Dengue is endemic throughout the tropical areas of the Americas, but little is known about its regional dynamics. We examined seasonal and multiannual dengue trends across the Americas and possible underlying mechanisms using monthly dengue surveillance data from 14 countries. We collected monthly dengue case data from 241 subnational locations in 14 countries, ranging from 1985 to 2018 (6 to 22 years), and used wavelet analysis to isolate seasonal (8 to 16 months) and multiannual (17+ months) patterns. For each location, we assessed seasonal and multiannual dynamics as well as coherence and differences in timing of dengue cycles between 22,578 location pairs. We assessed patterns in coherence over time and space and compared these patterns to variations in temperature, rainfall, and El Niño Southern Oscillation patterns. Strong synchrony in dengue dynamics was identified across the region at the seasonal and multiannual scales. Seasonal dengue dynamics were associated with local climate patterns and dengue dynamics in nearby locations. High synchrony at multiannual scales indicated that large dengue epidemics were shared across the region, with an average temporal lag of only 6 months at distances of up to 10,000 kilometers. Synchrony of these epidemics likely reflects both regional climate variability and human movement. Observed dengue dynamics were not unique to individual countries in the Americas but rather extended across the region reflecting climatic and nonclimatic drivers. These findings can support the development of better early warning tools to support epidemic preparedness and response and underscore the importance of working collaboratively across borders.

BACKGROUND:Small islands developing states in the Caribbean are exposed to increasingly frequent and intense extreme climatic events, which can exacerbate outbreaks of climate-sensitive infectious diseases. Few forecasting tools incorporate the compound and cascading effects of multiple delayed climatic indicators on disease outbreak risk. We aimed to create an impact-based modelling framework that employs interactions between climatic predictors to forecast the probability of a climate-sensitive infectious disease outbreak 3 months ahead, and to investigate the compound and cascading effects of temperature and long-lag and short-lag standardised precipitation index (SPI) on dengue outbreak risk in Barbados. METHODS:We developed a modelling framework to predict the probability of a dengue outbreak in Barbados with a 3-month lead time. We assessed the relationships between dengue incidence and interacting long-lag and short-lag hydrometeorological predictors with confirmed cases from 1999 to 2022 and a Bayesian hierarchical framework accounting for seasonal and interannual variation. With this long-short-lag interaction model, we piloted a dengue early warning system in Barbados for the International Cricket Council Men's Twenty20 World Cup in June, 2024, as a real-world prospective example. FINDINGS:We found that a three-way interaction between the 3-month averaged mean temperature anomaly lagged by 3 months, 6-month SPI (SPI-6) lagged by 5 months, and SPI-6 lagged by 1 month best predicted dengue outbreak risk in Barbados. Our findings showed that long-lag dry (lagged by 5 months), mid-lag hot (lagged by 3 months), and short-lag wet (lagged by 1 month) conditions led to the greatest dengue risk. During cross-validation from 2012 to 2022, the model exhibited a true positive rate (TPR) of 81% and a false positive rate (FPR) of 29%, outperforming a baseline model representing standard practice with a TPR of 68% and an FPR of 48%. For the Twenty20 World Cup, the model predicted a 95% outbreak probability due to epidemiological and climatic conditions, which was shared with the Barbados Ministry of Health and Wellness ahead of the tournament. INTERPRETATION:Our impact-based modelling framework with long-lag and short-lag interactions explicitly accounted for the compound and cascading effects of drought, heat, and excessively wet conditions on dengue outbreak risk in Barbados. The model is being implemented in a national dengue early warning system with ongoing monitoring and evaluation to ensure its reliability and usefulness in operational contexts. Future work could explore the applicability of this methodology to modelling or predicting climate-sensitive infectious diseases in other endemic settings. FUNDING:Wellcome Trust, Horizon Europe, European Development Fund, and Royal Society.

Here we introduce a demand-driven framework designed to implement climate services in the health sector, with a particular focus on the Caribbean region. Climate services are essential for supporting informed decision-making and response strategies in relation to climate-related health risks. Through collaborative efforts, we are co-producing a climate-driven dengue early warning system (EWS) to target vector-borne diseases effectively. While challenges exist in implementing such systems, EWSs provide valuable tools for managing epidemic risks by predicting potential disease outbreaks in advance. The scarcity of operational climate tools in the health sector underscores the need for increased investment and strategic implementation practices. To address these challenges, a demand-driven framework is proposed, emphasizing strategic planning focused on health intervention development, partnership building, data, communication, human resources, capacity building, and sustainable funding. This framework aims to integrate climate services seamlessly into health systems, thereby enhancing public health resilience and facilitating well-informed decision-making to effectively address climate-sensitive diseases.

Over the past decade, the Caribbean region has been challenged by compound climate and health hazards, including tropical storms, extreme heat and droughts and overlapping epidemics of mosquito-borne diseases, including dengue, chikungunya and Zika. Early warning systems (EWS) are a key climate change adaptation strategy for the health sector. An EWS can integrate climate information in forecasting models to predict the risk of disease outbreaks several weeks or months in advance. In this article, we share our experiences of co-learning during the process of co-creating a dengue EWS for the health sector in Barbados, and we discuss barriers to implementation as well as key opportunities. This process has involved bringing together health and climate practitioners with transdisciplinary researchers to jointly identify needs and priorities, assess available data, co-create an early warning tool, gather feedback via national and regional consultations and conduct trainings. Implementation is ongoing and our team continues to be committed to a long-term process of collaboration. Developing strong partnerships, particularly between the climate and health sectors in Barbados, has been a critical part of the research and development. In many countries, the national climate and health sectors have not worked together in a sustained or formal manner. This collaborative process has purposefully pushed us out of our comfort zone, challenging us to venture beyond our institutional and disciplinary silos. Through the co-creation of the EWS, we anticipate that the Barbados health system will be better able to mainstream climate information into decision-making processes using tailored tools, such as epidemic forecast reports, risk maps and climate-health bulletins, ultimately increasing the resilience of the health system.

To date, there are few examples of implementation science studies that help guide climate-related health adaptation. Implementation science is the study of methods to promote the adoption and integration of evidence-based tools, interventions, and policies into practice to improve population health. These studies can provide the needed empirical evidence to prioritise and inform implementation of health adaptation efforts. This Personal View discusses five case studies that deployed disease early warning systems around the world. These cases studies illustrate challenges to deploying early warning systems and guide recommendations for implementation science approaches to enhance future research. We propose theory-informed approaches to understand multilevel barriers, design strategies to overcome those barriers, and analyse the ability of those strategies to advance the uptake and scale-up of climate-related health interventions. These findings build upon previous theoretical work by grounding implementation science recommendations and guidance in the context of real-world practice, as detailed in the case studies.

Dengue fever and other febrile mosquito-borne diseases place considerable health and economic burdens on small island nations in the Caribbean. Here, we used two methods of cluster detection to find potential hotspots of transmission of dengue and chikungunya in Barbados, and to assess the impact of input surveillance data and methodology on observed patterns of risk. Using Moran's I and spatial scan statistics, we analyzed the geospatial and temporal distribution of disease cases and rates across Barbados for dengue fever in 2013-2016, and a chikungunya outbreak in 2014. During years with high numbers of dengue cases, hotspots for cases were found with Moran's I in the south and central regions in 2013 and 2016, respectively. Using smoothed disease rates, clustering was detected in all years for dengue. Hotspots suggesting higher rates were not detected via spatial scan statistics, but coldspots suggesting lower than expected rates of disease activity were found in southwestern Barbados during high case years of dengue. No significant spatiotemporal structure was found in cases during the chikungunya outbreak. Spatial analysis of surveillance data is useful in identifying outbreak hotspots, potentially complementing existing early warning systems. We caution that these methods should be used in a manner appropriate to available data and reflecting explicit public health goals-managing for overall case numbers or targeting anomalous rates for further investigation.

Background Small island developing states (SIDS) in the Caribbean region are challenged with managing the health outcomes of a changing climate. Health and climate sectors have partnered to co-develop climate services to improve the management of these diseases, for example, through the development of climate-driven early warning systems. The objective of this study was to identify health and climate stakeholder perceptions and needs in the Caribbean, with respect to the development of climate services for arboviruses (e.g. dengue, chikungunya, and Zika). Methods Stakeholders included public decision makers and practitioners from the climate and health sectors at the regional (Caribbean) level and from the countries of Dominica and Barbados. From April to June 2017, we conducted interviews (n=41), surveys (n=32), and national workshops with stakeholders. Survey responses were tabulated and audio recordings were transcribed and analyzed using qualitative coding to identify responses by research topic, country/region, and sector. Results Health practitioners indicated that their jurisdiction is currently experiencing an increased risk of diseases transmitted by Ae. aegypti due to climate variability, and most anticipated that this risk will increase in the future. National health sectors reported financial limitations and a lack of technical expertise in geographic information systems (GIS), statistics, and modeling, which constrained their ability to implement climate services for arboviruses. National climate sectors were constrained by a lack of personnel. Stakeholders highlighted the need to strengthen partnerships with the private sector, academia, and civil society. They identified a gap in local research on climate-arbovirus linkages, which constrained the ability of the health sector to make informed decisions. Strategies to strengthen the climate-health partnership included a top-down approach by engaging senior leadership, multi-lateral collaboration agreements, national committees on climate and health, and shared spaces of dialogue. Mechanisms for mainstreaming climate services for health operations to control arboviruses included climate-health bulletins and an online GIS platform that would allow for regional data sharing and the generation of spatiotemporal epidemic forecasts. Conclusions These findings support the creation of interdisciplinary and intersectoral communities of practices and the co-design of climate services for the Caribbean public health sector. By fostering the effective use of climate information within health policy, research and practice, nations will have greater capacity to adapt to a changing climate.

AbstractObjectiveTo detect potential hotspots of transmission of dengue and chikungunya in Barbados, and assess impact of input surveillance data and methodology on observed patterns of risk.MethodsUsing two methods of cluster detection, Moran’s I and spatial scan statistics, we analyzed the geospatial and temporal distribution of disease cases and rates across Barbados for dengue fever in 2013–2016, and a 2014 chikungunya outbreak.ResultsDuring years with high numbers of dengue cases, hotspots for cases were found with Moran’s I in south and central regions in 2013 and 2016, respectively. Using smoothed disease rates, clustering was detected every year for dengue. Hotspots were not detected via spatial scan statistics, but coldspots suggesting lower rates of disease activity were found in southwestern Barbados during high case years of dengue.ConclusionsSpatial analysis of surveillance data is useful in identifying outbreak hotspots, complementing existing early warning systems. We caution that these methods should be used in a manner appropriate to available data, and reflecting explicit public health goals – managing for overall case numbers, or targeting anomalous rates for further investigation.

Background Over the last 5 years (2013-2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016. Methods and findings Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure-lag-response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure-lag-response functions of these indicators-rather than linear effects for individual lags-more appropriately described the climate-disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%: 91%) compared with 64% (58%: 71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika. Conclusion We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.

Barbados is a Caribbean island country of approximately 285,000 people, with a thriving tourism industry. In 2015, Zika spread rapidly throughout the Americas, and its proliferation through the Caribbean islands followed suit. Barbados reported its first confirmed autochthonous Zika transmission to the Pan American Health Organization in January 2016, a month before the global public health emergency was declared. After detection of suspected Zika cases on Barbados in 2015, 926 individuals were described as suspected cases, and 147 laboratory-confirmed cases were reported through December 2016, the end of the most recent epidemiological year. In this short report, we describe the epidemiological characteristics of 926 clinical case records that were originally suspected as cases of Zika, and which were subsequently sent for testing and confirmation; 147 were found positive for Zika, using reverse transcription-polymerase chain reaction methods, another 276 tested negative, and the remaining 503 were either pending results or still in the suspected category. Women were represented at about twice the rate of men in case records where gender was reported (71.9%), and confirmed cases (78.2%), and 19 of the confirmed positive cases were children under the age of 10.