Making data for good better
Caroline O. Buckee, Satchit Balsari, Andrew Schroeder · PLOS Digital Health · 2022
Today's societies produce vast-and increasing-amounts of digital data "exhaust" from daily human activities such as the use of mobile devices, wearables and home sensors; store purchases; and online engagement on social media.Such data have historically been used by corporations to sell products and make life more convenient (even if in unevenly distributed ways), and in limited academic circles, to solve public heath challenges.During the COVID-19 pandemic, however, technology companies started making aggregated human mobility datasets widely available, as part of their corporate social responsibility efforts or "data for good" programs.[1,2] Many companies, researchers, and policy makers unfamiliar with the academic literature realized-for the first time-the potential use of digital data from mobile phones to monitor social distancing and other emergency public health measures.[3]An avalanche of social distancing dashboards, prediction tools, heat-maps, digital contact tracing programs, and symptom-based COVID-19 prediction apps, followed.Despite the long standing excitement about the potential for digital tools, Big Data and AI to transform our lives, these innovations-with some exceptions-have so far had little impact on the greatest public health emergency of our time.[4,5] Attempts to use digital data streams to rapidly produce public health insights that were not only relevant for local contexts in cities and countries around the world, but also available to decision makers who needed them, exposed enormous gaps across the translational pipeline.The insights from novel data streams which could help drive precise, impactful health programs, and bring effective aid to communities, found limited use among public health and emergency response systems.[6] We share here our experience from the COVID-19 Mobility Data Network (CMDN), now Crisis Ready (crisisready.io),a global collaboration of researchers, mostly infectious disease epidemiologists and data scientists, who served as trusted intermediaries between technology companies willing to share vast amounts of digital data, and policy makers, struggling to incorporate insights from these novel data streams into their decision making.[7]Through our experience with the Network, and using human mobility data as an illustrative example, we recognize three sets of barriers to the successful application of large digital datasets for public good.First, in the absence of pre-established working relationships with technology companies and data brokers, the data remain primarily confined within private circuits of ownership and control.During the pandemic, data sharing agreements between large technology companies and researchers were hastily cobbled together, often without the right kind of domain expertise in the mix.[8]Second, the lack of standardization, interoperability and information on the uncertainty and biases associated with these data, necessitated complex analytical processing by highly specialized domain experts.[9,10] And finally, local public health departments, understandably unfamiliar with these novel data streams, had neither the bandwidth nor the