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From the first case of COVID-19 confirmed in Wuhan, the capital of Hubei Province, China, in early December 2019, it has been found in more than 160 countries and caused over 11,000 deaths as of March 20, 2020. Wuhan, as the city where the epidemic first broke out, has made great sacrifices to block the possible transmission. In this research, we estimate the case fatality rate (CFR) of COVID-19 and quantify the effect of quarantine strategy utilized in Wuhan by developing an extended Susceptible-Infected-Recovered (SIR) model. The outcomes suggest that the CFR is 4.4% (95% CI [3.6%, 5.2%]) and the effect of the quarantine strategy is 99.3% (95% CI [99.2%, 99.5%]), which implies that such a method can significantly reduce the number of infections.

In this paper, we investigate various confidence intervals for the risk ratio under inverse sampling (also known as negative binomial sampling). Three existing confidence intervals (namely, the confidence intervals that are based on Fieller's theorem, the delta method and the F-statistic) are reviewed and three new confidence intervals (namely, the score, likelihood ratio and saddlepoint approximation (SA)-based confidence intervals) are developed. Comparative studies among these confidence intervals through Monte Carlo simulations are evaluated in terms of their coverage probabilities and expected interval widths under different settings. Our simulation results suggest that the SA-based confidence interval is generally more appealing. We illustrate these confidence interval construction methods with real data sets from a drug comparison study and a congenital heart disease study.