scholarly journals Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20–28 January 2020

2020 ◽  
Vol 25 (5) ◽  
Author(s):  
Jantien A Backer ◽  
Don Klinkenberg ◽  
Jacco Wallinga
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Credible Interval ◽  
Viral Pneumonia ◽  
Case Definitions ◽  
Travel History ◽  
The Mean ◽  
Novel Coronavirus

A novel coronavirus (2019-nCoV) is causing an outbreak of viral pneumonia that started in Wuhan, China. Using the travel history and symptom onset of 88 confirmed cases that were detected outside Wuhan in the early outbreak phase, we estimate the mean incubation period to be 6.4 days (95% credible interval: 5.6–7.7), ranging from 2.1 to 11.1 days (2.5th to 97.5th percentile). These values should help inform 2019-nCoV case definitions and appropriate quarantine durations.

scholarly journals The incubation period of 2019-nCoV infections among travellers from Wuhan, China

2020 ◽  
Author(s):  
Jantien A. Backer ◽  
Don Klinkenberg ◽  
Jacco Wallinga
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Viral Pneumonia ◽  
Case Definitions ◽  
Travel History ◽  
The Mean ◽  
Novel Coronavirus ◽  
Epidemiological Characteristics

AbstractCurrently, a novel coronavirus 2019-nCoV causes an outbreak of viral pneumonia in Wuhan, China. Little is known about its epidemiological characteristics. Using the travel history and symptom onset of 88 confirmed cases that were detected outside Wuhan, we estimate the mean incubation period to be 6.4 (5.6 – 7.7, 95% CI) days, ranging from 2.1 to 11.1 days (2.5th to 97.5th percentile). These values help to inform case definitions for 2019-nCoV and appropriate durations for quarantine.

scholarly journals Novel Coronavirus Outbreak in Wuhan, China, 2020: Intense Surveillance Is Vital for Preventing Sustained Transmission in New Locations

2020 ◽  
Vol 9 (2) ◽  
pp. 498 ◽  
Author(s):  
Robin N. Thompson
Keyword(s):  
Symptom Onset ◽  
Credible Interval ◽  
Imported Case ◽  
Global Pandemic ◽  
New Locations ◽  
Current Outbreak ◽  
The Mean ◽  
The Times ◽  
Novel Coronavirus ◽  
Mean Time

The outbreak of pneumonia originating in Wuhan, China, has generated 24,500 confirmed cases, including 492 deaths, as of 5 February 2020. The virus (2019-nCoV) has spread elsewhere in China and to 24 countries, including South Korea, Thailand, Japan and USA. Fortunately, there has only been limited human-to-human transmission outside of China. Here, we assess the risk of sustained transmission whenever the coronavirus arrives in other countries. Data describing the times from symptom onset to hospitalisation for 47 patients infected early in the current outbreak are used to generate an estimate for the probability that an imported case is followed by sustained human-to-human transmission. Under the assumptions that the imported case is representative of the patients in China, and that the 2019-nCoV is similarly transmissible to the SARS coronavirus, the probability that an imported case is followed by sustained human-to-human transmission is 0.41 (credible interval [0.27, 0.55]). However, if the mean time from symptom onset to hospitalisation can be halved by intense surveillance, then the probability that an imported case leads to sustained transmission is only 0.012 (credible interval [0, 0.099]). This emphasises the importance of current surveillance efforts in countries around the world, to ensure that the ongoing outbreak will not become a global pandemic.

scholarly journals Transmission onset distribution of COVID-19

2020 ◽  
Author(s):  
June Young Chun ◽  
Gyuseung Baek ◽  
Yongdai Kim
Keyword(s):  
Standard Deviation ◽  
Incubation Period ◽  
Bayesian Methods ◽  
Symptom Onset ◽  
Onset Time ◽  
Preventive Measure ◽  
Credible Interval ◽  
Serial Interval ◽  
Interval Distribution ◽  
Key Parameter

AbstractObjectivesThe distribution of the transmission onset of COVID-19 relative to the symptom onset is a key parameter for infection control. It is often not easy to study the transmission onset time, as is difficult to know who infected whom exactly when.MethodsWe inferred transmission onset time from 72 infector-infectee pairs in South Korea, either with known or inferred contact dates by means of incubation period. Combining this data with known information of infector’s symptom onset, we could generate the transmission onset distribution of COVID-19, using Bayesian methods. Serial interval distribution could be automatically estimated from our data.ResultsWe estimated the median transmission onset to be 1.31 days (standard deviation, 2.64 days) after symptom onset with peak at 0.72 days before symptom onset. The pre-symptomatic transmission proportion was 37% (95% credible interval [CI], 16–52%). The median incubation period was estimated to be 2.87 days (95% CI, 2.33–3.50 days) and the median serial interval to be 3.56 days (95% CI, 2.72–4.44 days).ConclusionsConsidering the transmission onset distribution peaked with the symptom onset and the pre-symptomatic transmission proportion is substantial, the usual preventive measure might be too late to prevent SARS-CoV-2 transmission.

scholarly journals Serial interval of novel coronavirus (COVID-19) infections

2020 ◽  
Author(s):  
Hiroshi Nishiura ◽  
Natalie M. Linton ◽  
Andrei R. Akhmetzhanov
Keyword(s):  
Incubation Period ◽  
Credible Interval ◽  
Substantial Proportion ◽  
List Type ◽  
Illness Onset ◽  
Full Dataset ◽  
Serial Interval ◽  
Novel Coronavirus ◽  
Right Truncation ◽  
Published Research

AbstractObjectiveTo estimate the serial interval of novel coronavirus (COVID-19) from information on 28 infector-infectee pairs.MethodsWe collected dates of illness onset for primary cases (infectors) and secondary cases (infectees) from published research articles and case investigation reports. We subjectively ranked the credibility of the data and performed analyses on both the full dataset (n=28) and a subset of pairs with highest certainty in reporting (n=18). In addition, we adjusting for right truncation of the data as the epidemic is still in its growth phase.ResultsAccounting for right truncation and analyzing all pairs, we estimated the median serial interval at 4.0 days (95% credible interval [CrI]: 3.1, 4.9). Limiting our data to only the most certain pairs, the median serial interval was estimated at 4.6 days (95% CrI: 3.5, 5.9).ConclusionsThe serial interval of COVID-19 is shorter than its median incubation period. This suggests that a substantial proportion of secondary transmission may occur prior to illness onset. The COVID-19 serial interval is also shorter than the serial interval of severe acute respiratory syndrome (SARS), indicating that calculations made using the SARS serial interval may introduce bias.Highlights-The serial interval of novel coronavirus (COVID-19) infections was estimated from a total of 28 infector-infectee pairs.-The median serial interval is shorter than the median incubation period, suggesting a substantial proportion of pre-symptomatic transmission.-A short serial interval makes it difficult to trace contacts due to the rapid turnover of case generations.

scholarly journals Reduction in time delay of isolation in COVID-19 cases in South Korea

2020 ◽  
Author(s):  
Sukhyun Ryu ◽  
Cheolsun Jang ◽  
Baekjin Kim
Keyword(s):  
Public Health ◽  
Time Delay ◽  
South Korea ◽  
Symptom Onset ◽  
The Public ◽  
Mean Delay ◽  
Health Authorities ◽  
Public Health Authorities ◽  
The Mean ◽  
Novel Coronavirus

AbstractKorean public health authorities raised the public alert to its highest level on February 23, 2020 to mitigate the 2019 novel coronavirus disease epidemic. We have identified that the mean delay from symptom onset to isolation was reduced to one day after raising the alert. Vigilance can reduce this interval.

scholarly journals Reconstruction of Transmission Pairs for Novel Coronavirus Disease 2019 (COVID-19) in Mainland China: Estimation of Superspreading Events, Serial Interval, and Hazard of Infection

2020 ◽  
Author(s):  
Xiao-Ke Xu ◽  
Xiao Fan Liu ◽  
Ye Wu ◽  
Sheikh Taslim Ali ◽  
Zhanwei Du ◽  
...  
Keyword(s):  
Social Relationships ◽  
Mainland China ◽  
Credible Interval ◽  
Old People ◽  
Travel History ◽  
Serial Interval ◽  
Significant Barrier ◽  
Working Age ◽  
Novel Coronavirus ◽  
Nonpharmaceutical Interventions

Abstract Background Knowledge on the epidemiological features and transmission patterns of novel coronavirus disease (COVID-19) is accumulating. Detailed line-list data with household settings can advance the understanding of COVID-19 transmission dynamics. Methods A unique database with detailed demographic characteristics, travel history, social relationships, and epidemiological timelines for 1407 transmission pairs that formed 643 transmission clusters in mainland China was reconstructed from 9120 COVID-19 confirmed cases reported during 15 January–29 February 2020. Statistical model fittings were used to identify the superspreading events and estimate serial interval distributions. Age- and sex-stratified hazards of infection were estimated for household vs nonhousehold transmissions. Results There were 34 primary cases identified as superspreaders, with 5 superspreading events occurred within households. Mean and standard deviation of serial intervals were estimated as 5.0 (95% credible interval [CrI], 4.4–5.5) days and 5.2 (95% CrI, 4.9–5.7) days for household transmissions and 5.2 (95% CrI, 4.6–5.8) and 5.3 (95% CrI, 4.9–5.7) days for nonhousehold transmissions, respectively. The hazard of being infected outside of households is higher for people aged 18–64 years, whereas hazard of being infected within households is higher for young and old people. Conclusions Nonnegligible frequency of superspreading events, short serial intervals, and a higher risk of being infected outside of households for male people of working age indicate a significant barrier to the identification and management of COVID-19 cases, which requires enhanced nonpharmaceutical interventions to mitigate this pandemic.

scholarly journals Transmission characteristics of the COVID-19 outbreak in China: a study driven by data

2020 ◽  
Author(s):  
Meili Li ◽  
Pian Chen ◽  
Qianqian Yuan ◽  
Baojun Song ◽  
Junling Ma
Keyword(s):  
Public Health ◽  
Incubation Period ◽  
Symptom Onset ◽  
Generation Time ◽  
Reproduction Number ◽  
Contact Tracing ◽  
Transmission Characteristics ◽  
Public Health Threat ◽  
The Mean ◽  
The Basic Reproduction Number

The COVID-19 outbreak has been a serious public health threat worldwide. We use individually documented case descriptions of COVID-19 from China (excluding Hubei Province) to estimate the distributions of the generation time, incubation period, and periods from symptom onset to isolation and to diagnosis. The recommended 14-day quarantine period may lead to a 6.7% failure for quarantine. We recommend a 22-day quarantine period. The mean generation time is 3.3 days and the mean incubation period is 7.2 days. It took 3.7 days to isolate and 6.6 days to diagnose a patient after his/her symptom onset. Patients may become infectious on average 3.9 days before showing major symptoms. This makes contact tracing and quarantine ineffective. The basic reproduction number is estimated to be 1.54 with contact tracing, quarantine and isolation, mostly driven by super spreaders.

scholarly journals Analysis of the epidemic growth of the early 2019-nCoV outbreak using internationally confirmed cases

2020 ◽  
Author(s):  
Qingyuan Zhao ◽  
Yang Chen ◽  
Dylan S Small
Keyword(s):  
Symptom Onset ◽  
Onset Time ◽  
Credible Interval ◽  
Important Distinction ◽  
Travel History ◽  
Infection Time ◽  
Prior Specification ◽  
Exponential Growth Model ◽  
Travel Rate ◽  
Epidemiological Parameters

AbstractBackgroundOn January 23, 2020, a quarantine was imposed on travel in and out of Wuhan, where the 2019 novel coronavirus (2019-nCoV) outbreak originated from. Previous analyses estimated the basic epidemiological parameters using symptom onset dates of the confirmed cases in Wuhan and outside China.MethodsWe obtained information on the 46 coronavirus cases who traveled from Wuhan before January 23 and have been subsequently confirmed in Hong Kong, Japan, Korea, Macau, Singapore, and Taiwan as of February 5, 2020. Most cases have detailed travel history and disease progress. Compared to previous analyses, an important distinction is that we used this data to informatively simulate the infection time of each case using the symptom onset time, previously reported incubation interval, and travel history. We then fitted a simple exponential growth model with adjustment for the January 23 travel ban to the distribution of the simulated infection time. We used a Bayesian analysis with diffuse priors to quantify the uncertainty of the estimated epidemiological parameters. We performed sensitivity analysis to different choices of incubation interval and the hyperparameters in the prior specification.ResultsWe found that our model provides good fit to the distribution of the infection time. Assuming the travel rate to the selected countries and regions is constant over the study period, we found that the epidemic was doubling in size every 2.9 days (95% credible interval [CrI], 2 days—4.1 days). Using previously reported serial interval for 2019-nCoV, the estimated basic reproduction number is 5.7 (95% CrI, 3.4—9.2). The estimates did not change substantially if we assumed the travel rate doubled in the last 3 days before January 23, when we used previously reported incubation interval for severe acute respiratory syndrome (SARS), or when we changed the hyperparameters in our prior specification.ConclusionsOur estimated epidemiological parameters are higher than an earlier report using confirmed cases in Wuhan. This indicates the 2019-nCoV could have been spreading faster than previous estimates.

scholarly journals Estimating the distribution of the incubation period of 2019 novel coronavirus (COVID-19) infection between travelers to Hubei, China and non-travelers

2020 ◽  
Author(s):  
Char Leung
Keyword(s):  
Incubation Period ◽  
Clinical Data ◽  
Hubei Province ◽  
The Novel ◽  
Travel History ◽  
Health Authorities ◽  
Incubation Periods ◽  
The Media ◽  
Novel Coronavirus ◽  
The Individual

AbstractObjectivesAmid the continuing spread of the novel coronavirus (COVID-19), the incubation period of COVID-19 should be regularly re-assessed as more information is available upon the increase in reported cases. The present work estimated the distribution of incubation periods of patients infected in and outside Hubei province of China.MethodsClinical data were collected from the individual cases reported by the media as they were not fully available on the official pages of the Chinese health authorities. MLE was used to estimate the distributions of the incubation period.ResultsIt was found that the incubation period of patients with no travel history to Hubei was longer and more volatile.ConclusionIt is recommended that the duration of quarantine should be extended to at least 3 weeks.

scholarly journals Correlation between times to SARS-CoV-2 symptom onset and secondary transmission undermines epidemic control efforts

2021 ◽  
Author(s):  
Natalie M. Linton ◽  
Andrei R. Akhmetzhanov ◽  
Hiroshi Nishiura
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Focal Point ◽  
Transmission Dynamics ◽  
Generation Interval ◽  
Improve Model ◽  
The Mean ◽  
Two Parameters ◽  
Respiratory Coronavirus ◽  
Epidemiological Characteristics

AbstractSevere acute respiratory coronavirus 2 (SARS-CoV-2) infections have been associated with substantial presymptomatic transmission, which occurs when the generation interval—the time between infection of an individual with a pathogen and transmission of the pathogen to another individual—is shorter than the incubation period—the time between infection and symptom onset. We collected a dataset of 257 SARS-CoV-2 transmission pairs in Japan and jointly estimated the mean generation interval (3.7–5.1 days) and mean incubation period (4.4–5.7 days) as well as measured their dependence (Kendall’s tau of 0.4–0.6), taking into consideration demographic and epidemiological characteristics of the pairs. The positive correlation between the two parameters demonstrates that reliance on isolation of symptomatic COVID-19 cases as a focal point of control efforts is insufficient to address the challenges posed by SARS-CoV-2 transmission dynamics. Accounting for this dependence within SARS-CoV-2 epidemic models can also improve model estimates.

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