scholarly journals 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

2020 ◽  
Author(s):  
Shi Zhao ◽  
Daozhou Gao ◽  
Zian Zhuang ◽  
Marc KC Chong ◽  
Yongli Cai ◽  
...  
Keyword(s):  
Hong Kong ◽  
Contact Tracing ◽  
Distributed Model ◽  
Serial Interval ◽  
Public Data ◽  
Interval Censored ◽  
Large Outbreak ◽  
Emerging Virus ◽  
Novel Coronavirus ◽  
Censored Likelihood

AbstractBackgroundsThe 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.

scholarly journals 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

2020 ◽  
Author(s):  
Shi Zhao ◽  
Daozhou Gao ◽  
Zian Zhuang ◽  
Marc KC Chong ◽  
Yongli Cai ◽  
...  
Keyword(s):  
Hong Kong ◽  
Small Sample Size ◽  
Information Criterion ◽  
Small Sample ◽  
Contact Tracing ◽  
Distributed Model ◽  
First Case ◽  
Public Data ◽  
Novel Coronavirus ◽  
Censored Likelihood

Abstract 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.

scholarly journals 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

2020 ◽  
Author(s):  
Shi Zhao ◽  
Daozhou Gao ◽  
Zian Zhuang ◽  
Marc KC Chong ◽  
Yongli Cai ◽  
...  
Keyword(s):  
Hong Kong ◽  
Small Sample Size ◽  
Information Criterion ◽  
Small Sample ◽  
Contact Tracing ◽  
Distributed Model ◽  
First Case ◽  
Public Data ◽  
Novel Coronavirus ◽  
Censored Likelihood

Abstract 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.

scholarly journals 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

2020 ◽  
Author(s):  
Shi Zhao ◽  
Daozhou Gao ◽  
Zian Zhuang ◽  
Marc KC Chong ◽  
Yongli Cai ◽  
...  
Keyword(s):  
Hong Kong ◽  
Lognormal Distribution ◽  
Small Sample Size ◽  
Small Sample ◽  
Contact Tracing ◽  
Distribution Model ◽  
First Case ◽  
Public Data ◽  
Novel Coronavirus ◽  
Censored Likelihood

Abstract 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.

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

2020 ◽  
Author(s):  
Menghui Li ◽  
Kai Liu ◽  
Yukun Song ◽  
Ming Wang ◽  
Jinshan Wu
Keyword(s):  
Reproduction Number ◽  
Contact Tracing ◽  
Time Varying ◽  
Generation Interval ◽  
2Nd Generation ◽  
Serial Interval ◽  
Interval Censored ◽  
Imported Cases ◽  
Emerging Virus ◽  
Censored Likelihood

AbstractBackgroundsThe 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.

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

2020 ◽  
Vol 25 (16) ◽  
Author(s):  
Kin On Kwok ◽  
Valerie Wing Yu Wong ◽  
Wan In Wei ◽  
Samuel Yeung Shan Wong ◽  
Julian Wei-Tze Tang
Keyword(s):  
Hong Kong ◽  
Attack Rate ◽  
Local Source ◽  
Paired Data ◽  
Serial Interval ◽  
Right Truncation ◽  
Epidemiological Characteristics ◽  
Censored Likelihood ◽  
Best Fit ◽  
Close Contacts

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.

scholarly journals Shrinkage in serial intervals across cluster transmission generations of COVID-19

2020 ◽  
Author(s):  
Shi Zhao ◽  
Yu Zhao ◽  
Biao Tang ◽  
Daozhou Gao ◽  
Zhao Guo ◽  
...  
Keyword(s):  
Hong Kong ◽  
Probability Distribution ◽  
Global Health ◽  
Contact Tracing ◽  
Epidemiological Factors ◽  
Transmission Process ◽  
Serial Interval ◽  
Speed Up ◽  
Population Scale ◽  
The Individual

The COVID-19 pandemic poses a serious threat to global health, and one of the key epidemiological factors that shape the transmission of COVID-19 is its serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, slight discrepancies in SI across different transmission generations are observed from the aggregated statistics in recent studies. To explore the change in SI across transmission generations, we develop a likelihood-based statistical inference framework to examine and quantify the change in SI. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that the individual SI of COVID-19 is likely to shrink with a rate of 0.72 per generation and 95%CI: (0.54, 0.96) as the transmission generation increases. We speculate that the shrinkage in SI is an outcome of competition among multiple candidate infectors within a cluster of cases. The shrinkage in SI may speed up the transmission process, and thus the nonpharmaceutical interventive strategies are crucially important to mitigate the COVID-19 epidemic.

Application of Inherent Risk of Contagion (IRC) framework & modelling to aid local Covid-19 response & mitigation: Viewpoint (Preprint)

2020 ◽  
Author(s):  
Helmi Zakariah ◽  
Fadzilah bt Kamaluddin ◽  
Choo-Yee Ting ◽  
Hui-Jia Yee ◽  
Shereen Allaham ◽  
...  
Keyword(s):  
Public Health ◽  
Public Health Problem ◽  
Analytical Framework ◽  
Vulnerable Population ◽  
Contact Tracing ◽  
Global Public Health ◽  
Inherent Risk ◽  
Transmission Area ◽  
Current Outbreak ◽  
Novel Coronavirus

UNSTRUCTURED The current outbreak of coronavirus disease 2019 (COVID-19) caused by the novel coronavirus named SARS-CoV-2 has been a major global public health problem threatening many countries and territories. Mathematical modelling is one of the non-pharmaceutical public health measures that plays a crucial role for mitigating the risk and impact of the pandemic. A group of researchers and epidemiologists have developed a machine learning-powered inherent risk of contagion (IRC) analytical framework to georeference the COVID-19 with an operational platform to plan response & execute mitigation activities. This framework dataset provides a coherent picture to track and predict the COVID-19 epidemic post lockdown by piecing together preliminary data on publicly available health statistic metrics alongside the area of reported cases, drivers, vulnerable population, and number of premises that are suspected to become a transmission area between drivers and vulnerable population. The main aim of this new analytical framework is to measure the IRC and provide georeferenced data to protect the health system, aid contact tracing, and prioritise the vulnerable.

scholarly journals Subcritical Transmission in the Early Stage of COVID-19 in Korea

2021 ◽  
Vol 18 (3) ◽  
pp. 1265
Author(s):  
Yong Sul Won ◽  
Jong-Hoon Kim ◽  
Chi Young Ahn ◽  
Hyojung Lee
Keyword(s):  
Incubation Period ◽  
Early Stage ◽  
Probability Distributions ◽  
Likelihood Estimation ◽  
Contact Tracing ◽  
Statistical Characterization ◽  
Serial Interval ◽  
Imported Cases ◽  
The Mean ◽  
Reporting Delays

While the coronavirus disease 2019 (COVID-19) outbreak has been ongoing in Korea since January 2020, there were limited transmissions during the early stages of the outbreak. In the present study, we aimed to provide a statistical characterization of COVID-19 transmissions that led to this small outbreak. We collated the individual data of the first 28 confirmed cases reported from 20 January to 10 February 2020. We estimated key epidemiological parameters such as reporting delay (i.e., time from symptom onset to confirmation), incubation period, and serial interval by fitting probability distributions to the data based on the maximum likelihood estimation. We also estimated the basic reproduction number (R0) using the renewal equation, which allows for the transmissibility to differ between imported and locally transmitted cases. There were 16 imported and 12 locally transmitted cases, and secondary transmissions per case were higher for the imported cases than the locally transmitted cases (nine vs. three cases). The mean reporting delays were estimated to be 6.76 days (95% CI: 4.53, 9.28) and 2.57 days (95% CI: 1.57, 4.23) for imported and locally transmitted cases, respectively. The mean incubation period was estimated to be 5.53 days (95% CI: 3.98, 8.09) and was shorter than the mean serial interval of 6.45 days (95% CI: 4.32, 9.65). The R0 was estimated to be 0.40 (95% CI: 0.16, 0.99), accounting for the local and imported cases. The fewer secondary cases and shorter reporting delays for the locally transmitted cases suggest that contact tracing of imported cases was effective at reducing further transmissions, which helped to keep R0 below one and the overall transmissions small.

scholarly journals COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

2021 ◽  
pp. 0272989X2110030
Author(s):  
Serin Lee ◽  
Zelda B. Zabinsky ◽  
Judith N. Wasserheit ◽  
Stephen M. Kofsky ◽  
Shan Liu
Keyword(s):  
Large City ◽  
Face Mask ◽  
School Closure ◽  
Contact Tracing ◽  
The Novel ◽  
Agent Based ◽  
Social Distancing ◽  
Variable Compliance ◽  
Novel Coronavirus ◽  
Nonpharmaceutical Interventions

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

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