This PathFinder explores collaboration challenges and facilitators for the digitally-enabled prediction of the outbreak, spread and management of epidemics.
The goal is to combine data with data analysis and machine learning tools to create a data-ecosystem for real-time epidemiology and the prediction of pandemics. The Real Time Epidemiology & Dashboard PathFinder also explores digital tools for public health responses and decision making dashboards.
During the COVID-19 pandemic, I-DAIR together with its partners (government, academia and private companies), have started exploring the public health response to COVID-19 from a citizen-centric and science-based perspective. The focus has been on developing modeling diversity, on integrating molecular science data into mobility and other types of epi-data, and on bringing narratives or stories as proxy-variables in place of missing numerical data. Our goal is to build on these pilots to develop more advanced AI models and digital tools to inform and power policy- and decision-makers in real-time on epidemic spread and pathogen evolution.