scholarly journals Variability of Zika Virus Incubation Period in Humans

2018 ◽  
Vol 5 (11) ◽  
Author(s):  
Toscane Fourié ◽  
Gilda Grard ◽  
Isabelle Leparc-Goffart ◽  
Sébastien Briolant ◽  
Albin Fontaine
Keyword(s):  
Confidence Interval ◽  
Incubation Period ◽  
Median Time ◽  
Censored Data ◽  
Zika Virus ◽  
Time To Event ◽  
Tropical Countries ◽  
Period Distribution ◽  
Interval Censored ◽  
Event Models

Abstract Zika virus (ZIKV) has recently emerged in numerous tropical countries worldwide. In this study, we estimated ZIKV incubation period distribution using time-to-event models adapted to interval-censored data based on declared date of travels from 123 symptomatic travelers returning from areas with active ZIKV transmission. The median time and 95th percentile of ZIKV incubation period was estimated to 6.8 days (95% confidence interval [CI], 5.8–7.7 days) and 15.4 days (95% CI, 12.7–19.7 days), respectively. Determining the incubation period for ZIKV is beneficial to improve protection guidelines.

scholarly journals Incubation Period of Coronavirus Disease 2019: New Implications for Intervention and Control

2021 ◽  
Author(s):  
Jing LI ◽  
Chengguqiu DAI ◽  
Zihan TIE ◽  
Jiazhao XU ◽  
Xiang XIONG ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Confidence Interval ◽  
Incubation Period ◽  
Censored Data ◽  
Estimation Method ◽  
Estimation Methods ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Sampling Procedures ◽  
And Control

Abstract Background: The COVID-19 pandemic raging around the world has caused serious disasters to mankind. The incubation period is a key parameter for epidemic control and also an important basis for epidemic prediction, but its distribution law remain unclear. Methods: The incubation period T was described by the accelerated failure time models, and the principle of interval-censored data processing and estimation methods were used. Statistical analysis were performed on R-4.0.2 software using “coarseDataTools 0.6-5” package to optimize the parameters to be estimated and calculate the confidence interval. The optimization method used when solving the maximum likelihood function is the simplex method. We used bootstrap re-sampling procedures with 1000 iterations to estimate the confidence interval. Results: Here we analyzed the epidemiological information of 787 confirmed non-Wuhan resident cases, and systematically studied the characteristics of the incubation period of COVID-19 based on the interval-censored data estimation method. Through the statistical analysis of the overall and 7 types of sub-group samples, it was concluded that the incubation period of COVID-19 approximately conformed to the Gamma distribution with a mean value of 7.8 (95%CI: 7.4-8.5) days and a median value of 7.0 (95%CI: 6.7-7.3) days. Conclusions: The incubation period was positively correlated with age and negatively correlated with disease severity. Female cases presented a slightly higher incubation period than that of males. The incubation period of cases with travel history to Hubei and multiple exposures was shorter. The proportion of infected persons who developed symptoms within 14 days was 91.6%. These results are of great significance to the prevention and control of the COVID-19 pandemic.

scholarly journals Estimation of incubation period distribution of COVID-19 using disease onset forward time: A novel cross-sectional and forward follow-up study

2020 ◽  
Vol 6 (33) ◽  
pp. eabc1202 ◽  
Author(s):  
Jing Qin ◽  
Chong You ◽  
Qiushi Lin ◽  
Taojun Hu ◽  
Shicheng Yu ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Incubation Period ◽  
Renewal Process ◽  
Low Cost ◽  
Disease Onset ◽  
Recall Bias ◽  
Cross Sectional ◽  
Follow Up Study ◽  
Period Distribution

We have proposed a novel, accurate low-cost method to estimate the incubation-period distribution of COVID-19 by conducting a cross-sectional and forward follow-up study. We identified those presymptomatic individuals at their time of departure from Wuhan and followed them until the development of symptoms. The renewal process was adopted by considering the incubation period as a renewal and the duration between departure and symptoms onset as a forward time. Such a method enhances the accuracy of estimation by reducing recall bias and using the readily available data. The estimated median incubation period was 7.76 days [95% confidence interval (CI): 7.02 to 8.53], and the 90th percentile was 14.28 days (95% CI: 13.64 to 14.90). By including the possibility that a small portion of patients may contract the disease on their way out of Wuhan, the estimated probability that the incubation period is longer than 14 days was between 5 and 10%.

scholarly journals Trends in Concussion Return-to-Play Timelines Among High School Athletes From 2007 Through 2009

2013 ◽  
Vol 48 (6) ◽  
pp. 836-843 ◽  
Author(s):  
Jennifer M. Medina McKeon ◽  
Scott C. Livingston ◽  
Ashley Reed ◽  
Robert G. Hosey ◽  
Williams S. Black ◽  
...  
Keyword(s):  
High School ◽  
Confidence Interval ◽  
Censored Data ◽  
Return To Play ◽  
Prognostic Indicators ◽  
High School Athletes ◽  
Time To Event ◽  
Interscholastic Sports ◽  
Probability Estimates ◽  
Recovery Times

Context: Whereas guidelines about return-to-play (RTP) after concussion have been published, actual prognoses remain elusive. Objective: To develop probability estimates for time until RTP after sport-related concussion. Design: Descriptive epidemiology study. Setting: High school. Patients or Other Participants: Injured high school varsity, junior varsity, or freshman athletes who participated in 1 of 13 interscholastic sports at 7 area high schools during the 2007–2009 academic years. Intervention(s): Athletic trainers employed at each school collected concussion data. The athletic trainer or physician on site determined the presence of a concussion. Athlete-exposures for practices and games also were captured. Main Outcome Measure(s): Documented concussions were categorized by time missed from participation using severity outcome intervals (same-day return, 1- to 2-day return, 3- to 6-day return, 7- to 9-day return, 10- to 21-day return, >21-day return, no return [censored data]). We calculated Kaplan-Meier time-to-event probabilities that included censored data to determine the probability of RTP at each of these time intervals. Results: A total of 81 new concussions were documented in 478 775 athlete-exposures during the study period. After a new concussion, the probability of RTP (95% confidence interval) was 2.5% (95% confidence interval = 0.3, 6.9) for a 1- to 2-day return, 71.3% (95% confidence interval = 59.0, 82.9) for a 7- to 9-day return, and 88.8% (95% confidence interval = 72.0, 97.2) for a 10- to 21-day return. Conclusions: For high school athletes, RTP within the first 2 days after concussion was unlikely. After 1 week, the probability of return rose substantially (approximately 71%). Prognostic indicators are used to educate patients about the likely course of disease. Whereas individual symptoms and recovery times vary, prognostic time-to-event probabilities allow clinicians to provide coaches, parents, and athletes with a prediction of the likelihood of RTP within certain timeframes after a concussion.

Exploring Migration Data Using Interval-Censored Time-to-Event Models

2008 ◽  
Vol 72 (5) ◽  
pp. 1211-1219 ◽  
Author(s):  
John Fieberg ◽  
Glenn Delgiudice
Keyword(s):  
Time To Event ◽  
Migration Data ◽  
Interval Censored ◽  
Event Models

scholarly journals An ensemble method for interval-censored time-to-event data

Biostatistics ◽  
2019 ◽  
Author(s):  
Weichi Yao ◽  
Halina Frydman ◽  
Jeffrey S Simonoff
Keyword(s):  
Censored Data ◽  
Cox Model ◽  
Conditional Inference ◽  
Interval Censored Data ◽  
Time To Event ◽  
Data Set ◽  
Interval Censored ◽  
Survival Tree ◽  
True Relationship ◽  
Tree Method

Summary Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longitudinal studies generate interval-censored data; one common example occurs in medical studies that entail periodic follow-up. In this article, we propose a survival forest method for interval-censored data based on the conditional inference framework. We describe how this framework can be adapted to the situation of interval-censored data. We show that the tuning parameters have a non-negligible effect on the survival forest performance and guidance is provided on how to tune the parameters in a data-dependent way to improve the overall performance of the method. Using Monte Carlo simulations, we find that the proposed survival forest is at least as effective as a survival tree method when the underlying model has a tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and outperforms the survival tree method and Cox model when the true relationship is nonlinear. We illustrate the application of the method on a tooth emergence data set.

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