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

We simulated local transmission in the United States from January 1 to March 12 using a branching process model, seeded by the aforementioned importation model. Each replicate draw of the number and timing of imported infections seeded one simulation of the branching process model, to maximally represent uncertainty in both importation and transmission processes. The number of secondary infections generated by each infection in the branching process model was drawn from a negative binomial offspring distribution with mean R and dispersion parameter k. Under our baseline scenario, we used a dispersion parameter of k = 1,000, approximating a Poisson distribution, due to a lack of estimates of k for SARS-CoV-2. Under alternative scenarios for k, we considered values of 0.15 and 0.30 to account for superspreading observed in outbreaks of SARS and MERS (33, 34) . The number of secondary infections generated by asymptomatic individuals was also drawn from a negative binomial distribution, but with mean R, where in [0,1]. Whether an individual was symptomatic was determined by a Bernoulli trial with probability equal to the proportion of infections that were asymptomatic in that replicate. Each secondary infection's exposure time was drawn from a log-normal generation interval distribution with mean 4.56 days. In doing so, we assumed that the generation interval followed the same distribution as the serial interval.