Using ILI surveillance to estimate state-specific case detection rates and forecast SARS-CoV-2 spread in the United States
View ORCID ProfileJustin D Silverman, View ORCID ProfileAlex D Washburne
doi: https://doi.org/10.1101/2020.04.01.20050542
This article is a preprint and has not been certified by peer review [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.
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 symptomatic case detection rate of SARS-CoV-2 in the US to be 1/100 to 1/1000. This corresponds to approximately 10 million presumed symptomatic SARS-CoV-2 patients across the US during the week starting on March 15, 2020. 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 three 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 repurpose outpatient time series for early detection and understanding of emerging infectious diseases.
