Imagine a weather app, but instead of rain, it predicts where mosquitoes carrying dangerous viruses will strike next. It combines data from smart traps, citizen reports, and satellite imagery to spot risks before they become outbreaks. This helps cities move from reacting to a crisis to stopping it before it starts.
Public health systems are currently reactive to mosquito-borne outbreaks, which is inefficient and costly. There is a lack of integrated, real-time intelligence to predict where diseases like Dengue and West Nile will emerge.
A disease intelligence system combining smart IoT traps, acoustic monitoring, and spatial statistical models to provide probabilistic risk forecasts.
If you are a digital health provider dealing with unpredictable disease spikes — this project developed a disease intelligence system that transforms multi-source data into actionable risk outputs. This allows for seasonal preparedness and strategic planning instead of reactive responses.
If you are an IoT company dealing with low-precision pest monitoring — this project developed smart IoT traps and passive acoustic monitoring for hosts. These tools integrate with climate and hydrological drivers to provide fine-scale risk intelligence.
If you are an insurer dealing with the rising cost of imported tropical diseases — this project developed importation-oriented components and probabilistic forecasts. This helps in quantifying risk in Europe as invasive vectors increase.
Based on available project data, no pricing or cost information is provided as this is a Horizon-RIA research project.
The project is designed for scalability across urban and peri-urban contexts, including specific implementations like D-MOSS for use in Vietnam and Sri Lanka.
Based on available project data, the project emphasizes open science and open innovation strategies, but specific licensing terms are not listed.
It is co-designed with public health administrations to align with daily operations, seasonal preparedness, and strategic planning cycles.
The project period runs from 2023-01-01 to 2026-12-31, indicating it is currently in the development and validation phase.
The consortium is well-balanced for technology transfer, featuring 12 partners across 6 countries. With a 33% industry ratio (including 4 industry partners and 3 SMEs), there is a strong commercial bridge to translate the research from the 7 academic and research entities into marketable digital health tools.
Contact AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS in Spain
Contact us to explore licensing opportunities for the D-MOSS early warning system.
