Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak

Abstract

A novel coronavirus (SARS-CoV-2) has recently emerged as a global threat. As the epidemic progresses, many disease modelers have focused on estimating the basic reproductive number R 0 -the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modeling approaches and resulting estimates of R 0 vary widely, despite relying on similar data sources. Here, we present a novel statistical framework for comparing and combining different estimates of R 0 across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate r, the mean generation intervalḠ, and the generation-interval dispersion κ. We then apply our framework to early estimates of R 0 for the SARS-CoV-2 outbreak. We show that many early R 0 estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of R 0 , including the shape of the generation-interval distribution, in efforts to estimate R 0 at the outset of an epidemic.

Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach

“…Our estimation of R t during the curve up period aligns with other recent studies 24 1 ). Furthermore, our results showed both transportation restriction and quarantine measures were able to limit virus transmission.…”

Intervention Serology and Interaction Substitution: Modeling the Role of ‘Shield Immunity’ in Reducing COVID-19 Epidemic Spread

“…Outbreak scenarios differ in transmission rates, with R 0 = 1.57 and 2.33 in the low and high scenarios, respectively. Early estimates of R 0 from Wuhan are consistent with a 95% CI of between 2.1 and 4.5 [28], putting our high scenario on the conservative end of estimated ranges. However, the R 0 of the high scenario we examine here is consistent with the range of 2.0 to 2.6 considered by the Imperial College London group [5], and with the median of R ef f = 2.38 (95% CI: 2.04-2.77) as estimated via stochastic model fits to outbreak data in China that accounts for undocumented transmission [26].…”

Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China

“…A further source of uncertainty is in the distribution of the generation time interval, since a different distribution gives a different outbreak risk even with the same R loc . For our calculations, we used the distribution from [19] (see also [20]); a more in-depth discussion of this topic may be found in [36]. Knowing R loc and the generation interval are needed not only to have a better quantitative risk estimation, but also for guidance as to which types of control measures may reduce the outbreak risk the most effectively.…”