In Rachel Lowe and colleagues show the potential

In , Rachel Lowe and colleagues show the potential of application of climate services for health to dengue in the city of Machala, El Oro Province, Ecuador, where dengue is hyperendemic and transmitted throughout the year with co-circulation of all four dengue serotypes. Machala is an ideal site for analysis of dengue transmission mechanisms, in view of the high vector indices and volume of people and goods moving across the Ecuador–Peru border. Monthly dengue incidence was predicted from January to November, 2016, using a seasonal climate forecast of precipitation and minimum temperature and El Niño forecast in a Bayesian statistical model framework. The estimated dengue incidence was then compared with active surveillance data. The collation of spatiotemporal climatic data with epidemiological, clinical, and environmental data is a powerful approach to understanding associations between health outcome and climate exposure. Although climate data has been used to formulate disease models and produce predictions, Lowe and her colleagues present the first study to use active surveillance data to account for misreporting of other diseases (chikungunya in this study) as dengue to improve the model. The great strengths of the study are the use of real-time climate forecasts to make long-lead dengue predictions and the use of active surveillance data. The practical benefit is the demonstration that the use of seasonal climate and El Niño forecasts allows a prediction to be made at the start of the year for the entire dengue season. While the Ecuadorian Ministry of Health, which is the institution responsible for dengue control, informally monitors dengue incidence based on historical passive surveillance data averaged over the previous 5 years, Lowe and colleagues provide a method for advanced warning of the timing and magnitude of peak dengue incidence. This work confirms that the health jak stat inhibitor in Ecuador needs climate services to anticipate dengue transmission. The predictions by Lowe and colleagues, if reproducible, represent an opportunity for the authorities to (1) increase resources for health surveillance, vector control, and prevention (eg, seasonal increase in personnel, community participation, sustainable access to piped water), (2) improve the diagnosis of clinical cases and laboratory confirmation of cases to avoid misreporting, and (3) increase reporting speed. Understanding how climate variability and long-term climate change affect transmission of dengue and other vector-borne diseases is an ongoing challenge. Climate services synthesise input from multiple disciplines and hence encourage innovative thinking to reduce uncertainty in projections. Lowe and colleagues present a case study, showing a way forward for the discipline of climate services. They contend that seasonal climate forecasting is more accurate during El Niño and La Niña events. However, how real-time climate forecasts would perform in the absence of a long El Niño season remains unclear. In some years, other factors will have a more powerful effect on dengue incidence than seasonal climate factors (eg, efficiency of vector control programmes, population immunity status, patterns of human settlement, movement from neighbouring endemic regions, population growth and density, socioeconomic factors, absence of community engagement, budget cuts in health). Such information, nevertheless, is challenging to obtain with a sufficient degree of quality, reliability, suitability, and at the appropriate spatiotemporal scale, especially in resource-limited settings. The aforementioned factors are not accounted for in the model, but including yearly random effects is a suitable way to quantify interannual variability in dengue risk resulting from unmeasured factors. Further work using improved seasonal climate forecasts is needed to confirm the potential value of incorporating climate information into health decision instruments. These methods must be generalised and translated into a public health decision-making instrument for health authorities to reduce the burden of climate-sensitive diseases, and to work to optimise quality and quantity of spatiotemporal data to deliver the best possible early warning systems for climate-affected diseases.