As the shortage of COVID-19 diagnostic test kits in the United States becomes more dire, healthcare providers are left with a dilemma—do we test everyone and risk running out, or do we ration tests and risk missing cases?
“Right now we’re not testing everyone because we have false positives and a limited supply of tests,” says Samuel Scarpino, an assistant professor at Northeastern who studies infectious diseases. As of Tuesday, more than 90,000 people have been diagnosed with COVID-19, and more than 3,000 have died.
The problem is only exacerbated by the global anxiety the disease has instilled. “If everyone with the flu starts getting tested for coronavirus, we’re going to run out of tests for people who actually need them,” he says.
But without this fear-induced “social contagion,” as Scarpino calls it, the college student in Boston who tested positive for COVID-19, for example, might not have thought twice about his runny nose.
“He wasn’t very sick,” says Scarpino, “But he had just come from China and was scared. He was infected with this social contagion, so we found this case that we would normally never find in a big outbreak because individuals with mild cases might not get tested.”
Whether to test for COVID-19 is not just a question for nurses and doctors, though. When predicting the spread of diseases, epidemiologists must also consider questions like these, details they haven’t been able to account for previously because, mathematically, they didn’t know how.
Or rather, they did know how. They just didn’t realize it until now.
In a recent paper published in Nature Physics, Scarpino shows how the models currently used to predict social trends can also forecast the spread of contagious diseases.
“It’s a funny tool,” Scarpino says. “We know the model is wrong, but we can still use it early in an outbreak to learn something important about the progression of an epidemic.”
In other words, even though the model was created to show how, for example, memes spread, it still works when applied to contagious diseases.
As it stands, Scarpino says, epidemiologists usually look only at diseases in isolation without examining how they might interact with other pathogens or change depending on people’s behavior. Hypothetically, the size of an outbreak should be proportional to the rate of transmission.
This is called a simple model. For biological contagions, that would mean that for every sick person you encounter, your chances of contracting that disease increase linearly. The number of sick people you come into contact with directly represents your chances of getting sick. This is often an inaccurate model because it fails to account for other factors, such as your lifestyle or your health, that change the actual likelihood that you will contract the disease.
But Scarpino proposes that epidemiologists adopt a complex model used primarily to track social trends. According to this model, multiple exposures are required for a person to become affected by a contagion, whether that’s a hashtag or a disease.
“One way to think about it is, for example, I’m online, and if I see an idea five or six times, I become much more likely to engage with that meme than if I only see it from one or two people.”
To apply this analogy to biological contagions, “it’s not increasingly likely you’ll become infected as you come into contact with one, two, three, four people” like the simple models suggests, Scarpino says.
“It’s that you almost have no chance of getting infected until you come into contact simultaneously with 10 people and then you get infected with close to 100 percent probability,” he says, with the caveat that this particular example is only hypothetical.
“We know that’s not how biological contagions work in real life,” Scarpino says. “They’re much more complicated than social contagions. But mathematically, they’re the same.”
Scarpino says that transmission predictions change dramatically once epidemiologists start accounting for other biological and social contagions that are happening simultaneously with an outbreak, which is why it’s important to use a complex rather than a simple model.
“We know that coronavirus is probably interacting with influenza in the United States,” Scarpino says. Exactly how is it interacting? Scientists don’t know yet.
“But luckily, we have a model now that we can use to find out if coronavirus is interacting positively or negatively or not at all with the flu,” Scarpino says.
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