Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study

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.
Rachel Lowe and colleagues model for the delayed effects of climate change on outbreaks of Dengue in Barbados, an approach that can be extended to model risk for other arboviruses in the Caribbean
Author summary Why was this study done? Changes in local climate conditions (i.e., rainfall, temperature) can affect the risk of outbreaks of diseases transmitted by mosquitoes, such as dengue fever, chikungunya, and Zika. Climate information can be used to develop forecasts of disease outbreaks. What did the researchers do and find? In this study, we devised a statistical model to test whether dengue outbreaks in the Caribbean island of Barbados could be predicted using weather station data for temperature and a precipitation index—used to monitor drought and extreme rainfall—as model inputs from June 1999 to May 2016. The model was able to successfully predict months with dengue outbreaks versus nonoutbreaks in most years. The risk of dengue outbreaks increased with increasing minimum temperature (Tmin; up to 25°C). Disease outbreaks were more likely to occur 4 to 5 months after periods of drought and 1 month after periods of excess rainfall. The modelling results suggest that a drought period followed by intense rainfall 4 to 5 months later could provide optimum conditions for an imminent dengue outbreak. What do these findings mean? In practice, the Regional Climate Centre (RCC) could use this dengue model to generate disease forecasts using their seasonal climate forecast products. Public health decision-makers can use the dengue forecasts as an early warning tool to plan interventions to reduce the risk of dengue and other mosquito-borne diseases. The ability of the model to predict dengue outbreaks is complicated by the emergence of new diseases with similar symptoms that are transmitted by the same mosquito vector, including chikungunya and Zika.