Imagine a time when your virus-blocking face covering is like an umbrella. Most days, it stays in your closet or is stowed somewhere in your car. But when a COVID-19 outbreak is in the forecast, you can put it to use.

Beyond that, an inclement viral forecast might induce you to choose an outdoor table when meeting a friend for coffee. If catching the coronavirus is likely to make you seriously ill, you might opt to work from home or attend church services online until the threat has passed.

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A man, left, arrives at a coronavirus testing site without wearing a facemask Dec. 16, 2020 in Los Angeles.

Such a future assumes that Americans will heed public health warnings about the pandemic virus — and that is a big if. It also assumes the existence of a system that can reliably predict imminent outbreaks with few false alarms, and with enough timeliness and geographic precision that the public will trust its forecasts.

A group of would-be forecasters says it’s got the makings for such a system. Their proposal for building a viral weather report was published recently in the journal Science Advances.

Like the meteorological models that drive weather forecasts, the system to predict COVID-19 outbreaks emerges from a river of data fed by hundreds of streams of local and global information. They include time-stamped internet searches for symptoms such as chest tightness, loss of smell or exhaustion; geolocated tweets that include terms like “corona,” “pandemic,” or “panic buying”; aggregated location data from smartphones that reveal how much people are traveling; and a decline in online requests for directions, indicating that fewer folks are going out.

The resulting volume of information is far too much for humans to manage, let alone interpret. But with the help of powerful computers and software trained to winnow, interpret and learn from the data, a map begins to emerge.

If you check that map against historical data — in this case, two years of pandemic experience in 93 counties — and update it accordingly, you may have the makings of a forecasting system for disease outbreaks.

That’s exactly what the team led by a Northeastern University computer scientist has done. In their bid to create an early-warning system for COVID-19 outbreaks, the study authors built a “machine learning” system capable of chewing through millions of digital traces, incorporating new local developments, refining its focus on accurate signals of illness, and generating timely notices of impending local surges of COVID-19.

Among the many internet searches it scoured, one proved to be a particularly good warning sign of an impending outbreak: “How long does COVID last?”

When tested against real-world data, the researchers’ machine-learning method anticipated upticks of local viral spread as many as six weeks in advance. Its alarm bells would go off roughly at the point where each infected person was likely to spread the virus to at least one more person.

Put to the test of anticipating 367 actual county-wide outbreaks, the program provided accurate early warnings of 337 — or 92% — of them. Of the remaining 30 outbreaks, it recognized 23 just as they would have become evident to human health officials.

Once the omicron variant began to circulate widely in the United States, the early-warning system was able to detect early evidence of 87% of outbreaks at the county level.

A predictive system with these capabilities might prove useful for local, state and national public health officials who need to plan for COVID-19 outbreaks and warn vulnerable citizens that the coronavirus is threatening an imminent local resurgence.

But “we’re looking beyond” COVID, said Mauricio Santillana, who directs Northeastern’s Machine Intelligence Group for the Betterment of Health and the Environment.

“Our work is aimed at documenting what techniques and approaches might be useful not just for this, but for the next pandemic,” he said. “We’re gaining trust from public health officials so they won’t need more convincing” when another disease begins spreading across the country.

That may not be an easy sell to state public health agencies and the Centers for Disease Control and Prevention, all of which struggled to keep up with pandemic data and incorporate new methods of tracking the virus’ spread. The CDC’s inability to adapt and communicate effectively during the pandemic led to some “pretty dramatic, pretty public mistakes,” Dr. Rochelle Walensky, the agency’s director, has acknowledged. Only “changing culture” will prepare the federal agency for the next pandemic, she warned.

The CDC’s lackluster efforts to develop prediction tools have not paved the way to easy acceptance either. A 2022 assessment of forecasting efforts used by the CDC concluded that most “have failed to reliably predict rapid changes” in COVID-19 cases and hospitalizations. The authors of that assessment warned that the systems developed to date “should not be relied upon for decisions about the possibility or timing of rapid changes in trends.”

Anasse Bari, an expert in machine learning at New York University, called the new early-warning system “very promising,” though “still experimental.”

“The machine learning methods presented in the paper are good, mature and very well studied,” said Bari, who was not involved in the research. But he cautioned that in a once-in-a-lifetime emergency such as the pandemic, it would be risky to rely heavily on a new model to predict events.

For starters, Bari noted, this coronavirus’ first encounter with humankind has not produced the long historical record needed to fully test the model’s accuracy.

The CDC and state health departments have only begun to use epidemiological techniques such as phylodynamic genetic sequencing and wastewater surveillance to monitor the spread of the coronavirus. Using machine learning to forecast the location of coming viral surges may take another leap of imagination for these agencies, Santillana said.