An a priori search strategy, predefined eligibility criteria, and systematic data extraction procedures were implemented to identify and evaluate studies.
RESULTS: Of 5,035 studies retrieved, 57 met inclusion criteria. Prediction for dengue risk ranged from 1950 to 2115, and 52.63% (n=30) of all studies used Representative Concentration Pathways (RCPs). Specifically, RCP 8.5 (34.94%; n=29), Shared Socioeconomic Pathways (SSPs) 2 (32.35%; n=11), and the Special Report on Emissions Scenarios (SRES) A1 (58.33%; n=7) were utilized the most among all the RCPs, SSPs, and SRES climate change scenarios. Most studies (57.89%; n=33) used only climatic variables for the prediction, and 21.05% (n=12) of studies employed fine spatial resolution (≈1km) for the climate data. We identified that correlative approach was used mostly across the studies for modeling the future risk (61.40%; n=35). Among mechanistic models, 35% (n=7) lacked outcome validation, and 75% (n=15) did not report model evaluation metrics.
DISCUSSION: We identified the urgent need to strengthen dengue databases, use finer spatial resolutions to integrate big data, and incorporate potential socioenvironmental factors such as human movement, vegetation, microclimate, and vector control efficacy in modeling. Utilizing appropriate spatiotemporal models and validation techniques will be crucial for developing functional climate-driven early warning systems for dengue fever. https://doi.org/10.1289/EHP14463.