Recognition of months of work studying the prevalence of coronavirus in real time
Over 150 researchers and 50 organisations participated in the call for proposals entitled the COVID-19 Symptom Data Challenge, sponsored by Facebook Data for Good, with the participation of Delphi Group-Carnegie Mellon University (CMU), the Joint Program on Survey Methodology-University of Maryland (UMD), the Duke Margolis Center for Health Policy, and Resolve to Save Lives, an initiative by Vital Strategies.
Organised by Catalyst @Health 2.0, on Wednesday the 16th the announcement was made that the award went to the project DeepOutbreak (Georgia Tech), during a ceremony which saw the participation of renowned people including Dr Tom Frieden (President and CEO of Resolve to Save Lives); Dr Mark McClellan (Director of the Duke-Margolis Center for Health Policy); Farzad Mostashari, CEO of Aledade; Kang-Xing Jin, Head of Health at Facebook, and specialist health and technology journalist Christina Farr (NBC). During the event, the finalists pointed out the most noteworthy issues from their research studies.
CoronaSurveys won 5000 dollars in recognition of its status as a finalist, thanks to the results obtained in recent months for the real-time collection, via simple anonymous surveys, of the number of COVID-19 cases in the world and innovations like collecting data on vaccination. Great collaborative work based on an indirect reporting method called Network Scale-up, which leads to extremely precise estimates from a moderate number of responses. The survey is available in 60 languages from countries round the world (150 of them regional), with data and estimates available for use by political leaders and the scientific community.
As pointed out by Antonio Fernández Anta, Research Professor at IMDEA Networks and project promoter since March 2020, along with an extensive team of university researchers and research centres: ‘Participation in Data Challenge has been a unique experience. On the one hand, it has obligated the CoronaSurveys team to start up a working line on prediction and early warning, which we hope will be extremely useful. On the other, it has let us give widespread visibility to the combination of obtaining indirect information and applying the Network Scale-up Method as a technique with many advantages for its application to public health.’