Imagine having a weather app that doesn't just tell you it's hot, but predicts exactly how many people will get sick from heat or pollen in a specific neighborhood. It's like a high-tech early warning system that connects climate shifts to actual health records. This helps cities and hospitals prepare for the surge in patients before it actually happens.
Public health systems are reactive rather than proactive when facing climate-driven health crises. There is a lack of precise, localized data to predict heat and pollen surges, leading to inefficient resource allocation.
Predictive models for cycle use in 864 cities, a pollen early warning system, and digital surveillance tools using social media data.
If you are a health-tech app developer dealing with unpredictable user demand for allergy relief — this project developed a pollen early warning system and digital surveillance models using TikTok and Google data that can trigger timely user alerts.
If you are an insurance provider dealing with rising claims from heat-related illnesses — this project developed spatial bayesian models to produce small area risk estimates of heat-related mortality to better price risk.
If you are a consultancy dealing with low adoption of green transport — this project developed a predictive model of cycle utilization for 864 cities to help design healthier urban environments.
Based on available project data, no pricing or licensing costs are mentioned as this is an EU-funded research project.
The project is currently refining digital surveillance models and conducting randomized trials for pollen warnings, suggesting it is in the testing phase rather than full industrial scale.
Based on available project data, there are no specific details regarding patents or licensing agreements provided in the summary.
The project period runs from 2022-09-01 to 2027-08-31, indicating that final results and tools will be fully matured by August 2027.
The project integrates multi-modal data from Google searches, TikTok, and temperature forecasts into spatial models and digital surveillance tools.
The consortium is heavily weighted toward research and academia (15 out of 22 partners), but maintains a strategic 18% industry ratio with 4 industrial partners and 2 SMEs. This balance suggests the project is designed to bridge the gap between high-level public health data and practical, market-ready tools across 12 different countries.
Contact the Fundacion Privada Instituto de Salud Global Barcelona
Contact us to explore licensing for the predictive health models.
