Dengue Fever: From Extreme Climates to Outbreak Prediction

Abstract

Dengue Fever (DF) is an emerging mosquito-borne infectious disease that affect hundred millions of people each year with considerable morbidity and mortality rates, especial on children. Together with global climate changes, it is continuously increasing in terms of number of cases and new locations. Thus, having effective early warning systems become an urgent need to improve disease controls and prevention. In this paper, we introduce a novel framework, called Proximity Time Ensemble, to predict DF outbreaks for multiple areas (provinces) and multiple time step ahead, and to study the effects of climate data on DF outbreaks. PT-Ensem consists of 6 key components: (1) an eventto-event probabilistic framework to study links among extreme climate events and DF outbreaks; (2) a proximity graph that connects similar provinces; (3) an ensemble prediction technique that combines many different advanced machine learning (ML) methods to predict outbreaks within t time steps in the future using extreme climate events as model inputs; (4) a data aggregate scheme to enrich training data for each provinces via its neighbors in the proximity graph; (5) a proximity propagation step that propagates predicted results among similar provinces via the proximity graph until maximal agreements are reached among provinces; and (6) a time propagation step to propagate results via different predicted time steps in each province. We use PT-Ensem to predict DF outbreaks for all provinces in Vietnam using data collected from 1997-2016. Experiments show that PT-Ensem acquires significant performance boost compared to many highly-rated ML models like XGBoost, LightGBM and Catboost in the outbreak prediction task. Compared to most recent deep learning approaches like LSTM-ATT, LSTM, CNN and Transformer for predicting DF incidence, PT-Ensem also dominates in both prediction accuracy and computation times.

A Rule-Based Ensemble Approach for Diarrhoea Outbreak Prediction

“…On the other hand, deep learning models have also been applied, with the expectation of harnessing the novelty of these architectures in accessing information from existing data, e.g., ANN [17], CNN [9], [16], LSTM [9], [16], and Transformer [9]. However, to the best of our knowledge, there is no existing work that aims at predicting diarrhoea outbreak directly, despite of its importance in disease prevention and management [18]. All current works focus on incidence rates/cases predictions and infer outbreak based on the predicted results.…”

“…All current works focus on incidence rates/cases predictions and infer outbreak based on the predicted results. While such approaches are straightforward, they have limited performance as shown in other disease prediction tasks like [18], [19]. Hence, the primary objective of this research is to directly detect diarrhoea outbreaks in an area, rather than calculating post-processing the results obtained from regressed incidence.…”