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

While an ILI surge tightly correlated with COVID case counts across the US strongly suggests that SARS-CoV-2 has potentially infected millions in the US, laboratory confirmation of our hypotheses are needed to test our findings and guide public health decisions. Our conceptual model for the epidemic with the US makes clear and testable predictions. Our model would suggest rela-190 tively high rates of community seropositivity in states that have already seen an ILI surge. A study of ILI patients from mid-march who were never diagnosed with COVID could test our model's predictions about the number and regional prevalence of undetected COVID cases presenting with ILI during that time. If seroprevalence estimates are consistent with our estimated prevalence from these ILI analyses, it would strongly suggest lower case severity rates for COVID and indicate 195 the value of ILI and other public time-series of outpatient illness in facilitating early estimates of crucial epidemiological parameters for rapidly unfolding, novel pandemic diseases. Since not all novel pandemic diseases are expected to present with influenza-like symptoms, surveillance of other common presenting illnesses in the outpatient setting could provide a vital tool for rapidly understanding and responding to novel infectious diseases. 200