Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Estimation of incubation period and generation time based on observed length-biased epidemic cohort with censoring for COVID-19 outbreak in China.

ABSTRACT: The incubation period and generation time are key characteristics in the analysis of infectious diseases. The commonly used contact-tracing-based estimation of incubation distribution is highly influenced by the individuals' judgment on the possible date of exposure, and might lead to significant errors. On the other hand, interval censoring-based methods are able to utilize a much larger set of traveling data but may encounter biased sampling problems. The distribution of generation time is usually approximated by observed serial intervals. However, it may result in a biased estimation of generation time, especially when the disease is infectious during incubation. In this paper, the theory from renewal process is partially adopted by considering the incubation period as the interarrival time, and the duration between departure from Wuhan and onset of symptoms as the mixture of forward time and interarrival time with censored intervals. In addition, a consistent estimator for the distribution of generation time based on incubation period and serial interval is proposed for incubation-infectious diseases. A real case application to the current outbreak of COVID-19 is implemented. We find that the incubation period has a median of 8.50 days (95% confidence interval [CI] [7.22; 9.15]). The basic reproduction number in the early phase of COVID-19 outbreak based on the proposed generation time estimation is estimated to be 2.96 (95% CI [2.15; 3.86]).

SUBMITTER: Deng Y

PROVIDER: S-EPMC7362037 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Joint Estimation of Generation Time and Incubation Period for Coronavirus Disease 2019.

Project description:BackgroundCoronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution.MethodsWe developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19.ResultsThe estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1-5.6), and mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows.ConclusionsProperly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.

| S-EPMC8499762 | biostudies-literature

Comprehensive estimation for the length and dispersion of COVID-19 incubation period: a systematic review and meta-analysis.

Project description:PurposeTo estimate the central tendency and dispersion for incubation period of COVID-19 and, in turn, assess the effect of a certain length of quarantine for close contacts in active monitoring.MethodsLiterature related to SARS-CoV-2 and COVID-19 was searched through April 26, 2020. Quality was assessed according to Agency for Healthcare Research and Quality guidelines. Log-normal distribution for the incubation period was assumed to estimate the parameters for each study. Incubation period median and dispersion were estimated, and distribution was simulated.ResultsFifty-six studies encompassing 4095 cases were included in this meta-analysis. The estimated median incubation period for general transmissions was 5.8 days [95% confidence interval (95% CI): 5.3, 6.2]. Incubation period was significantly longer for asymptomatic transmissions (median: 7.7 days; 95% CI 6.3, 9.4) than for general transmissions (P = 0.0408). Median and dispersion were higher for SARS-CoV-2 incubation compared to other viral respiratory infections. Furthermore, about 12 in 10,000 contacts in active monitoring would develop symptoms after 14 days, or below 1 in 10,000 for asymptomatic transmissions. Meta-regression suggested that each 10-year increase in age resulted in an average 16% increment in length of median incubation (incubation period ratio, 1.16, 95% CI 1.01, 1.32; P = 0.0250).ConclusionThis study estimated the median and dispersion of the SARS-CoV-2 incubation period more precisely. A 14-day quarantine period is sufficient to trace and identify symptomatic infections.

| S-EPMC8372687 | biostudies-literature

Estimation of the incubation period of COVID-19 in Vietnam.

Project description:ObjectiveTo estimate the incubation period of Vietnamese confirmed COVID-19 cases.MethodsOnly confirmed COVID-19 cases who are Vietnamese and locally infected with available data on date of symptom onset and clearly defined window of possible SARS-CoV-2 exposure were included. We used three parametric forms with Hamiltonian Monte Carlo method for Bayesian Inference to estimate incubation period for Vietnamese COVID-19 cases. Leave-one-out Information Criterion was used to assess the performance of three models.ResultsA total of 19 cases identified from 23 Jan 2020 to 13 April 2020 was included in our analysis. Average incubation periods estimated using different distribution model ranged from 6.0 days to 6.4 days with the Weibull distribution demonstrated the best fit to the data. The estimated mean of incubation period using Weibull distribution model was 6.4 days (95% credible interval (CrI): 4.89-8.5), standard deviation (SD) was 3.05 (95%CrI 3.05-5.30), median was 5.6, ranges from 1.35 to 13.04 days (2.5th to 97.5th percentiles). Extreme estimation of incubation periods is within 14 days from possible infection.ConclusionThis analysis provides evidence for an average incubation period for COVID-19 of approximately 6.4 days. Our findings support existing guidelines for 14 days of quarantine of persons potentially exposed to SARS-CoV-2. Although for extreme cases, the quarantine period should be extended up to three weeks.

| S-EPMC7757798 | biostudies-literature

Incubation period for outbreak-associated, non-typhoidal salmonellosis cases, Minnesota, 2000-2015.

Project description:Incubation period for non-typhoidal Salmonella (NTS) infections is generally reported as 6-72 h despite numerous reports of foodborne NTS outbreaks with median incubation periods >3 days. We summarised 16 years of Minnesota foodborne NTS outbreaks to better estimate the expected range of incubation periods for NTS infections. Of the 1517 NTS outbreak cases, 725 had enough data to calculate a precise incubation period. The median incubation period was 45 h; 77 (11%) cases had incubations ⩽12 h and 211 (29%) cases had incubations >72 h. Incubation period length varied by outbreak vehicle type, Salmonella serotype and outbreak setting. Based on our data, a more accurate description would be that the incubation of NTS infection is usually from 12 to 96 h, that incubations in >96 to 144 h (>4 to 6-day) range are not unusual and that incubations from 7 to 9 days and occasionally longer also occur.

| S-EPMC9134522 | biostudies-literature

Estimation of the incubation period of COVID-19 using viral load data.

Project description:The incubation period, or the time from infection to symptom onset, of COVID-19 has usually been estimated by using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in the cases' recall of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used reported data on viral load for 30 hospitalized patients from multiple countries (Singapore, China, Germany, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.85 days (95 % CI: 5.05, 6.77), 2.65 days (2.04, 3.41), and 12.99 days (9.98, 16.79), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach, especially when it is impractical to directly observe the infection event.

| S-EPMC7959696 | biostudies-literature

Mean generation function model in AIDS epidemic estimation.

Project description:BackgroundSince the first case of HIV infection was reported in China in 1985, the incidence and mortality of AIDS have been increasing rapidly, which has caused serious damage to the life and health of people in China and all over the world. Therefore, it is of great significance to study the technique for predicting AIDS morbidity and mortality. The purpose of this research is to explore the applicability of the mean generation function model (MGFM) in the early warning of AIDS morbidity and mortality, to predict its prevalence trend, to enrich the prediction techniques and methods of AIDS research and to provide suggestions for AIDS transmission control.MethodsIn this research, the MGFM was applied to predict the incidence and mortality of AIDS in China. AIDS incidence and mortality data in China from 2008 to 2019 were used to construct the prediction model.ResultsThe MGFM can predict the annual incidence and mortality of AIDS. The model constructed in this research predicted that the incidence and mortality of AIDS in China will continue to increase from 2020 to 2023.ConclusionThe mean birth function model was an effective method to monitor and predict the changing trend of AIDS incidence and mortality in China.

| S-EPMC9013215 | biostudies-literature

Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information.

Project description:Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China.

| S-EPMC8242591 | biostudies-literature

Estimating SARS incubation period.

Project description: Not available

| S-EPMC3320416 | biostudies-literature

Pathogenesis of experimental bovine spongiform encephalopathy (BSE): estimation of tissue infectivity according to incubation period.

Project description:This paper reports the results of tissue infectivity assays of bovine spongiform encephalopathy (BSE) agent in orally exposed cattle at stages during the incubation period. Estimations of the titre of infectivity in central nervous system (CNS), certain peripheral nerve ganglia and distal ileum tissue were made according to time post exposure from the relationship between incubation period and dose for RIII mice and C57bl mice using data from titrations of brain material from cases of BSE. The rate of increase of infectivity in the bovine CNS was then estimated, taking into account these tissue infectivity titres, the variability of the brain titre of clinical field cases of BSE, and the probability density of the expected number of months before clinical onset of each infected bovine. The doubling time for CNS was shown to equal 1.2 months. The titre in the thoracic dorsal root ganglia (DRG) was, on average, approximately 1 log units less than CNS, and cervical DRG approximately 0.5 log less than thoracic DRG. The pattern of increase of infectivity in the distal ileum is that of an initial increase up to 14-18 months post exposure, followed by a decrease, which is likely to be highly variable between animals. These results will be informative for future risk assessments of BSE, especially in relation to reviewing current control measures.

| S-EPMC2695012 | biostudies-literature

Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19.

Project description:The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.

| S-EPMC9747081 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data