Climate-based ensemble machine learning model to forecast Dengue epidemics (Papers Track)

Rochelle Schneider (European Space Agency); Alessandro Sebastianelli (European Space Agency); Dario Spiller (Italian Space Agency); James Wheeler (European Space Agency); Raquel Carmo (European Space Agency); Artur Nowakowski (Warsaw University of Technology); Manuel Garcia-Herranz (UNICEF); Dohyung Kim (UNICEF); Hanoch Barlevi (UNICEF LACRO); Zoraya El Raiss Cordero (UNICEF LACRO); Silvia Liberata Ullo (University of Sannio); Pierre-Philippe Mathieu (European Space Agency); Rachel Lowe (London School of Hygiene & Tropical Medicine)

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Societal Adaptation & Resilience Health


Dengue fever is one of the most common and rapidly spreading arboviral diseases in the world, with major public health and economic consequences in tropical and sub-tropical regions. Countries such as Peru, 17.143 cases of dengue were reported in 2019, where 81.4% of cases concentrated in five of the 25 departments. When predicting infectious disease outbreaks, it is crucial to model the long-term dependency in time series data. However, this is challenging when performed on a countrywide level since dengue incidence varies across administrative areas. Therefore, this study developed and applied a climate-based ensemble model using multiple machine learning (ML) approaches to forecast dengue incidence rate (DIR) by department. The ensemble combined the outputs from Long Short-Term Memory (LSTM) recurrent neural network and Categorical Boosting (CatBoost) methods to predict DIR one month ahead for each department in Peru. Monthly dengue cases stratified by Peruvian departments were analysed in conjunction with associated demographic, geographic, and satellite-based meteorological data for the period January 2010–December 2019. The results demonstrated that the ensemble model was able to forecast DIR in low-transmission departments, while the model was less able to detect sudden DIR peaks in some departments. Air temperature and wind components demonstrated to be the significant predictors for DIR predictions. This dengue forecast model is timely and can help local governments to implement effective control measures and mitigate the effects of the disease. This study advances the state-of-the-art of climate services for the public health sector, by informing what are the key climate factors responsible for triggering dengue transmission. Finally, this project summarises how important it is to perform collaborative work with complementary expertise from intergovernmental organizations and public health universities to advance knowledge and address societal challenges.