The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology

This event will feature Caroline Buckee and be hosted by Applied Machine Learning Days. Learn more and register here. A fee is required to attend.

During the COVID-19 pandemic, public health advice, stay-at-home orders, and self-imposed limitations have dramatically changed human behaviors on a global scale. For the first time in an epidemic, such an unprecedented natural experiment has been sensed through digital platforms in real-time. Digital traces such as mobile phone data, social media posts, search engine queries, bank transactions have revealed how the pandemic has deeply changed our daily habits, such as our mobility patterns, our social and economic interactions, both online and offline.

The power of Machine Learning and Artificial Intelligence applied to such large-scale data has proven to be important to address several policy issues, such as evaluating the effectiveness of interventions aimed at containing the epidemic or measuring the social and economic cost of these interventions.

The aim of the track is to gather contributions from researchers, public health officials, and industry leaders about the insights gained from the analysis of large-scale data in their fight against the pandemic. Different aspects relevant to ML and AI will be discussed: from the ethical issues arising from the analysis of digital traces to the challenges in delivering actionable insights to governments and policymakers.