Using viral load to model disease dynamics

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Science  16 Jul 2021:
Vol. 373, Issue 6552, pp. 280-281
DOI: 10.1126/science.abj4185


Assays for detecting pathogens are used primarily to diagnose infections. Epidemiologists accumulate results from these tests in time series of case reports to conduct disease surveillance, a cornerstone of public health. During the COVID-19 pandemic, these data have been presented on dashboards of health agencies and media outlets all over the world. The shortcomings of these data have also become apparent: Trends can be misleading when demand for testing changes, when testing becomes more available, or when more (or less) accurate tests are rolled out. Time series of case counts are also a major simplification of the raw data used to generate them; modern diagnostics offer more than binary (positive or negative) results—they also estimate viral load, which can indicate the stage of infection. On page 299 of this issue, Hay et al. (1) develop an approach that uses aggregated viral load data to monitor epidemics more accurately than simple case series.

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