VIS Posters

Visualizing Dengue Forecasts for Public Health Preparedness

Yiwen Xing (University of Oxford), Tuyen Huynh (Oxford University Clinical Research Unit (OUCRU)), Abhishek Dasgupta (University of Oxford), Iago Perez Fernandez (University of Oxford), Manh Nguyen (Oxford University Clinical Research Unit), Moritz Kraemer (University of Oxford), Marc Choisy (Oxford University Clinical Research Unit), David Wallom (University of Oxford), Sophie Yacoub (Oxford University Clinical Research Unit), Sarah Sparrow (University of Oxford), Min Chen (University of Oxford)

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Abstract

Forecasting infectious disease outbreaks is not only a modelling challenge but also a visualization challenge. Public health practitioners must interpret forecasts, compare them with historical trends, and identify emerging outbreak hotspots to support timely interventions. We present a visual analytics system developed within the Dengue Advanced Readiness Tools (DART) project. Designed with Ho Chi Minh City Center for Disease Control (HCDC) stakeholders, the system integrates dengue surveillance data, machine learning forecasts, and historical reference data into a coordinated spatiotemporal interface. The system supports city-level forecasting, district-level comparison, uncertainty-aware exploration, and linked spatial-temporal analysis of dengue dynamics. The prototype is deployed on the infrastructure of Oxford University Clinical Research Unit (OUCRU) and is undergoing pilot evaluation with HCDC. The system also highlights opportunities for visual analytics to support epidemiological model development.