In a groundbreaking development poised to transform public health strategies in the Caribbean, researchers have unveiled a novel predictive model capable of forecasting dengue outbreaks up to three months in advance. This innovative approach, emerging from a collaboration of epidemiologists, climate scientists, and public health officials, offers an unprecedented ability to anticipate dengue transmission dynamics by integrating the compounded effects of climatic extremes such as drought, heat, and intense rainfall. The research, spearheaded by Chloe Fletcher and Rachel Lowe of the Global Health Resilience group at the Barcelona Supercomputing Center (BSC), charts a major advancement in climate-informed disease early warning systems tailored for the Caribbean context, a region acutely vulnerable to both vector-borne diseases and escalating climate variability.
Dengue fever, transmitted primarily by Aedes mosquitoes, has surged in incidence and severity across the tropics and subtropics over recent decades. The Caribbean, in particular, has witnessed increasingly explosive outbreaks interlinked with shifting climate patterns. Climatic extremes like periods of drought punctuated by subsequent intense rainfall create ideal breeding grounds for Aedes aegypti mosquitoes, fostering the conditions necessary for rapid dengue transmission. Traditional epidemiological models have struggled to fully capture the nonlinear interactions between such climatic drivers and vector ecology. The new model breaks ground by incorporating lagged interactions between key climate variables, thereby capturing the compound and cascading effects that modulate outbreak risk.
At the heart of this research lies a sophisticated computational framework that models the interplay of climatic variables over varying time lags to predict outbreak probabilities and case numbers. By analyzing long-term data from Barbados, the team discovered that a sequence of extreme weather events triggers the highest risk of dengue epidemics. Specifically, periods of extreme dryness occurring approximately five months before an outbreak, followed by elevated temperatures about three months prior, and culminating in heavy rainfall roughly one month ahead of time, collectively amplify the risk of disease transmission. This temporal cascade illustrates how drought conditions may initially reduce mosquito habitats but subsequently lead to water storage behaviors that become breeding hotspots once rain resumes.
The model’s validation phase, examining data spanning from 2012 to 2022, demonstrated remarkable predictive accuracy, successfully identifying 81% of observed outbreaks. This performance notably surpasses traditional surveillance approaches which often rely on proximal case counts or static environmental indicators. By incorporating long-lag and short-lag climatic interactions, the framework captures the complex epidemiological processes underpinning dengue dynamics in climates subject to increasing variability. Such predictive power equips public health authorities with actionable foresight, enabling preemptive interventions that could attenuate the scale or even prevent epidemics.
Crucially, this research is not confined to theoretical advances; it has been operationalized in a real-world scenario in collaboration with the Barbados Ministry of Health and regional meteorological agencies. Ahead of the ICC Men’s Twenty20 Cricket World Cup held in June 2024, the model forecasted a 95% probability of a dengue outbreak based on observed cases and climate projections as of March 2024. This early warning catalyzed intensified vector control measures, including targeted larvicide applications and community awareness campaigns around the cricket venues and adjoining neighborhoods. Such timely interventions underscore the model’s potential to support public health preparedness during mass gatherings or other epidemiologically sensitive events.
Rachel Lowe, senior author and leader of the Global Health Resilience group, underscores the broader implications of this study: the model lays a foundation for establishing robust dengue early warning systems across the Caribbean and beyond. As climate-sensitive infectious diseases continue to threaten vulnerable populations worldwide, adaptive forecasting tools that integrate environmental and epidemiological data are imperative. The Barbados pilot signals a scalable blueprint for other regions confronting similar risks, emphasizing the value of interdisciplinary collaborations at the nexus of climate science, epidemiology, and public health policy.
From a technical standpoint, the predictive model leverages computational simulation methodologies to dissect complex climatic datasets spanning multiple decades. It systematically quantifies the lagged effects of climate extremes on vector ecology and viral transmission potential, translating these insights into probabilistic outbreak forecasts. Unlike conventional models that examine single climatic factors in isolation or static time windows, this approach embraces dynamic interactions, including synergistic effects and temporal sequencing of weather events. Such sophistication is vital in a warming world where climate extremes do not occur in isolation but often cascade, cumulatively impacting disease risk.
The model’s findings also illuminate important mechanistic pathways underlying dengue outbreaks under changing climate regimes. Extended dry spells can paradoxically prime environments for epidemics by encouraging the collection and storage of stagnant water in containers, a preferred breeding habitat for Aedes mosquitoes. Subsequent warming temperatures accelerate mosquito development and viral replication cycles, while episodic heavy rainfall increases habitat availability. This cascade from drought to heatwave to flooding exemplifies how compound climatic extremes produce epidemiologically fertile conditions and challenges simplistic assumptions about vector ecology.
Looking beyond dengue, the research team intends to expand the model’s application to other mosquito-borne diseases such as Chikungunya and Zika, which share similar climatic sensitivities and vector dynamics. Moreover, they plan to validate the framework in diverse geographic settings across the Caribbean and potentially other tropical regions. This extension would rigorously test the model’s adaptability to different climatological and socio-ecological contexts, paving the way for the integration of multi-disease early warning systems tailored to local needs and vulnerabilities.
This interdisciplinary breakthrough exemplifies how data-driven scientific innovation can inform climate resilience and public health adaptation strategies. By anticipating disease outbreaks before clinical cases surge, governments can optimize resource allocation, enhance community engagement, and deploy vector control efforts with greater efficacy. Amid growing climatic uncertainty, integrating predictive modeling with on-the-ground surveillance and health infrastructure represents a forward-thinking paradigm to mitigate the burden of vector-borne diseases in vulnerable populations.
Finally, the model is slated for incorporation into Barbados’ national dengue early warning system starting in 2025, marking a transition from proof-of-concept to routine operational use. This integration will facilitate continual real-time assessment of outbreak risk, empowering public health agencies to act decisively and mitigate epidemic impacts. As climate change continues to destabilize weather patterns, such innovative tools will be indispensable in safeguarding health and well-being across the Caribbean and potentially globally.
Subject of Research: Not applicable
Article Title: Compound and cascading effects of climatic extremes on dengue outbreak risk in the Caribbean: an impact-based modelling framework with long-lag and short-lag interactions
News Publication Date: 18-Aug-2025
Web References: http://dx.doi.org/10.1016/j.lanplh.2025.06.003
References: The Lancet Planetary Health
Image Credits: Rachel Lowe (BSC)
Keywords: Climate change effects
