Climate Service for Vector and Vector-Borne Disease Risk Monitoring and Management
Lead Institution: Flemish Institute for Technological Research (VITO)Project Partners/Subcontractors: Belgian Climate Centre (BCC)Implementing User: National Public Health Institute Belgium (Sciensano)
Climate change could significantly reshape the public health landscape in Belgium, particularly through its impacts on vectors (mosquitoes, ticks…) and vector-borne diseases (VBDs) such as Lyme disease, dengue, West Nile fever, and tick-borne encephalitis. Rising temperatures, shifting humidity levels, and changing precipitation patterns are impacting the ecology (reproduction, feeding, …), the seasonal pattern, and the geographical spread of disease-carrying vectors like ticks and mosquitoes.
Currently, the integration of real-time climate data into Belgium’s public health system remains limited. Sciensano — the national public health institute — relies on retrospective and manually retrieved climate information. This project addresses the urgent need for automated, near-real-time climate services tailored to public health applications.
Hereto, a national Climate Service for vectors and VBD management in Belgium is being developed by VITO, in collaboration with Sciensano and the Belgian Climate Centre. The service will automate the retrieval, downscaling, and processing of daily climate data and produce actionable, high-resolution (100 m) maps that seamlessly integrate into Sciensano’s epidemiological workflows.
“This Climate Service will bridge the gap between climate science and public health, enabling Sciensano to anticipate seasonal peaks in vector activity and support early warning systems.”
Figure 1. Screenshot of the citizen science platform ‘MosquitoSurveillance’ for monitoring and reporting tiger mosquitoes in Belgium.
This national platform empowers citizens to contribute to public health surveillance by reporting sightings of the invasive Aedes albopictus (tiger mosquito). The collected data supports early detection and timely response strategies such as control and prevention of the species, particularly in the context of changing climate conditions that favour the spread of mosquitoes.
Figure 2. Screenshot of reported tick bites in Belgium over a one-year period.
The map shows the cumulative number of reported tick bites across Belgian municipalities, highlighting regional variations in exposure risk. Areas with high risk are concentrated in the northeast, central, and southern forested regions, underlining the importance of climate-informed surveillance tools.
Key actions include:
AI Model Development: A deep-learning model trained on 20+ years of ERA5-Land data and UrbClim simulations to generate daily historical climate maps at high spatial resolution (till ‘today-6 days’).Regression Model Implementation: A meteo pattern-matching approach using Royal Meteorological Institute (RMI) Automatic Weather Station (AWS) data to select representative historical maps for recent days (from ‘today-5’ to today).CLIMSERVE Python Package: A modular open-source toolkit developed to process land and climate data and produce high-resolution maps.Integration at Sciensano: Full technical handover and integration into Sciensano’s POSIT Enterprise environment for sustainable operational use.High-Resolution Risk Indicators: Including vector-specific outputs like Growing Degree Days (GDD) for mosquito and tick risk modeling.
Figure 3. Dual-model architecture of the Climate Service for vector and vector-borne disease management in Belgium.
The figure outlines two complementary workflows: one using an AI model fed by ERA5-Land and land surface data to produce high-resolution climate maps for days up to t–6, and another using a regression model that matches recent weather observations with historical conditions to generate maps for days t–5 to t. Together, these workflows enable seamless, near-real-time monitoring of climatic variables at 100 m resolution across Belgium.