“Fast forward a month, and the world did have a pandemic on its hands. Modelers around the world scrambled to forecast the spread of SARS-CoV-2 and the COVID-19 disease it causes in their own countries and communities. Many epidemiologists were then and still are tasked by policymakers with answering urgent questions: How fast will it spread? How many hospital beds and ventilators will we need? When can we lift lockdowns and restart our economies again? Will we see a second wave? Will it be worse than the first?
Getting good estimates for R0—a key epidemiological metric that reflects the transmissibility of a virus—is key to answering such questions with accuracy. But R0 is notoriously tricky to nail down. It depends not only on the biological characteristics of a virus—which are a mystery at the beginning of an outbreak—but also on understanding how often people come into contact with one another. Faced with uncertainty, modelers have to make assumptions about the factors that determine human movement, which can limit the precision of their models and the accuracy of the predictions they generate.
“R0 is a metric that is, first of all, poorly measured. And secondly, it’s informing models that result in public health action,” says Juan B. Gutiérrez, a mathematician at the University of Texas at San Antonio. “If we get it wrong, the public health action will be misplaced.”
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