Most modeling efforts during the COVID-19 pandemic have sought to address urgent practical concerns. But some groups aim to bolster the theoretical underpinnings of that work instead.
Researchers can’t directly observe many key features of disease transmission. As a result, they rely on statistical models to translate what they can see to what they want to know. But they’re finding that for COVID-19 in particular, some of these methods have been giving them the wrong answers.
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