A survey launched by a team of researchers from the Joint Program in Survey Methodology, College of Information Studies and Department of Geographical Sciences at the pandemic’s onset proves it can function as an early warning system for changes in outbreaks.
Survey Launched at Pandemic’s Onset Proves It Can Function as Early Warning System for Changes in Outbreaks
Compared to local governments’ attempts at predicting COVID-19 case count numbers, a global Facebook survey launched by University of Maryland researchers last spring to identify and track disease symptoms gave more accurate results in 77% of the 114 countries and territories included in the survey.
The team from the Joint Program in Survey Methodology (JPSM), College of Information Studies and Department of Geographical Sciences successfully trained a machine learning algorithm to identify which COVID-19 symptoms were most often associated with a positive result—critical knowledge during the early days of the pandemic when telltale symptoms were still being identified. They used that information, plus additional responses, to make close-to-reality predictions about case counts at that time.
The findings from the University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS) were presented in a paper published today in the Proceedings of the National Academy of Sciences.
“As intended, this is an early warning system for changes in outbreaks distributed geographically over time,” said Professor Frauke Kreuter, JPSM director, whose team worked with Facebook, the World Health Organization and researchers at other universities to develop the questionnaire. “It was reassuring to see we don’t have to wait for someone to show up in a hospital, that there are other ways of collecting data very quickly and globally to get a read on what is going on.”
