E-DENGUE is building a user-friendly digital early warning system to help health teams anticipate dengue risk and target prevention in Vietnam's Mekong Delta Region.
E-DENGUE is developing and evaluating a digital dengue early warning system designed to help local health services and communities in Vietnam's Mekong Delta Region act proactively before outbreaks escalate.
The Mekong Delta Region (MDR) of Vietnam is vulnerable to climate change, resulting in more frequent and intense mosquito-borne dengue outbreaks. Current dengue control measures are mostly reactive because there is no early warning system tailored to the needs of local health systems. Local health practitioners and communities are therefore not adequately empowered to deploy preventive actions that reduce the impact of an outbreak.
We propose to develop and evaluate E-DENGUE, a digital early warning system based on a prediction model, to assist local health systems and communities affected by dengue to proactively mitigate outbreak impacts in the MDR. The system builds on foundational work with a district-level prediction model in a smaller geographical area, where dengue prevention and control measures are implemented under the guidance and supervision of provincial medical centres. Once developed, E-DENGUE will provide a user-friendly software infrastructure for use at local health centres.
To reduce dengue incidence by 25% by creating a model-based system to predict dengue outbreaks, enabling proactive outbreak prevention.
E-DENGUE will support local health departments and communities to proactively plan and deploy dengue prevention and control measures in high-risk districts of the MDR through:
The project connects prediction with routine prevention practice: identifying the data needed, selecting an optimal forecasting model, integrating E-DENGUE into local programs, understanding user acceptance, and testing both effectiveness and cost-effectiveness. At project completion, E-DENGUE is expected to be translatable to other vulnerable populations across South-East Asia and other tropical regions where similar environmental and sociodemographic characteristics prevail.
See how E-DENGUE is turning dengue forecasting research into an early-warning platform designed for real public-health decisions.
E-DENGUE combines public health, epidemiology, modelling, climate science, economics, social science, software engineering and implementation expertise.
The University of Queensland
NIHE Vietnam
Yale University
Griffith University
Southern Cross University
University of Tsukuba
Forecasting only creates value when it connects to usable tools, real interventions and evidence about what works.
Develop and validate probabilistic dengue forecasts using spatiotemporal, statistical and semi-mechanistic approaches.
Translate multi-source data and model outputs into a practical web and mobile decision-support experience for local health teams.
Evaluate whether forecast-guided prevention reduces dengue burden and whether the approach is cost-effective for health systems.
Co-design around real user needs, implementation context, capacity and pathways for sustainable integration into surveillance.
A concise view of the programme, from launch and model development through implementation, evaluation and translation.
The international programme launched and established the data, ethics and implementation foundations for all four research themes.
Technical and user consultations translated research requirements into a selected forecasting approach and an implementable digital product.
The web and mobile platform moved from development into operational testing, training and forecast-guided field intervention.
The current phase combines large-scale implementation and effectiveness evaluation with a major technical revision of the forecasting system.
The final programme phase will consolidate trial results, cost-effectiveness evidence and pathways for sustainable adoption.
UQ highlights the transition from pilot work toward scale-up, operational capacity building and E-DENGUE-supported interventions across the Mekong Delta.
An international project team examined how early-warning information supports surveillance, larval control and community communication in practice.
Project partners reviewed implementation, handed over E-DENGUE infrastructure and strengthened research and training collaboration.
Current phase: scale-up of E-DENGUE-supported interventions, real-world use of early warnings and rigorous evaluation of effectiveness, cost-effectiveness and implementation.
The team assessed how early-warning information could support vector surveillance, targeted prevention and local public-health decision-making in practice.
Project partners strengthened implementation arrangements, handed over E-DENGUE infrastructure and expanded institutional collaboration for the next phase.
Project Lead · Steering Committee Member · Theme 1 Co-Lead · Themes 3 & 4 Member
The University of Queensland
Steering Committee Lead
Yale University
Theme 1 Lead · Theme 2 & 3 Member · Technical Advisor
Yale University
Theme 2 Lead · Software & App Development
Southern Cross University
Steering Committee Member · Theme 3 Lead
National Institute of Hygiene and Epidemiology (NIHE), Vietnam
Steering Committee Member · Theme 4 Lead
Griffith University
Steering Committee Member
The University of Queensland
Theme 3 Co-Lead
Australian National University
Steering Committee Member · Theme 4 Member
The University of Queensland
Steering Committee Member
GDPM, Ministry of Health, Vietnam
Project Management Team · NIHE
National Institute of Hygiene and Epidemiology (NIHE), Vietnam