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

We made the following prior specifications: We set the bandwidth parameter for the squared exponential kernel as ρ = 3 representing a strong local correlation in time that died off sharply 235 beyond 3 weeks, α = 1 representing a signal to noise ratio of of approximately 1, υ = 1 and ξ = 1 representing weak prior knowledge regarding the overall scale of variation in the the latent space. Finally, we set θ = −2.197 representing an off-season prevalance of 0.1% non-influenza ILI. Samples from the posterior predictive density p(π it |y i1 , . . . ,ỹ iT , n i1 , . . . , n iT ) were collected using the function basset from the R package stray [16]; a total of 4000 such samples were collected in 240 this analysis. We define the prevalence of non-influenza ILI in excess of normal seasonal variation as y * it =ỹ it /n it −π it . To exclude variation attributable to unseasonably high rates of other ILI causing viruses (such as the outbreak of RSV in Washington state in November-December 2019) we only investigate y * it for weeks after March 7th 2020 as only these later weeks had high correlation to the COVID 245 confirmed case rate ( Figure S1 ).