scholarly journals Serial Interval and Reproductive Number of COVID-19 Among 116 Infector-infectee Pairs — Jingzhou City, Hubei Province, China, 2020

2020 ◽  
Vol 2 (27) ◽  
pp. 491-495 ◽  
Author(s):  
Tian Liu ◽  
◽  
Li Qi ◽  
Menglei Yao ◽  
Keqing Tian ◽  
...  
Keyword(s):  
Hubei Province ◽  
Reproductive Number ◽  
Serial Interval

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

2020 ◽  
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.

scholarly journals Quantifying TB transmission: a systematic review of reproduction number and serial interval estimates for tuberculosis

2018 ◽  
Vol 146 (12) ◽  
pp. 1478-1494 ◽  
Author(s):  
Y. Ma ◽  
C. R. Horsburgh ◽  
L. F. White ◽  
H. E. Jenkins
Keyword(s):  
Systematic Review ◽  
Parameter Estimation ◽  
The Netherlands ◽  
Cause Of Death ◽  
Reproduction Number ◽  
Reproductive Number ◽  
Interval Estimates ◽  
Transmission Parameters ◽  
Serial Interval ◽  
Number Of Publications

AbstractTuberculosis (TB) is the leading global infectious cause of death. Understanding TB transmission is critical to creating policies and monitoring the disease with the end goal of TB elimination. To our knowledge, there has been no systematic review of key transmission parameters for TB. We carried out a systematic review of the published literature to identify studies estimating either of the two key TB transmission parameters: the serial interval (SI) and the reproductive number. We identified five publications that estimated the SI and 56 publications that estimated the reproductive number. The SI estimates from four studies were: 0.57, 1.42, 1.44 and 1.65 years; the fifth paper presented age-specific estimates ranging from 20 to 30 years (for infants <1 year old) to <5 years (for adults). The reproductive number estimates ranged from 0.24 in the Netherlands (during 1933–2007) to 4.3 in China in 2012. We found a limited number of publications and many high TB burden settings were not represented. Certain features of TB dynamics, such as slow transmission, complicated parameter estimation, require novel methods. Additional efforts to estimate these parameters for TB are needed so that we can monitor and evaluate interventions designed to achieve TB elimination.

scholarly journals Estimation of incubation period and serial interval of COVID-19: analysis of 178 cases and 131 transmission chains in Hubei province, China

2020 ◽  
Vol 148 ◽  
Author(s):  
Lin Yang ◽  
Jingyi Dai ◽  
Jun Zhao ◽  
Yunfu Wang ◽  
Pingji Deng ◽  
...  
Keyword(s):  
Incubation Period ◽  
Failure Time ◽  
Age Groups ◽  
Hubei Province ◽  
Interval Censoring ◽  
Contact Tracing ◽  
Considerable Proportion ◽  
Outbreak Period ◽  
Serial Interval ◽  
Incubation Periods

Abstract A novel coronavirus disease, designated as COVID-19, has become a pandemic worldwide. This study aims to estimate the incubation period and serial interval of COVID-19. We collected contact tracing data in a municipality in Hubei province during a full outbreak period. The date of infection and infector–infectee pairs were inferred from the history of travel in Wuhan or exposed to confirmed cases. The incubation periods and serial intervals were estimated using parametric accelerated failure time models, accounting for interval censoring of the exposures. Our estimated median incubation period of COVID-19 is 5.4 days (bootstrapped 95% confidence interval (CI) 4.8–6.0), and the 2.5th and 97.5th percentiles are 1 and 15 days, respectively; while the estimated serial interval of COVID-19 falls within the range of −4 to 13 days with 95% confidence and has a median of 4.6 days (95% CI 3.7–5.5). Ninety-five per cent of symptomatic cases showed symptoms by 13.7 days (95% CI 12.5–14.9). The incubation periods and serial intervals were not significantly different between male and female, and among age groups. Our results suggest a considerable proportion of secondary transmission occurred prior to symptom onset. And the current practice of 14-day quarantine period in many regions is reasonable.

scholarly journals Transmissibility of swine flu at Fort Dix, 1976

2007 ◽  
Vol 4 (15) ◽  
pp. 755-762 ◽  
Author(s):  
Justin Lessler ◽  
Derek A.T Cummings ◽  
Steven Fishman ◽  
Amit Vora ◽  
Donald S Burke
Keyword(s):  
United States ◽  
New Jersey ◽  
Pandemic Influenza ◽  
The United States ◽  
Basic Reproductive Number ◽  
World Health ◽  
United States Government ◽  
Swine Flu ◽  
Reproductive Number ◽  
Serial Interval

Abstarct The 1976 outbreak of A/New Jersey/76 influenza in Fort Dix is a rare example of an influenza virus with documented human to human transmission that failed to spread widely. Despite extensive epidemiological investigation, no attempt has been made to quantify the transmissibility of this virus. The World Health Organization and the United States Government view containment of emerging influenza strains as central to combating pandemic influenza. Computational models predict that it may be possible to contain an emergent pandemic influenza if virus transmissibility is low. The A/New Jersey/76 outbreak at the United States Army Training Center at Fort Dix, New Jersey in January 1976 caused 13 hospitalizations, 1 death and an estimated 230 cases. To characterize viral transmission in this epidemic, we estimated the basic reproductive number and serial interval using deterministic epidemic models and stochastic simulations. We estimated the basic reproductive number for this outbreak to be 1.2 (supported interval 1.1–1.4), the serial interval to be 1.9 days (supported interval 1.6–3.8 days), and that the virus had at least six serial human to human transmissions. This places the transmissibility of A/New Jersey/76 virus at the lower end of circulating flu strains, well below the threshold for control.

scholarly journals Prediction of daily and cumulative cases for COVID-19 infection based on reproductive number (R0) in Karnataka: a data-driven analytics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kuralayanapalya Puttahonnappa Suresh ◽  
Sharanagouda S. Patil ◽  
Bharath Prasad Cholanayakanahalli Thyagaraju ◽  
Srikantha Gowda Ramkrishnappa ◽  
Divakar Hemadri ◽  
...  
Keyword(s):  
Data Driven ◽  
Reproductive Number ◽  
Bootstrap Resampling ◽  
Medical Facility ◽  
Expected Number ◽  
R Program ◽  
Serial Interval ◽  
Karnataka State ◽  
Resampling Method ◽  
Rapid Pace

AbstractTo estimate the reproductive number (R0) of the coronavirus in the present scenario and to predict the incidence of daily and probable cumulative cases, by 20 August, 2020 for Karnataka state in India. The model used serial interval with a gamma distribution and applied ‘early R’ to estimate the R0 and ‘projections’ package in R program. This was performed to mimic the probable cumulative epidemic trajectories and predict future daily incidence by fitting the data to existing daily incidence and the estimated R0 by a model based on the assumption that daily incidence follows Poisson distribution. The maximum-likelihood (ML) value of R0 was 2.242 for COVID-19 outbreak, as on June 2020. The median with 95% CI of R0 values was 2.242 (1.50–3.00) estimated by bootstrap resampling method. The expected number of new cases for the next 60 days would progressively increase, and the estimated cumulative cases would reach 27,238 (26,008–28,467) at the end of 60th day in the future. But, if R0 value was doubled the estimated total number of cumulative cases would increase up to 432,411 (400,929–463,893) and if, R0 increase by 50%, the cases would increase up to 86,386 (80,910–91,861). The probable outbreak size and future daily cumulative incidence are largely dependent on the change in R0 values. Hence, it is vital to expedite the hospital provisions, medical facility enhancement work, and number of random tests for COVID-19 at a very rapid pace to prepare the state for exponential growth in next 2 months.

scholarly journals Study on SARS-CoV-2 transmission and the effects of control measures in China

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242649
Author(s):  
Bo Zhang ◽  
Hongwei Zhou ◽  
Fang Zhou
Keyword(s):  
Confidence Interval ◽  
Turning Point ◽  
Estimation Method ◽  
Hubei Province ◽  
Control Measures ◽  
Reproductive Number ◽  
Wuhan City ◽  
Epidemic Area ◽  
Stage 1 ◽  
The City

Objective To reconstruct the transmission trajectory of SARS-CoV-2 and analyze the effects of control measures in China. Methods Python 3.7.1 was used to write a SEIR class to model the epidemic procedure and proportional estimation method to estimate the initial true infected number. The epidemic area in China was divided into three parts, Wuhan city, Hubei province (except Wuhan) and China (except Hubei) based on the different transmission pattern. A testing capacity limitation factor for medical resources was imposed to model the number of infected but not quarantined individuals. Baidu migration data were used to assess the number of infected individuals who migrated from Wuhan to other areas. Results Basic reproduction number, R0, was 3.6 before the city was lockdown on Jan 23, 2020. The actual infected number the model predicted was 4508 in Wuhan before Jan 23, 2020. By January 22 2020, it was estimated that 1764 infected cases migrated from Wuhan to other cities in Hubei province. Effective reproductive number, R, gradually decreased from 3.6 (Wuhan), 3.4 (Hubei except Wuhan,) and 3.3 (China except Hubei) in stage 1 (from Dec 08, 2019 to Jan 22, 2020) to 0.67 (Wuhan), 0.59 (Hubei except Wuhan) and 0.63 (China except Hubei) respectively. Especially after January 23, 2020 when Wuhan City was closed, the infected number showed a turning point in Wuhan. By early April, there would be 42073 (95% confidence interval, 41673 to 42475), 21342 (95% confidence interval, 21057 to 21629) and 13384 (95% confidence interval, 13158 to 13612) infected cases in Wuhan, Hubei (except Wuhan) and China (except Hubei), respectively. Conclusion A series of control measures in China have effectively prevented the spread of COVID-19, and the epidemic should be under control in early April with very few new cases occasionally reported.

scholarly journals Estimating effective reproduction number using generation time versus serial interval, with application to COVID-19 in the Greater Toronto Area, Canada

2020 ◽  
Author(s):  
Jesse Knight ◽  
Sharmistha Mishra
Keyword(s):  
Time Series ◽  
Generation Time ◽  
Time Distribution ◽  
Reproduction Number ◽  
Reproductive Number ◽  
Transmission Potential ◽  
Greater Toronto Area ◽  
Critical Measure ◽  
Serial Interval ◽  
Interval Distribution

AbstractBackgroundThe effective reproductive number Re(t) is a critical measure of epidemic potential. Re(t) can be calculated in near real time using an incidence time series and the generation time distribution—the time between infection events in an infector-infectee pair. In calculating Re(t), the generation time distribution is often approximated by the serial interval distribution—the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts.MethodsWe developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of Re(t) for covid-19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution.ResultsWe estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of Re(t) due to larger mean, while negative-permitting serial interval distribution caused underestimation of Re(t) due to larger variance.ImplicationsApproximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating Re(t) may result in over or underestimation of transmission potential, respectively.

scholarly journals Epidemiological Profile and Transmission Dynamics of COVID-19 in the Philippines

2020 ◽  
Author(s):  
Nel Jason Ladiao Haw ◽  
Jhanna Uy ◽  
Karla Therese L. Sy
Keyword(s):  
Doubling Time ◽  
The Philippines ◽  
Reproductive Number ◽  
National Capital Region ◽  
Time To Event ◽  
Duration Of Illness ◽  
Care Workers ◽  
Serial Interval ◽  
National Capital ◽  
Epidemiological Profile

The Philippines confirmed local transmission of COVID-19 on 7 March 2020. We described the characteristics and epidemiological time-to-event distributions for laboratory-confirmed cases in the Philippines. The median age of 8,212 cases was 46 years (IQR: 32-61), with 46.2% being female and 68.8% living in the National Capital Region. Health care workers represented 24.7% of all detected infections. Mean length of hospitalization for those who were discharged or died were 16.00 days (95% CI: 15.48, 16.54) and 7.27 days (95% CI: 6.59, 8.24). Mean duration of illness was 26.66 days (95% CI: 26.06, 27.28) and 12.61 days (95% CI: 11.88, 13.37) for those who recovered or died. Mean serial interval was 6.90 days (95% CI: 5.81, 8.41). Epidemic doubling time pre-quarantine (11 February and 19 March) was 4.86 days (95% CI: 4.67, 5.07) and the reproductive number was 2.41 (95% CI: 2.33, 2.48). During quarantine (March 20 to April 9), doubling time was 12.97 days (95% CI: 12.57, 13.39) and the reproductive number was 0.89 (95% CI: 0.78, 1.02).

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