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

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.

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Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions

Science ◽

10.1126/science.abc9004 ◽

2020 ◽

Vol 369 (6507) ◽

pp. 1106-1109 ◽

Cited By ~ 36

Author(s):

Sheikh Taslim Ali ◽

Lin Wang ◽

Eric H. Y. Lau ◽

Xiao-Ke Xu ◽

Zhanwei Du ◽

...

Keyword(s):

Mainland China ◽

Time Estimation ◽

Control Measures ◽

Illness Onset ◽

Reproduction Numbers ◽

Serial Interval ◽

Novel Coronavirus ◽

Nonpharmaceutical Interventions ◽

The Impact ◽

Over Time

Studies of novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have reported varying estimates of epidemiological parameters, including serial interval distributions—i.e., the time between illness onset in successive cases in a transmission chain—and reproduction numbers. By compiling a line-list database of transmission pairs in mainland China, we show that mean serial intervals of COVID-19 shortened substantially from 7.8 to 2.6 days within a month (9 January to 13 February 2020). This change was driven by enhanced nonpharmaceutical interventions, particularly case isolation. We also show that using real-time estimation of serial intervals allowing for variation over time provides more accurate estimates of reproduction numbers than using conventionally fixed serial interval distributions. These findings could improve our ability to assess transmission dynamics, forecast future incidence, and estimate the impact of control measures.

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Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study

10.1101/2020.02.21.20026328 ◽

2020 ◽

Cited By ~ 19

Author(s):

Juanjuan Zhang ◽

Maria Litvinova ◽

Wei Wang ◽

Yan Wang ◽

Xiaowei Deng ◽

...

Keyword(s):

Age Groups ◽

Mainland China ◽

The Elderly ◽

Hubei Province ◽

Epidemic Threshold ◽

Epidemic Dynamics ◽

Serial Interval ◽

The Mean ◽

Novel Coronavirus ◽

Local Transmission

AbstractBackgroundThe COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.MethodsWe collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.ResultsThe median age of the cases was 44 years, with an increasing of cases in younger age groups and the elderly as the epidemic progressed. The delay from symptom onset to hospital admission decreased from 4.4 days (95%CI: 0.0-14.0) until January 27 to 2.6 days (0.0-9.0) from January 28 to February 17. The mean incubation period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei was highly variable, but consistently included a mix of case importations and local transmission. We estimate that the epidemic was self-sustained for less than three weeks with Rt reaching peaks between 1.40 (1.04-1.85) in Shenzhen City of Guangdong Province and 2.17 (1.69-2.76) in Shandong Province. In all the analyzed locations (n=10) Rt was estimated to be below the epidemic threshold since the end of January.ConclusionOur findings suggest that the strict containment measures and movement restrictions in place may contribute to the interruption of local COVID-19 transmission outside Hubei Province. The shorter serial interval estimated here implies that transmissibility is not as high as initial estimates suggested.

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Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17093054 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3054 ◽

Cited By ~ 14

Author(s):

Oyelola A. Adegboye ◽

Adeshina I. Adekunle ◽

Ezra Gayawan

Keyword(s):

Doubling Time ◽

Reproduction Number ◽

Infected Individual ◽

Credible Interval ◽

World Health ◽

Time Interval ◽

Serial Interval ◽

Imported Cases ◽

Novel Coronavirus ◽

Health Organization

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

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Serial interval of novel coronavirus (COVID-19) infections

10.1101/2020.02.03.20019497 ◽

2020 ◽

Cited By ~ 66

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.

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Country distancing reveals the effectiveness of travel restrictions during COVID-19

10.1101/2020.07.24.20160994 ◽

2020 ◽

Author(s):

Lu Zhong ◽

Mamadou Diagne ◽

Weiping Wang ◽

Jianxi Gao

Keyword(s):

Mainland China ◽

Credible Interval ◽

Economic Loss ◽

Geographic Area ◽

Strategic Action ◽

Infected Case ◽

Travel Restrictions ◽

In Series ◽

Backbone Tree ◽

Novel Coronavirus

Travel restrictions are the current central strategy to globally stop the transmission of the novel coronavirus disease (COVID-19). Despite remarkably successful approaches in predicting the spatiotemporal patterns of the ongoing pandemic, we lack an intrinsic understanding of the travel restriction's effectiveness. We fill this gap by developing a surprisingly simple measure, country distancing, that is analogical to the effective resistance in series and parallel circuits and captures the propagation backbone tree from the outbreak locations globally. This approach enables us to map the effectiveness of travel restrictions to arrival time delay (ATD) or infected case reduction (ICR) systematically. Our method estimates that 50.8\% of travel restrictions as of Apr-4 are ineffective, resulting in zero ATD or ICR worldwide. Instead, by imposing Hubei's lockdown on Jan-23 and national lockdown on Feb-8, mainland China alone leads to 11.66 [95\% credible interval (CI), 9.71 to 13.92] days of ATD per geographic area and 1,012,233 (95\% CI, 208,210 -4,959,094) ICR in total as of Apr-4. Our result unveils the trade-off between the country distancing increase and economic loss, offering practical guidance for strategic action about when and where to implement travel restrictions, tailed to the real-time national context.

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Evolution of effective serial interval of SARS-CoV-2 by non-pharmaceutical interventions

10.21203/rs.3.rs-32486/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sheikh Taslim Ali ◽

Lin Wang ◽

Eric H. Y. Lau ◽

Xiao-Ke Xu ◽

Zhanwei Du ◽

...

Keyword(s):

Mainland China ◽

Time Estimation ◽

Control Measures ◽

Reproduction Numbers ◽

Serial Interval ◽

Real Time Estimation ◽

Definition Of ◽

Novel Coronavirus ◽

The Impact ◽

Pharmaceutical Interventions

Abstract Studies of novel coronavirus disease (COVID-19) have reported varying estimates of epidemiological parameters such as serial intervals and reproduction numbers. By compiling a unique line-list database of transmission pairs in mainland China, we demonstrated that serial intervals of COVID-19 have shortened substantially from a mean of 7.8 days to 2.6 days within a month. This change is driven by enhanced non-pharmaceutical interventions, in particular case isolation. We also demonstrated that using real-time estimation of serial intervals allowing for variation over time would provide more accurate estimates of reproduction numbers, than by using conventional definition of fixed serial interval distributions. These findings are essential to improve the assessment of transmission dynamics, forecasting future incidence, and estimating the impact of control measures.

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

Eurosurveillance ◽

10.2807/1560-7917.es.2020.25.5.2000062 ◽

2020 ◽

Vol 25 (5) ◽

Cited By ~ 498

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.

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COVID-19 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities

10.1101/2020.04.23.20075796 ◽

2020 ◽

Cited By ~ 4

Author(s):

Zhanwei Du ◽

Xiaoke Xu ◽

Ye Wu ◽

Lin Wang ◽

Benjamin J. Cowling ◽

...

Keyword(s):

Mainland China ◽

Transmission Characteristics ◽

Interval Estimates ◽

Chinese Cities ◽

Time Period ◽

Complete Dataset ◽

Serial Interval ◽

Novel Coronavirus

AbstractAs a novel coronavirus (COVID-19) continues to spread widely and claim lives worldwide, its transmission characteristics remain uncertain. Here, we present and analyze the serial intervals–the time period between the onset of symptoms in an index (infector) case and the onset of symptoms in a secondary (infectee) case–of 339 confirmed cases of COVID-19 identified from 264 cities in mainland China prior to February 19, 2020. Here, we provide the complete dataset in both English and Chinese to support further COVID-19 research and modeling efforts.

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Spatially Explicit Modeling of 2019-nCoV Epidemic Trend Based on Mobile Phone Data in Mainland China

10.1101/2020.02.09.20021360 ◽

2020 ◽

Cited By ~ 9

Author(s):

Xiaolin Zhu ◽

Aiyin Zhang ◽

Shuai Xu ◽

Pengfei Jia ◽

Xiaoyue Tan ◽

...

Keyword(s):

Mobile Phone ◽

Current Trend ◽

Mainland China ◽

Virus Transmission ◽

Mobile Phone Data ◽

Spatially Explicit ◽

Travel History ◽

Level Model ◽

The Government ◽

Novel Coronavirus

AbstractAs of February 11, 2020, all prefecture-level cities in mainland China have reported confirmed cases of 2019 novel coronavirus (2019-nCoV), but the city-level epidemical dynamics is unknown. The aim of this study is to model the current dynamics of 2019-nCoV at city level and predict the trend in the next 30 days under three possible scenarios in mainland China. We developed a spatially explicit epidemic model to consider the unique characteristics of the virus transmission in individual cities. Our model considered that the rate of virus transmission among local residents is different from those with Wuhan travel history due to the self-isolation policy. We introduced a decay rate to quantify the effort of each city to gradually control the disease spreading. We used mobile phone data to obtain the number of individuals in each city who have travel history to Wuhan. This city-level model was trained using confirmed cases up to February 10, 2020 and validated by new confirmed cases on February 11, 2020. We used the trained model to predict the future dynamics up to March 12, 2020 under different scenarios: the current trend maintained, control efforts expanded, and person-to-person contact increased due to work resuming. We estimated that the total infections in mainland China would be 72172, 54348, and 149774 by March 12, 2020 under each scenario respectively. Under the current trend, all cities will show the peak point of daily new infections by February 21. This date can be advanced to February 14 with control efforts expanded or postponed to February 26 under pressure of work resuming. Except Wuhan that cannot eliminate the disease by March 12, our model predicts that 95.4%, 100%, and 75.7% cities will have no new infections by the end of February under three scenarios. The spatial pattern of our prediction could help the government allocate resources to cities that have a more serious epidemic in the next 30 days.

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The transmissibility of novel Coronavirus in the early stages of the 2019-20 outbreak in Wuhan: Exploring initial point-source exposure sizes and durations using scenario analysis

Wellcome Open Research ◽

10.12688/wellcomeopenres.15718.1 ◽

2020 ◽

Vol 5 ◽

pp. 17 ◽

Cited By ~ 40

Author(s):

Sam Abbott ◽

Joel Hellewell ◽

James Munday ◽

Sebastian Funk ◽

Keyword(s):

Point Source ◽

Process Model ◽

Initial Point ◽

Credible Interval ◽

Wholesale Market ◽

Initial Exposure ◽

Transmission Event ◽

Serial Interval ◽

Novel Coronavirus ◽

Initial Transmission

Background: The current novel coronavirus outbreak appears to have originated from a point-source exposure event at Huanan seafood wholesale market in Wuhan, China. There is still uncertainty around the scale and duration of this exposure event. This has implications for the estimated transmissibility of the coronavirus and as such, these potential scenarios should be explored. Methods: We used a stochastic branching process model, parameterised with available data where possible and otherwise informed by the 2002-2003 Severe Acute Respiratory Syndrome (SARS) outbreak, to simulate the Wuhan outbreak. We evaluated scenarios for the following parameters: the size, and duration of the initial transmission event, the serial interval, and the reproduction number (R0). We restricted model simulations based on the number of observed cases on the 25th of January, accepting samples that were within a 5% interval on either side of this estimate. Results: Using a pre-intervention SARS-like serial interval suggested a larger initial transmission event and a higher R0 estimate. Using a SARs-like serial interval we found that the most likely scenario produced an R0 estimate between 2-2.7 (90% credible interval (CrI)). A pre-intervention SARS-like serial interval resulted in an R0 estimate between 2-3 (90% CrI). There were other plausible scenarios with smaller events sizes and longer duration that had comparable R0 estimates. There were very few simulations that were able to reproduce the observed data when R0 was less than 1. Conclusions: Our results indicate that an R0 of less than 1 was highly unlikely unless the size of the initial exposure event was much greater than currently reported. We found that R0 estimates were comparable across scenarios with decreasing event size and increasing duration. Scenarios with a pre-intervention SARS-like serial interval resulted in a higher R0 and were equally plausible to scenarios with SARs-like serial intervals.

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