Wolters Kluwer, Health has announced that a new predictive algorithm is using UpToDate® search activity from clinicians at the point of care - together with data streams from social media, internet search trends and mobility data from smartphones - to serve as a coronavirus early-warning system that could forecast an outbreak two to three weeks in advance. The algorithm is presented in a pre-published paper on arXiv.com.
Researchers, including Dr. Mauricio Santillana, Director of the Machine Intelligence Lab, Boston Children’s Hospital, and an Assistant Professor of Pediatrics and Epidemiology at Harvard Medical School, created a novel model to combine information from clinicians’ searches in UpToDate, as well as COVID-related Twitter posts that included geotag location information, Google searches on COVID topics, anonymous mobility data from smartphones, predictions from a model developed by researchers at Northeastern University, and temperature readings from the Kinsa Smart Thermometer app.
In order to test the ability of the algorithm to forecast COVID-19 outbreaks, researchers compared how each data stream correlated with case counts and deaths in each state in March and April. For example, an abrupt increase in tweets about COVID-19 appeared over a week before reported cases spiked dramatically in New York in the middle of March. Related Google searches and Kinsa temperature readings increased several days before the spike as well. The hybrid algorithm estimated outbreaks by an average of 21 days.
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