ENDCast: A framework for El Niño driven disease forecasting in Latin America and the Caribbean.

CHLOE FLECHTER; LOTTO BATISTA, MARTÍN; MÜLLER, GABRIELA V.; GOMEZ, ANDREA ALEJANDRA; LOPEZ MS; LOWE, RACHEL

Bruselas

Conferencia; Research Perspectives on the Health Impacts of Climate Change Conference; 2024

El Niño influences extreme climatic events around the globe, such as floods, droughts andstorms, which can impact the timing and intensity of climate-sensitive infectious diseaseoutbreaks. Early warning systems and decision-support tools that integrate seasonal climateforecasts can predict the risk of outbreaks 1-6 months in advance, enabling sufficient time toimplement actions for epidemic preparation. However, there is currently a lack of accessibleand reproducible prediction tools that can be implemented in response to emerging climaticevents. To address this gap, we present a decision-support framework, ENDCast (El NiñoDriven Disease Forecasting), that produces probabilistic predictions of infectious diseaseoutbreaks in response to El Niño or La Niña events and associated seasonal climaticanomalies. We have opted to focus our initial prototype in the Latin American and Caribbean(LAC) region, an area experiencing multiple concurrent outbreaks during the current 2023/24El Niño. An operational platform that issues monthly disease forecasts with a 1-6 monthoutlook will be hosted via an R Shiny web application throughout the event. At present, weproduce forecasts for two vector-borne diseases (dengue and malaria) and one water-bornezoonosis (leptospirosis) in LAC hotspots that are sensitive to El Niño, including NortheasternArgentina, Barbados, the Brazilian Amazon, Northeastern and Southern Brazil, Colombia,Ecuador and Peru. However, the ENDCast framework represents a simple, reproduciblemethodology that could be rapidly and flexibly deployed to any location or climate-sensitivedisease during a future El Niño or La Niña. Using climate data obtained from open globalproducts and locally provided epidemiological data, we undertake a comprehensive modelfitting, selection and verification process to establish bespoke climate-integrated diseaseprediction models in each site. We employ a hierarchical Bayesian mixed modellingframework, where models contain up to three climatic covariates including lagged El Niño-,temperature- and precipitation-based indicators. By integrating climate forecasts that havebeen individually calibrated through tailored post-processing techniques, we produceprobabilistic 1-6 month forecasts for dengue, malaria and leptospirosis each month. Theseforecasts are hosted on a web application that has been co-produced with localcollaborators. The platform includes visualisations, alerts and risk levels for predictedoutbreaks at varying spatial and temporal scales, providing decision-makers with earlywarning of potential outbreaks to trigger early action. The ENDCast approach could be easilyadapted to other endemic settings, including Asia or Africa, to predict the probability ofoutbreaks for a host of different climate-sensitive diseases, contributing to the WellcomeTrust funded projects (HARMONIZE and IDExtemes) and EU-funded projects (E4Warningand IDAlert).