EPFL COVID-19 Real Time Epidemiology I-DAIR Pathfinder

This is an interdisciplinary project funded by the Botnar Foundation in which the partners involved aim at developing better technology for digital epidemiology, such as new proximity tracing technologies and protocols, as well as new data mining techniques to predict epidemiological events.
Governments around the world have to design, develop, and deploy disease prevention and control strategies at unprecedented scale and speed. The COVID-19 pandemic has shown how technology can provide essential support to these strategies, offering governments with the means to act quickly and efficiently at large scale. A prime example are Proximity Tracing Apps for COVID-19.
An important lesson learned from deploying these apps is that if technology is to be accepted by society to reach its maximum potential, it must be respectful of privacy and guarantee security whilst being compatible with existing hardware. This project seeks to systematize the design of secure and privacy-preserving technologies for prevention and control by developing and demonstrating building blocks for future use by researchers and governments. The project will complement this work by developing decentralized machine-learning models on novel data sources for digital epidemiology (e.g. social media).