Using influenza surveillance networks to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States

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Abstract
Detection of SARS-CoV-2 infections to date has relied on RT-PCR testing. However, a failure to identify early cases imported to a country, bottlenecks in RT-PCR testing, and the existence of infections which are asymptomatic, sub-clinical, or with an alternative presentation than the standard cough and fever have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how publicly available CDC influenza-like illness (ILI) outpatient surveillance data can be repurposed to estimate the detection rate of symptomatic SARS-CoV-2 infections. We find a surge of non-influenza ILI above the seasonal average and show that this surge is correlated with COVID case counts across states. By quantifying the number of excess ILI patients in March relative to previous years and comparing excess ILI to confirmed COVID case counts, we estimate the syndromic case detection rate of SARS-CoV-2 in the US to be less than 13%. If only 1/3 of patients infected with SARS-CoV-2 sought care, the ILI surge would correspond to more than 8.7 million new SARS-CoV-2 infections across the US during the three week period from March 8 to March 28. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to be doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID epidemic in the US in which rapid spread across the US are combined with a large population of infected patients with presumably mild-to-moderate clinical symptoms. We emphasize the importance of testing these findings with seroprevalence data, and discuss the broader potential to use syndromic time series for early detection and understanding of emerging infectious diseases.
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