Estimating unobserved SARS-CoV-2 infections in the United States

Alex Perkins; Sean M. Cavany; Sean M Moore; Rachel J Oidtman; Anita Lerch; Marya Poterek

All parameter values, and their associated distributions, are described in Table 1 . Where parameter distributions were described in the literature using medians and interval measures of spread, we used the optim function in R to estimate parameters of those distributions that matched distribution moments reported by those studies. In that sense, all parameters in our analysis were treated as random variables, with associated uncertainty accounted for throughout our analysis. For the delay between symptom onset and case notification, we fitted a gamma distribution to data on the delay between symptoms and reporting for 26 US cases in the MIDAS line list data; the gamma distribution fitted the data better than negative binomial or log-normal distributions according to AIC (133.5, 134.6, and 134.0, respectively) (Fig. S1 ). Our mean estimate of 6.0 for this delay is in line with previous estimates from China of 5.8 by Li et al. (36) and 5.5 by Bi et al. (37) . Three key parameters -R, the serial interval, and the incubation period -were taken from a single reference (3) to ensure that those estimates were consistent with each other. That is important because R and the serial interval jointly control the epidemic growth rate (38) , so taking estimates of R and the serial interval from different studies could have led to unrealistic projections of epidemic growth rate.