Journal Article

Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model

Yin, Ming-Ze, Zhu, Qing-Wen, Lü, Xing

2021

Overview

With the spread of the novel coronavirus disease 2019 (COVID-19) around the world, the estimation of the incubation period of COVID-19 has become a hot issue. Based on the doubly interval-censored data model, we assume that the incubation period follows lognormal and Gamma distribution, and estimate the parameters of the incubation period of COVID-19 by adopting the maximum likelihood estimation, expectation maximization algorithm and a newly proposed algorithm (expectation mostly conditional maximization algorithm, referred as ECIMM). The main innovation of this paper lies in two aspects: Firstly, we regard the sample data of the incubation period as the doubly interval-censored data without unnecessary data simplification to improve the accuracy and credibility of the results; secondly, our new ECIMM algorithm enjoys better convergence and universality compared with others. With the framework of this paper, we conclude that 14-day quarantine period can largely interrupt the transmission of COVID-19, however, people who need specially monitoring should be isolated for about 20 days for the sake of safety. The results provide some suggestions for the prevention and control of COVID-19. The newly proposed ECIMM algorithm can also be used to deal with the doubly interval-censored data model appearing in various fields.

• Publisher: Springer Netherlands,Springer Nature B.V
• Subject: Algorithms / Automotive Engineering / Classical Mechanics / Control / Coronaviruses / COVID-19 / Data models / Disease transmission / Dynamical Systems / Engineering / Mathematical models / Maximization / Maximum likelihood estimation / Mechanical Engineering / Optimization / Original Paper / Parameter estimation / Probability distribution functions / Vibration / Viral diseases