How Computer Modeling Of COVID-19's Spread Could Help Fight The Virus

Mar 4, 2020
Originally published on March 4, 2020 9:38 am

Scientists who use math and computers to simulate the course of epidemics are taking on the new coronavirus to try to predict how this global outbreak might evolve and how best to tackle it.

But some say more could be done to take advantage of these modeling tools and the researchers' findings.

"It is sort of an ad hoc, volunteer effort, and I think that's something that we could improve upon," says Caitlin Rivers, an infectious diseases modeler with the Johns Hopkins Center for Health Security.

In her view, "modeling plays a really important role in understanding how an outbreak is unfolding, where it might be going, and what we should be thinking through."

But only a small number of the modelers of epidemics work for the federal government, she says. Most are in academia, and they don't have formal relationships with officials who have to make key public health decisions.

Still, they are highly motivated to put their skills to use for the public good. They got to work almost as soon as the first reports of a new virus in China emerged in late December.

They've made similar efforts for past outbreaks, such as Ebola and Zika. But this time around felt different, people in the field say.

"I've never seen the modeling community so galvanized, you know — around this outbreak, and willing to share and collaborate," says Cecile Viboud, a scientist at the Fogarty International Center, based at the National Institutes of Health.

And research teams have been rapidly churning out insights. By mid-January, one group had circulated an analysis listing the top 15 cities at risk of the virus spreading, based on airplane flights and travel data.

Another team announced that it had used the number of infected people detected outside China to help figure out what might really be going on in Wuhan city. Those researchers estimated that there were actually more than 1,700 cases at the time, though the official count of confirmed cases was only 41.

"Putting that information out really quickly helped to bring a lot more attention of other modelers to say, 'There are now things that we can do, so let's do that,'" says Rosalind Eggo, an infectious disease modeler at the London School of Hygiene & Tropical Medicine.

She and her colleagues recently took the best available information about transmission and looked at how feasible it would be to control the virus by isolating sick people and tracking down every person they'd had contact with.

"That's a study that we did to try and give useful information to policymakers about what they would need to do with that strategy in order to have a good chance of controlling outbreaks," says Eggo.

What they found is that public health workers would need to trace a high percentage of a sick person's contacts to be effective in shutting down an outbreak quickly.

"You would need to trace at least 80% of contacts to have an 80% chance of containing an outbreak that started with 20 cases within three months," she says. "So that's quite a high number. And it's feasible in some places and potentially infeasible in others."

Her group also looked at the utility of screening airport travelers to detect infected people. One simulation showed that if 100 infected travelers began a journey on a 12-hour flight, about half would not be detected by inbound or outbound traveler screening that was based on symptoms.

"Due to the natural history of the infection, people spend about five days from when they get infected to when they start showing symptoms," Eggo explains. "In that time period, they can travel and won't be detected."

Studies like these can help public health officials decide where to concentrate their time, energy and money.

During the Ebola epidemic in West Africa in 2014, for example, models forecast that the outbreak would continue to grow rapidly without aggressive intervention.

"These forecasts likely contributed to the acceleration of the international response and provided guidance for how resources might be effectively deployed," according to a recent commentary on "outbreak science."

In recent weeks, modelers inside and outside the government have been able to dial into weekly conference calls held by the Centers for Disease Control and Prevention.

The CDC typically gives an update during these calls on where things stand with the new coronavirus and talks about the questions it needs help with, says Viboud. She says one such call had more than 80 people on the line.

Epidemic modelers also have been communicating through the instant messaging platform Slack, as well as Twitter.

"It's never been as organized as it is now," Viboud says.

Still, a computer model is only as good as the data that get put into it.

"Right now the quality of the data is so uncertain that we don't know how good the models are going to be in projecting this kind of outbreak," says Marc Lipsitch, an epidemiologist at the Harvard T.H. Chan School of Public Health.

Modelers are eager for more information about critical issues, such as how many people are infected without symptoms and how much they can spread the virus to others.

Even without perfect data, however, Lipsitch says that virtual outbreaks studied in computers can help policymakers just get their heads around various possible scenarios.

"It's really that models help you think about things," says Lipsitch, "rather than that they tell you things for sure that you didn't know."

Some modelers believe that rather than just using models to react to emerging outbreaks, it would be better for the world to invest in real-time disease forecasting on an ongoing basis.

Sara Del Valle, a mathematical and computational epidemiologist at Los Alamos National Laboratory, says she'd like to see a global center set up to constantly collect information about circulating infectious illnesses.

Much like how the National Weather Service provides forecasts to help people prepare for their local weather, she says, such a center could tell people about their local risk of infectious diseases.

"People could actually, you know, just open their phones and open an app and then see the probability of infection," she says. "It could say like, 'There's a 20% probability of getting flu in your community, based on what is spreading there.'"

The advantage of a system like that, she says, is that all of the researchers, plus data-gathering and modeling resources, would already be in place if a new infectious disease threat like this coronavirus suddenly emerged.

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NOEL KING, HOST:

The coronavirus continues to spread across the world, and scientists are using data to replicate the course of the epidemic. They're trying to help public health officials understand how the situation might evolve and how to best respond. NPR's Nell Greenfieldboyce reports that this is a mostly impromptu volunteer effort, and some people think that should change.

NELL GREENFIELDBOYCE, BYLINE: The first reports of a new coronavirus emerging in China came in late December, and experts on modeling epidemics got to work. By mid-January, one team had predicted where the virus might spread based on airplane flight data. Another group used the number of cases detected outside China to help figure out what might really be going on in Wuhan city. They estimated over 1,700 cases, though the official count was only 41.

ROSALIND EGGO: Putting that information out really quickly helped to bring a lot more attention of other modelers to say there are now things that we can do, so let's do that.

GREENFIELDBOYCE: Rosalind Eggo is an assistant professor of mathematical modelling at the London School of Hygiene and Tropical Medicine. Her group recently looked at how feasible it would be to control the virus by isolating sick people and tracking down every person they'd had contact with.

EGGO: To try and understand if this intervention strategy can prevent outbreaks occurring in new areas that haven't yet seen transmission.

GREENFIELDBOYCE: They found that you'd have to track down a high percentage of a sick person's contacts. In most scenarios, this wouldn't be feasible.

EGGO: And so that's a study that we did to try and give useful information to policymakers about what they would need to do in that strategy in order to have a good chance of controlling outbreaks.

GREENFIELDBOYCE: After all, public health officials have limited resources. They have to decide how to invest their time and money. Computer modelers can help inform all kinds of decisions, from whether or not to close schools to how to deploy a limited supply of vaccines. Modelers have helped make predictions in other recent virus outbreaks, like Zika and Ebola. But Cecile Viboud says this time feels different.

CECILE VIBOUD: And it's never been as organized as it is now (laughter).

GREENFIELDBOYCE: Viboud works at the National Institutes of Health. She's one of dozens of experts who dial in to weekly conference calls held by the Centers for Disease Control and Prevention. She says the agency typically gives an update on where things stand with the virus and talks about what questions it needs help with. Epidemic modelers also communicate through the instant messaging platform Slack, as well as Twitter.

VIBOUD: So I would say that I've never seen the modelling community so galvanized, you know, around this outbreak and willing to share and collaborate.

GREENFIELDBOYCE: The trouble is a computer model is only as good as the data that's put into it. Marc Lipsitch is an epidemiologist at Harvard School of Public Health.

MARC LIPSITCH: Right now the quality of the data is so uncertain that we don't know how good the models are going to be in projecting this kind of outbreak.

GREENFIELDBOYCE: Modelers are eager for more information about how many people are infected without symptoms and how much they can spread the virus to others. Still, even now, Lipsitch says that virtual outbreaks studied in computers are useful. They can help policymakers just get their heads around possible scenarios.

LIPSITCH: Then it's really that models help you think about things rather than that they tell you things for sure that you didn't know.

GREENFIELDBOYCE: Some say outbreak modeling ought to be better funded and better integrated into the government's decision-making process. Caitlin Rivers is a researcher at the Johns Hopkins Center for Health Security.

CAITLIN RIVERS: There are modelers within federal government, but it's - they are small teams. And, you know, they have regular responsibilities that they have to stay on top of. So when something like 2014 Ebola or COVID starts, it's usually the modeling community and academia that provides search support.

GREENFIELDBOYCE: She says these academic scientists work for free as volunteers. They sometimes don't have access to the best data.

RIVERS: And so having a more formal relationship could really improve that data sharing, which in turn would improve the quality of the models.

GREENFIELDBOYCE: And rather than just reacting to outbreaks, maybe the world should invest in real-time disease forecasting. That's the view of Sara Del Valle, a mathematical and computational epidemiologist at Los Alamos National Lab. She'd like to see a global center set up to constantly collect information about all circulating illnesses, hospitalizations. She says it would be for infectious diseases what the National Weather Service is for weather.

SARA DEL VALLE: And people could actually, you know, just open their phones and open an app and then see the probability of infection. It could say, like, there's 20% probability of getting flu in your community based on what's spreading there.

GREENFIELDBOYCE: She says the advantage of a system like that is that all the resources would be in place if a new threat like this coronavirus suddenly emerged.

Nell Greenfieldboyce, NPR News.

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