Judging when to tighten, or loosen, the local economy has become the world’s most consequential guessing game, and each policymaker has his or her own instincts and benchmarks. The point when hospitals reach 70 percent capacity is a red flag, for instance; so are upticks in coronavirus case counts and deaths.
But as the governors of states like Florida, California and Texas have learned in recent days, such benchmarks make for a poor alarm system. Once the coronavirus finds an opening in the population, it gains a two-week head start on health officials, circulating and multiplying swiftly before its re-emergence becomes apparent at hospitals, testing clinics and elsewhere.
Now, an international team of scientists has developed a model — or, at minimum, the template for a model — that could predict outbreaks about two weeks before they occur, in time to put effective containment measures in place.
In a paper posted on Thursday on arXiv.org, the team, led by Mauricio Santillana and Nicole Kogan of Harvard, presented an algorithm that registered danger 14 days or more before case counts begin to increase. The system uses real-time monitoring of Twitter, Google searches and mobility data from smartphones, among other data streams.
The algorithm, the researchers write, could function “as a thermostat, in a cooling or heating system, to guide intermittent activation or relaxation of public health interventions” — that is, a smoother, safer reopening.
“In most infectious-disease modeling, you project different scenarios based on assumptions made up front,” said Dr. Santillana, director of the Machine Intelligence Lab at Boston Children’s Hospital and an assistant professor of pediatrics and epidemiology at Harvard. “What we’re doing here is observing, without making assumptions. The difference is that our methods are responsive to immediate changes in behavior and we can incorporate those.”
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