A District-level Ensemble Model to Enhance Dengue Prediction and Control for the Mekong Delta Region of Vietnam

TL;DR

This paper presents an ensemble probabilistic model that predicts dengue outbreaks in Vietnam's Mekong Delta region with up to three months lead time, aiding targeted prevention efforts amid climate and urbanization challenges.

Contribution

It introduces a novel ensemble approach combining multiple models for improved dengue forecasting at the district level in Vietnam.

Findings

Achieved 69% accuracy at 3-month prediction horizon.

Outperformed baseline models in dengue outbreak prediction.

Performance declined during years with atypical seasonality.

Abstract

The Mekong Delta Region of Vietnam faces increasing dengue risks driven by urbanization, globalization, and climate change. This study introduces a probabilistic forecasting model for predicting dengue incidence and outbreaks with one to three month lead times, integrating meteorological, sociodemographic, preventive, and epidemiological data. Seventy-two models were evaluated, and an ensemble combining top-performing spatiotemporal, supervised PCA, and semi-mechanistic hhh4 frameworks was developed. Using data from 2004-2022 for training, validation, and evaluation, the ensemble model demonstrated 69% accuracy at a 3-month horizon, outperforming a baseline model. While effective, its performance declined in years with atypical seasonality, such as 2019 and 2022. The model provides critical lead time for targeted dengue prevention and control measures, addressing a growing public health need in the region.