Imagine a high-tech ambulance that doesn't treat people, but instead hunts for dangerous mosquitoes. It uses smart traps and satellite data to find where outbreaks are starting and identifies the exact virus in the field. This allows health teams to stop a disease from spreading before it becomes a crisis.
Health authorities lack a rapid-response system to detect and map invasive, disease-carrying mosquitoes in real-time. This leads to delayed containment of outbreaks, resulting in preventable deaths and high healthcare costs.
A mobile bio-lab system including ML-powered smart-traps, GEOSS-compliant risk maps, and a citizen-science surveillance platform.
If you are a health agency dealing with invasive species like Aedes albopictus — this project developed a mobile bio-lab that provides real-time viral infection data. This allows first-responders to contain outbreaks and prevent deaths, as seen in the 2018 West Nile outbreak.
If you are a monitoring firm dealing with climate-driven insect migration — this project developed GEOSS compliant vector risk maps. These tools use Copernicus data to predict where disease-carrying mosquitoes will colonize new regions.
If you are a software provider dealing with fragmented outbreak data — this project developed a machine-learning cloud application. It fuses clinical data with IoT sensor inputs to create disease transmission models.
Based on available project data, specific pricing for the end-user is not listed, though the project received an EU contribution of EUR 2,998,500 for development.
Yes, the project aims to provide a global service using interoperable cloud applications and GEOSS compliant maps to support first-responders worldwide.
Based on available project data, the IP details are not specified, but the project is coordinated by IRIDEON SL, an SME.
The system integrates directly with Copernicus, EGNSS, and GEOSS to build vector risk maps and disease transmission models.
The project period runs from 2023-07-01 to 2027-06-30, with a specific objective to create a pathway for future delivery onto the market.
The consortium is lean and industry-heavy, consisting of 7 partners across 3 countries (BE, ES, PT). With a 43% industry ratio and 3 SMEs involved, the project is well-positioned for commercial transition rather than purely academic research.
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Contact us to explore licensing for the smart-trap ML models.
