Computer Models Of COVID-19 Outbreaks Could Help Stop Coronavirus : Shots - Health News As the world watches the outbreak of a novel coronavirus, epidemiologists are watching simulations of that outbreak on their computers to try to predict what might happen next.

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

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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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