Can an Algorithm Predict the Pandemic’s Next Moves?
by Benedict Carey, NYT
An international team of scientists has developed a computer model to predict Covid-19 outbreaks about two weeks before they happen. Team leaders Mauricio Santillana and Nicole Kogan of Harvard University created the algorithm, which monitors Twitter, Google searches, and mobility data from smartphones in real time in order to forecast outbreaks 14 days or more before case counts start rising.
“We know that no single data stream is useful in isolation,” said Madhav Marathe, a computer scientist at the University of Virginia. “The contribution of this new paper is that they have a good, wide variety of streams.”
In the new paper, the team analyzed real-time data from four sources, in addition to Google: Covid-related Twitter posts, geotagged for location; doctors’ searches on a physician platform called UpToDate; anonymous mobility data from smartphones; and readings from the Kinsa Smart Thermometer, which uploads to an app. It integrated those data streams with a sophisticated prediction model developed at Northeastern University, based on how people move and interact in communities.
Santillana said the model is based on observations rather than assumptions, employing methods responsive to immediate behavioral changes. The team integrated multiple real-time data streams with a prediction model from Northeastern University, based on people’s movements and interactions in communities, and assessed the value of trends in the data stream by observing how each correlated with case counts and deaths over March and April in each state.
Santillana said: “And we don’t see this data as replacing traditional surveillance but confirming it. It’s the kind of information that can enable decision makers to say, ‘Let’s not wait one more week, let’s act now.’”
DCL: This is an example of CEP. It is an attempt to build a real-time event processing system for predicting trends in a pandemic. This type of CEP application is amply illustrated in The Power of Events and in Event Processing For Business, and not just for predicting pandemics but for Tsunami prediction, Smart Cities, and many other aspects of society.
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