Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020

Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020. Methods: Based on the publicly available surveillance data, we identified 21 transmission events, which occurred in Hong Kong, and had primary cases known, as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions, Gamma, Weibull and lognormal, that govern the SI of COVID-19. We selection the distribution according to the Akaike information criterion corrected for small sample size (AICc). Findings: We found the Lognormal distribution performed lightly better than the other two distributions in terms of the AICc. Assuming a Lognormal distribution model, we estimated the mean of SI at 4.9 days (95%CI: 3.6−6.2) and SD of SI at 4.4 days (95%CI: 2.9−8.3) by using the information of all 21 transmission events in Hong Kong. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak.

Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020

Background : The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020. Methods : Based on the publicly available surveillance data, we identified 21 transmission events, which occurred in Hong Kong, and had primary cases known, as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions, Gamma, Weibull and lognormal, that govern the SI of COVID-19. We selection the distribution according to the Akaike information criterion corrected for small sample size (AICc). Findings : We found the Lognormal distribution performed lightly better than the other two distributions in terms of the AICc. Assuming a Lognormal distributed model, we estimated the mean of SI at 3.9 days (95%CI: 2.7−7.3) and SD of SI at 3.1 days (95%CI: 1.7−10.1) by using the information of all 21 transmission events in Hong Kong. Conclusion : The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak.

Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020

Background COVID-19, caused by SARS-CoV-2, first appeared in China and subsequently developed into an ongoing epidemic. Understanding epidemiological factors characterising the transmission dynamics of this disease is of fundamental importance. Aims This study aimed to describe key epidemiological parameters of COVID-19 in Hong Kong. Methods We extracted data of confirmed COVID-19 cases and their close contacts from the publicly available information released by the Hong Kong Centre for Health Protection. We used doubly interval censored likelihood to estimate containment delay and serial interval, by fitting gamma, lognormal and Weibull distributions to respective empirical values using Bayesian framework with right truncation. A generalised linear regression model was employed to identify factors associated with containment delay. Secondary attack rate was also estimated. Results The empirical containment delay was 6.39 days; whereas after adjusting for right truncation with the best-fit Weibull distribution, it was 10.4 days (95% CrI: 7.15 to 19.81). Containment delay increased significantly over time. Local source of infection and number of doctor consultations before isolation were associated with longer containment delay. The empirical serial interval was 4.58–6.06 days; whereas the best-fit lognormal distribution to 26 certain-and-probable infector–infectee paired data gave an estimate of 4.77 days (95% CrI: 3.47 to 6.90) with right-truncation. The secondary attack rate among close contacts was 11.7%. Conclusion With a considerable containment delay and short serial interval, contact-tracing effectiveness may not be optimised to halt the transmission with rapid generations replacement. Our study highlights the transmission risk of social interaction and pivotal role of physical distancing in suppressing the epidemic.

Serial interval and generation interval for respectively the imported and local infectors estimated using reported contact-tracing data of COVID-19 in China

BackgroundsThe emerging virus, COVID-19, has caused a massive out-break worldwide. Based on the publicly available contact-tracing data, we identified 337 transmission chains from 10 provinces in China and estimated the serial interval (SI) and generation interval (GI) of COVID-19 in China.MethodsInspired by possibly different values of the time-varying reproduction number for the imported cases and the local cases in China, we divided all transmission events into three subsets: imported (the zeroth generation) infecting 1st-generation locals, 1st-generation locals infecting 2nd-generation locals, and others transmissions among 2+ generations. The corresponding SI (GI) is respec-tively denoted as , and . A Bayesian approach with doubly interval-censored likelihood is employed to fit the lognormal, gamma, and Weibull distribution function of the SI and GI using the identified 337 transmission chains.FindingsIt is found that the estimated , and , thus overall both SI and GI decrease when generation increases.

Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020

Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020.Methods: Based on the publicly available surveillance data, we identified 21 transmission events, which occurred in Hong Kong, and had primary cases known, as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions, Gamma, Weibull and lognormal, that govern the SI of COVID-19. We select the distribution according to the Akaike information criterion corrected for small sample size (AICc).Findings: We found the Gamma distribution performed lightly better than the other two distributions. Assuming a Gamma distributed model, we estimated the mean of SI at 4.4 days (95%CI: 2.9−6.7) and SD of SI at 3.0 days (95%CI: 1.8−5.8) by using the information of all 21 transmission events in Hong Kong.Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak.

Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020

BackgroundsThe emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. Based on the publicly available surveillance data, we identified 21 transmission chains in Hong Kong and estimated the serial interval (SI) of COVID-19.MethodsIndex cases were identified and reported after symptoms onset, and contact tracing was conducted to collect the data of the associated secondary cases. An interval censored likelihood framework is adopted to fit a Gamma distribution function to govern the SI of COVID-19.FindingsAssuming a Gamma distributed model, we estimated the mean of SI at 4.4 days (95%CI: 2.9−6.7) and SD of SI at 3.0 days (95%CI: 1.8−5.8) by using the information of all 21 transmission chains in Hong Kong.ConclusionThe SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are recommended in combating the COVID-19 outbreak.

Distribution of Time Between Failures of Numerical Control System Based on Censored Data

The calculation of mean time between failures (MTBF) is of significance in reliability engineering. The precondition before calculation of MTBF is to make sure what failure model the numerical control (NC) system follows. For different distributions, the corresponding calculation methods of MTBF are always different. Undertaken a 2 year period of timing censored test with replacement of NC lathe, authors apply a type I censored likelihood function to make the distribution fitting of time between failures of NC system. The tests of goodness-of-fit applying Hollander’s method demonstrate that the time between failures of NC system follows the Weibull distribution. The conclusion not only deeply analyzes the NC system failure law, but also establishes the basis of calculation for the mean time between failures of NC system based on censored data with replacement.