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

Justin D Silverman; Alex D Washburne

Our study has several limitations. First, the observed ILI surge may represent more than just 165 SARS-CoV-2 infected patients. A second epidemic of a non-seasonal pathogen that presents with ILI could confound our estimates of ILI due to SARS-CoV-2. Alternatively, it is also possible that our use of ILI data has underestimated the prevalence of SARS-CoV-2 within the US. While early clinical reports focused on cough and fever as the dominant features of COVID [5] , other reports have documented digestive symptoms as the complaint affecting up to half of patients 170 with laboratory-confirmed COVID [12] , and alternative presentations, including asymptomatic or unnoticeable infections, could result in ILI surges underestimating SARS-CoV-2 prevalence. Additionally, our models have several limitations. First we assume that ILI prevalence within states can be scaled to case counts at the state level. This is based on the assumption that the average number of cases seen by sentinel providers in a given week is representative of the 175 average number of patients seen by all providers within that state in a given week. Errors in this assumptions would cause proportional errors in our estimated case counts and syndromic case detection rate. Second, our epidemic models are crude, US-wide SEIR models varying by growth rate alone and as such do not capture regional variation or intervention-induced changes in transmission. Our models were used to estimate growth rates from ILI for testing with COVID 180 data and to estimate the mutual dependency of growth rate, the lag between the onset of infection and presentation to a doctor, and clinical rates; these models were not intended to be fine-grained forecasts for municipality hospital burden and other common goals for COVID models. Finer models with regional demographic, and case-severity compartments are needed to translate our range of estimated prevalence, growth rate, and clinical rates into actionable models for public 185 health managers.