Associated organisationsAustralian National University · Griffith University · Ho Chi Minh City University of Medicine and Pharmacy · Ind Nguyen Duc 146914 · Ind Quang Bui 210207 · IND Trung Nguyen 7612 · Ministry of Health (Vietnam) · National Institute of Hygiene and Epidemiology · School of Public Health · The University of Queensland · University of Tsukuba · Yale UniversityEurope PMC affiliations are not treated as award recipients or mapped locations.
Funding£4.8M
PeriodSept 2023 — Sept 2028
In plain English
AI plain-English summary
Dengue outbreaks in Vietnam’s Mekong Delta are currently fought reactively, with no early warning system to tell local health teams when and where to act before cases surge. This matters because climate change is making dengue outbreaks more frequent and intense in the region, yet local health practitioners and communities lack the tools to deploy preventive measures in time. The project aims to fill that gap by building a digital early warning system called E-DENGUE, which predicts dengue risk at the district level two months in advance. The system will be open-source, with a web-based and mobile-app interface designed for non-specialist users. If successful, E-DENGUE could shift dengue control from reactive spraying and hospital surges to targeted, pre-emptive action—stockpiling repellents, clearing breeding sites, or issuing community alerts before an outbreak peaks. The team will test this in a cluster-randomised trial across the Mekong Delta, measuring whether the tool actually reduces dengue incidence and whether it is cost-effective for routine use. If proven, the same prediction framework could be adapted for other mosquito-borne diseases in climate-vulnerable regions.
View original technical description
The Mekong Delta Region (MDR) of Vietnam is vulnerable to climate change which results in more frequent and intense mosquito-borne dengue outbreaks. Current dengue control measures are mostly reactive due to the absence of an early warning system (EWS) tailored to the needs of the local health systems. Local health practitioners and the community are, therefore, not adequately empowered to deploy preventive actions to reduce the impact of a dengue outbreak. We propose to develop and evaluate a digital dengue early warning system (E-DENGUE), based on a prediction model, to assist the local health systems and the local communities affected by dengue to proactively mitigate the impact of outbreaks in the MDR. The specific aims are: i) to build a predictive dengue model that accurately predicts dengue risk, at the district level, two months in advance; ii) to develop E-DENGUE––an open-source software system with a user-friendly web-based and mobile-app interface––aimed at local health practitioners to predict dengue incidence and outbreaks at the district level; iii) to evaluate the effectiveness of E-DENGUE in reducing dengue incidence using a cluster-randomised control trial based in the MDR; iv) To evaluate the cost-effectiveness of E-DENGUE for outbreak prevention in the MDR.
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