Networked data and COVID-19

Abstract

The COVID-19 pandemic has upended our societies and re-shaped the way we go about our day-to-day lives---from how we work and interact to the way we buy groceries and attend school. Leveraging global data sets that represent billions of people, I will present a series of studies exploring how our behavior [2, 10], mobility patterns [6, 7], and social networks [3, 9] have altered and been altered by COVID-19 and the non-pharmaceutical interventions implemented to control its spread. Next, I will examine how we can better incorporate stochasticity and social network heterogeneity [4] and link directionality [1] into forecasting pandemic risk. With these results, I will demonstrate how the complexity of COVID-19 creates epistemological challenges associated with model identifiability [5, 8, 11]. Finally, I will discuss work by Global.health, a new collaborative network of researchers, technologists, and public health experts that has developed and built an open access platform for collecting, storing, securing, and sharing anonymized, individual-level COVID-19 data. Currently, our data includes almost 30M individual-level cases from 160 countries, which are tagged with up to 40 fields of meta-data. Writing for The New York Times Magazine, Steven Johnson said the data captured by Global.health, "may well be the single most accurate portrait of the virus's spread through the human population in existence."