A surge in flu-like infections in the U.S. in March of 2020 suggests that the likely number of COVID-19 cases was far larger than official estimates, according to a new study of existing surveillance networks for influenza-like infections (ILIs). The findings support a scenario where more than 8.7 million new SARS-CoV-2 infections appeared in the U.S. during March, and estimate that more than 80% of these cases remained unidentified as the outbreak rapidly spread. Furthermore, the results suggest that surveillance networks for influenza-like disease offer an important tool to estimate the prevalence of COVID-19, which has been hard to pin down. Many scientists suspect that the true rate of SARS-CoV-2 infections is higher than the number of confirmed cases due to the low availability of testing and because some infected individuals show no symptoms or only mild flu-like symptoms. Using an outpatient surveillance system for diseases with symptoms that resemble influenza, Justin Silverman and colleagues determined the prevalence of non-influenza ILIs in the U.S. annually using surveillance data starting from 2010. In March of 2020, they observed a huge spike in ILIs exceeding normal seasonal numbers in various states - New York, for example, showed twice its previous record for ILIs in the fourth week of March. The authors also saw that the dynamics of non-influenza ILIs closely matched patterns of confirmed COVID-19 cases. After calculating that approximately 32% of people infected with SARS-CoV-2 sought medical care, Silverman et al. found that at least 8.7 million SARS-CoV-2 infections occurred between March 8 and March 28 in the U.S., with new deaths doubling approximately every 3 days. The team concludes that the initial spread of COVID-19 therefore included a large undiagnosed outpatient population who potentially showed milder symptoms compared with those who were hospitalized.
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Science Translational Medicine