Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States

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Science Translational Medicine  29 Jul 2020:
Vol. 12, Issue 554, eabc1126
DOI: 10.1126/scitranslmed.abc1126

Inferring infections

The prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in many countries is likely underestimated because of limited or inaccurate testing and undetected asymptomatic cases. Silverman et al. used data collected through an existing infrastructure for reporting influenza-like illness to estimate the actual prevalence of SARS-CoV-2 infections in US states. They used a statistical model to estimate the proportion of observed influenza-like illness during the early pandemic that was in excess of the seasonal variation seen in prior years, then adjusted this estimate to take into account subclinical infections. Their model estimated that more than 80% of individuals with SARS-CoV-2 infections in the US went undetected in March 2020.


Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections to date has relied heavily on reverse transcription polymerase chain reaction testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or subclinical infections have resulted in an undercounting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with coronavirus disease 2019 (COVID-19) case counts across states. If one-third of patients infected with SARS-CoV-2 in the United States sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the United States during the 3-week period from 8 to 28 March 2020. Combining excess ILI counts with the date of onset of community transmission in the United States, we also show that the early epidemic in the United States was unlikely to have been doubling slower than every 4 days. Together, these results suggest a conceptual model for the COVID-19 epidemic in the United States characterized by rapid spread across the United States with more than 80% infected individuals remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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