epidemiological

Brown professors describe the ‘art’ of drawing the curve in epidemiological modeling

Picture thousands of undergraduate students unpacking their bags at an isolated Brown University campus free from outside contact amid the COVID-19 pandemic. In this simplified scenario, a certain number of these students are carrying stowaway SARS-CoV-2 particles. Others have yet to be exposed to the virus and have not developed immune protection against it.  

Assume that each student breaks the six-foot barrier between them and others at a specified interval as they go about their days, that they continue to pass on the virus for a period of time and that each person’s recovery spans a set period, after which they cannot get COVID-19 again. 

By assigning each of these variables a predicted value, researchers can incorporate them into a model to estimate the rise and fall of COVID-19 — the familiar “curve” — for this hypothetical Brown University. 

While this situation is not exactly representative of the epidemiological models

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