Author: Alex Perkins; Sean M. Cavany; Sean M Moore; Rachel J Oidtman; Anita Lerch; Marya Poterek
Title: Estimating unobserved SARS-CoV-2 infections in the United States Document date: 2020_3_18
ID: fb8mca1h_28
Snippet: We estimated how the probability of detecting locally acquired, symptomatic infections, Ï local , changed over time. These estimates were based on the number of symptomatic cases reported each day, C(t), and our model's predictions for the number of symptomatic infections that could have been reported each day, S(t), after accounting for a delay between symptom onset and reporting. We assumed a uniform prior for Ï local , and on each day estima.....
Document: We estimated how the probability of detecting locally acquired, symptomatic infections, Ï local , changed over time. These estimates were based on the number of symptomatic cases reported each day, C(t), and our model's predictions for the number of symptomatic infections that could have been reported each day, S(t), after accounting for a delay between symptom onset and reporting. We assumed a uniform prior for Ï local , and on each day estimated a posterior equal to Ï local (t) ~ Beta(1+C(t), 1+S(t)-C(t)). We then smoothed over each of 1,000 replicates of independent daily draws of logit-transformed values of Ï local (t) using the smooth.spline function in the stats package in R, using weekly knots (Fig. S2 ).
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