And as we move forward, we have much bigger risk of flare-ups,” Jenkins says. “If we discover that only 5 percent of a population have recovered and are immune, that means we’ve still got 95 percent of the population susceptible. The assumption for how big any of those fractions of the population are, and how fast they move from one compartment to another, start to matter immediately. They infect the susceptible people, and you see an exponential rise in the infected,” says Helen Jenkins, an infectious disease epidemiologist at the Boston University School of Public Health. “At the beginning, everybody is susceptible and you have a small number of infected people. Assign a best-guess number to those and more, turn a few virtual cranks, and let it run. Those are variables like how many people one infected person infects before being taken off the board by recovery or death, how long it takes one infected person to infect another (also known as the interval generation time), which demographic groups recover or die, and at what rate. Then the modelers make decisions about the rules of the game, based on what they think about how the disease spreads. Some models also drop in an E-SEIR-for people who are “exposed” but not yet infected. A basic version is an SIR model, with three teams: susceptible to infection, infected, and recovered or removed (which is to say, either alive and immune, or dead). Epidemiologists break up a population into “compartments,” a sorting-hat approach to what kind of imaginary people they’re studying. The basic math of a computational model is the kind of thing that seems obvious after someone explains it. Models are imperfect, but they’re better than flying blind-if you use them right. In the case of Covid-19, responding to those models may yet be the difference between global death tolls in the thousands or the millions. But governors and task force leads still tout their models from behind podiums, increasingly famous modeling labs release regular reports into the content mills of the press and social media, and policymakers still use models to make decisions. When data is sparse, which happens when a virus crosses over into humans for the first time, models can vary widely in terms of assumptions, uncertainties, and conclusions.
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Federal agencies like the Centers for Disease Control and Prevention and the National Institutes of Health have modeling teams, as do many universities.Īs with simulations of Earth’s changing climate or what happens when a nuclear bomb detonates in a city, the goal here is to make an informed prediction-within a range of uncertainty-about the future. Since the 2009 outbreak of H1N1 influenza, researchers worldwide have increasingly relied on mathematical models, computer simulations informed by what little data they can find, and some reasoned inferences. The ongoing catastrophe of testing for the virus in the United States means no researcher has even a reliable denominator, an overall number of infections that would be a reasonable starting point for untangling how rapidly the disease spreads. Chinese researchers have only published some of their findings on the spread of Covid-19 in Hubei. During a pandemic, real data is hard to find.
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The city was a ticking time bomb.īut it’s a big if. Their analysis of genetic data indicated the virus had been silently circulating in the Seattle area for weeks and had already infected at least 500 to 600 people. In early March, she and many public health authorities were shaken by an urgent report produced by computational biologists at the Fred Hutchinson Cancer Research Center. As president of the University of Washington Medicine Hospitals and Clinics, Brandenburg oversees the region’s largest health network, which treats more than half a million patients every year. It’s where US health officials confirmed the nation’s first case, back in January, and its first death a month later.
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Until this past week, Seattle had been the center of the Covid-19 pandemic in the United States. From 3,000 miles away in Seattle, as Lisa Brandenburg watched the scenes unfold-isolation wards cobbled together in lobbies, nurses caring for Covid-19 patients in makeshift trash bag gowns, refrigerated mobile morgues idling on the street outside-she couldn’t stop herself from thinking: “That could be us.” Thousands of patients sick with the novel coronavirus have swarmed into emergency rooms and intensive care units.
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In the past few days, New York City’s hospitals have become unrecognizable.