scholarly journals Longer incubation periods of SARS-CoV-2 infection in infants

2021 ◽  
Author(s):  
Char Leung
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Nasal Congestion ◽  
Large Body ◽  
Likelihood Estimation ◽  
Infants And Children ◽  
Web Pages ◽  
Health Authorities ◽  
Incubation Periods ◽  
Interval Censored

Objective: A large body of research has described the incubation period of SARS-CoV-2 infection, an important metric for assessing the risk of developing a disease as well as surveillance. While longer incubation periods for elderly have been found, it remains elusive whether this also holds true for infants and children, partly due to the lack of data. The present work clarified the incubation periods of COVID-19 for infants and children. Methods: Using the data released by the Chinese health authorities and municipal offices, statistical comparisons of clinical features were made between infants (aged below 1 year) and children (aged between 1 and 17 years). An age-varying incubation period distribution period was modeled using maximum likelihood estimation modified for interval censored exposure time and age. Discussion: Reported in 56 web pages, a total of 65 cases from 20 provinces dated between January and June 2020, including 18 infants and 47 children, were eligible for inclusion. Infants appeared to bear more severe clinical courses, as demonstrated by the higher prevalence of breathing difficulty as well as nasal congestion. In contrast, fever was less prominent in infants than in children. The incubation period was found to decrease with age, with infants appearing to have longer incubation periods. Conclusion: Fever remained to be one of the most commonly seen symptoms in infants and children with SARS-CoV-2 infection and have continued to determine the time of symptom onset. While shorter incubation periods should be seen in patients with weaker immune system due to weaker antiviral response that is beneficial for viral growth, the longer incubation period in infants may be due to their weaker febrile response to the virus, leading to prolonged symptom onset.

scholarly journals 2367. Selecting Testing Frequency for Estimation of Incubation Periods: A Simulation Study Based on Clostridioides difficile Observations

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S816-S816
Author(s):  
Brigid Wilson ◽  
Mustafa S Ascha ◽  
Justin O’Hagan ◽  
Curtis Donskey
Keyword(s):  
Incubation Period ◽  
Simulation Study ◽  
Symptom Onset ◽  
Pathogen Transmission ◽  
Sample Sizes ◽  
Clostridioides Difficile ◽  
Testing Frequency ◽  
Incubation Periods ◽  
Interval Censored ◽  
The Impact

Abstract Background Estimates of the incubation period (time between pathogen transmission and symptom onset) for an infection inform infection control and prevention measures. However, observation of the exact transmission and onset times rarely occurs and “coarse,” or doubly interval-censored, data about these exact times are typically used for estimation. The effect of coarseness on the required number of symptomatic cases and the uncertainty of the estimates is unknown, prompting a simulation study informed by data from an investigation of the incubation period of Clostridioides difficile. Methods We simulated incubation period data assuming a log-normal distribution, a true median incubation period of 7 days, and a standard deviation of 1 day for sample sizes of 50 to 300 symptomatic cases. For each sample size, we simulated 1000 datasets and examined the impact of testing frequencies, considering intervals between tests of 0.25 to 2 times the median incubation period (1.75 to 14 days) about both transmission and symptom onset times. With these doubly interval-censored observed values, we fit accelerated failure time models to estimate the median incubation time and its 95% confidence interval (CI). Comparing the coverage of the true median and the widths of the CIs, we summarized simulation results across sample sizes and testing frequencies. Results Model results from all combinations of sample sizes and testing frequencies yielded median incubation period CIs close to the target 95% coverage level (Figure 1). The width of the 95% CI about the median decreased with larger sample sizes and shorter times between tests (Figure 2). Thus, similar estimates and confidence intervals would be observed from 100 symptomatic cases with a testing frequency of 3.5 days as from 200 symptomatic cases tested every 14 days. Conclusion The frequency of testing is a key factor in planning studies to estimate incubation periods for infectious diseases. To achieve a desired degree of certainty in estimation, increased frequency of testing can reduce the number of symptomatic cases required. We showed that simulations can assist in planning natural history studies, and these methods could be extended to include population data (e.g., transmission incidence) and cost constraints. Disclosures All authors: No reported disclosures.

scholarly journals Association between incubation period and clinical characteristics of patients with COVID-19

2020 ◽  
Vol 48 (9) ◽  
pp. 030006052095683
Author(s):  
Yeyu Cai ◽  
Jiayi Liu ◽  
Haitao Yang ◽  
Mian Wang ◽  
Qingping Guo ◽  
...  
Keyword(s):  
Incubation Period ◽  
Disease Severity ◽  
Symptom Onset ◽  
Clinical Characteristics ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Receiver Operator Characteristic Curve ◽  
Hunan Province ◽  
Laboratory Findings ◽  
Incubation Periods

Purpose To investigate associations between the clinical characteristics and incubation periods of patients infected with coronavirus disease 2019 (COVID-19) in Wuhan, China. Methods Complete clinical and epidemiological data from 149 patients with COVID-19 at a hospital in Hunan Province, China, were collected and retrospectively analyzed. Results Analysis of the distribution and receiver operator characteristic curve of incubation periods showed that 7 days was the optimal cut-off value to assess differences in disease severity between groups. Patients with shorter (≤7 days) incubation periods (n = 79) had more severe disease, longer durations of hospitalization, longer times from symptom onset to discharge, more abnormal laboratory findings, and more severe radiological findings than patients with longer (>7 days) incubation periods. Regression and correlation analyses also showed that a shorter incubation period was associated with longer times from symptom onset to discharge. Conclusion The associations between the incubation periods and clinical characteristics of COVID-19 patients suggest that the incubation period may be a useful marker of disease severity and prognosis.

Probable longer incubation period for human infection with avian influenza A(H7N9) virus in Jiangsu Province, China, 2013

2014 ◽  
Vol 142 (12) ◽  
pp. 2647-2653 ◽  
Author(s):  
Y. HUANG ◽  
K. XU ◽  
D. F. REN ◽  
J. AI ◽  
H. JI ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Incubation Period ◽  
Influenza A ◽  
Human Infection ◽  
Jiangsu Province ◽  
H7n9 Virus ◽  
Medical Surveillance ◽  
Health Authorities ◽  
Incubation Periods ◽  
Public Health Authorities

SUMMARYHuman infection with the emerging avian influenza A(H7N9) virus in China in 2013 has raised global concerns. We conducted a retrospective descriptive study of 27 confirmed human influenza A(H7N9) cases in Jiangsu Province, to elaborate poultry-related exposures and to provide a more precise estimate of the incubation periods of the illness. The median incubation period was 6 days (range 2–10 days) in cases with single known exposure and was 7·5 days (range 6·5–12·5 days) in cases with exposures on multiple days, difference between the two groups was not significant (Z = −1·895, P = 0·058). The overall median incubation period for all patients was estimated to be 7·5 days (range 2–12·5 days). Our findings further highlight the necessity for public health authorities to extend the period of medical surveillance from 7 days to 10 days.

scholarly journals Demand for longer quarantine period among common and uncommon COVID-19 infections: a scoping review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhi-Yao Li ◽  
Yu Zhang ◽  
Liu-Qing Peng ◽  
Rong-Rong Gao ◽  
Jia-Rui Jing ◽  
...  
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Failure Time ◽  
Accelerated Failure Time Model ◽  
China National Knowledge Infrastructure ◽  
Active Monitoring ◽  
Time Model ◽  
Infected People ◽  
Interval Censored ◽  
Coarse Information

Abstract Background As one of the non-pharmacological interventions to control the transmission of COVID-19, determining the quarantine duration is mainly based on the accurate estimates of the incubation period. However, patients with coarse information of the exposure date, as well as infections other than the symptomatic, were not taken into account in previously published studies. Thus, by using the statistical method dealing with the interval-censored data, we assessed the quarantine duration for both common and uncommon infections. The latter type includes the presymptomatic, the asymptomatic and the recurrent test positive patients. Methods As of 10 December 2020, information on cases have been collected from the English and Chinese databases, including Pubmed, Google scholar, CNKI (China National Knowledge Infrastructure) and Wanfang. Official websites and medias were also searched as data sources. All data were transformed into doubly interval-censored and the accelerated failure time model was applied. By estimating the incubation period and the time-to-event distribution of worldwide COVID-19 patients, we obtain the large percentiles for determining and suggesting the quarantine policies. For symptomatic and presymptomatic COVID-19 patients, the incubation time is the duration from exposure to symptom onset. For the asymptomatic, we substitute the date of first positive result of nucleic acid testing for that of symptom onset. Furthermore, the time from hospital discharge or getting negative test result to the positive recurrence has been calculated for recurrent positive patients. Results A total of 1920 laboratory confirmed COVID-19 cases were included. Among all uncommon infections, 34.1% (n = 55) of them developed symptoms or were identified beyond fourteen days. Based on all collected cases, the 95th and 99th percentiles were estimated to be 16.2 days (95% CI 15.5–17.0) and 22.9 days (21.7‒24.3) respectively. Besides, we got similar estimates based on merely symptomatic and presymptomatic infections as 15.1 days (14.4‒15.7) and 21.1 days (20.0‒22.2). Conclusions There are a certain number of infected people who require longer quarantine duration. Our findings well support the current practice of the extended active monitoring. To further prevent possible transmissions induced and facilitated by such infectious outliers after the 14-days quarantine, properly prolonging the quarantine duration could be prudent for high-risk scenarios and in regions with insufficient test resources.

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 ◽  
Author(s):  
Jing Qin ◽  
Chong You ◽  
Qiushi Lin ◽  
Taojun Hu ◽  
Shicheng Yu ◽  
...  
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Prevention And Control ◽  
Recall Bias ◽  
Cross Sectional ◽  
Follow Up Study ◽  
Incubation Periods ◽  
Current Outbreak ◽  
And Control

SummaryBackgroundThe current outbreak of coronavirus disease 2019 (COVID-19) has quickly spread across countries and become a global crisis. However, one of the most important clinical characteristics in epidemiology, the distribution of the incubation period, remains unclear. Different estimates of the incubation period of COVID-19 were reported in recent published studies, but all have their own limitations. In this study, we propose a novel low-cost and accurate method to estimate the incubation distribution.MethodsWe have conducted a cross-sectional and forward follow-up study by identifying those asymptomatic individuals at their time of departure from Wuhan and then following them until their symptoms developed. The renewal process is hence adopted by considering the incubation period as a renewal and the duration between departure and symptom onset as a forward recurrence time. Under mild assumptions, the observations of selected forward times can be used to consistently estimate the parameters in the distribution of the incubation period. Such a method enhances the accuracy of estimation by reducing recall bias and utilizing the abundant and readily available forward time data.FindingsThe estimated distribution of forward time fits the observations in the collected data well. The estimated median of incubation period is 8·13 days (95% confidence interval [CI]: 7·37-8·91), the mean is 8·62 days (95% CI: 8·02-9·28), the 90th percentile is 14·65 days (95% CI: 14·00-15·26), and the 99th percentile is 20·59 days (95% CI: 19·47, 21·62). Compared with results in other studies, the incubation period estimated in this study is longer.InterpretationBased on the estimated incubation distribution in this study, about 10% of patients with COVID-19 would not develop symptoms until 14 days after infection. Further study of the incubation distribution is warranted to directly estimate the proportion with long incubation periods.FundingThis research is supported by National Natural Science Foundation of China grant 8204100362 and Zhejiang University special scientific research fund for COVID-19 prevention and control.Research in contextEvidence before this studyBefore the current outbreak of coronavirus disease (COVID-19) in China, there were two other coronaviruses that have caused major global epidemics over the last two decades. Severe acute respiratory syndrome (SARS) spread to 37 countries and caused 8424 cases and 919 deaths in 2002-03, while Middle East respiratory syndrome (MERS) spread to 27 countries, causing 2494 cases and 858 deaths worldwide to date. Precise knowledge of the incubation period is crucial for the prevention and control of these diseases. We have searched PubMed and preprint archives for articles published as of February 22, 2020, which contain information about these diseases by using the key words of “COVID-19”, “SARS”, “MERS”, “2019-nCoV”, “coronavirus”, and “incubation”. We have found 15 studies that estimated the distribution of the incubation period. There are four articles focused on COVID-19, five on MERS, and six on SARS. Most of these studies had limited sample sizes and were potentially influenced by recall bias. The estimates for mean, median, and percentiles of the incubation period from these articles are summarized in Table 1.Added value of this studyIn the absence of complete and robust contact-tracing data, we have inferred the distribution of the incubation period of COVID-19 from the durations between departure from Wuhan and symptom onset for the confirmed cases. More than 1000 cases were collected from publicly available data. The proposed approach has a solid theoretical foundation and enhances the accuracy of estimation by reducing recall bias and utilizing a large pool of samples.Implications of all the available evidenceBased on our model, about 10% of patients with COVID-19 do not develop symptoms until 14 days after infection. Further study of individuals with long incubation periods is warranted.

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 Subcritical Transmission in the Early Stage of COVID-19 in Korea

2021 ◽  
Vol 18 (3) ◽  
pp. 1265
Author(s):  
Yong Sul Won ◽  
Jong-Hoon Kim ◽  
Chi Young Ahn ◽  
Hyojung Lee
Keyword(s):  
Incubation Period ◽  
Early Stage ◽  
Probability Distributions ◽  
Likelihood Estimation ◽  
Contact Tracing ◽  
Statistical Characterization ◽  
Serial Interval ◽  
Imported Cases ◽  
The Mean ◽  
Reporting Delays

While the coronavirus disease 2019 (COVID-19) outbreak has been ongoing in Korea since January 2020, there were limited transmissions during the early stages of the outbreak. In the present study, we aimed to provide a statistical characterization of COVID-19 transmissions that led to this small outbreak. We collated the individual data of the first 28 confirmed cases reported from 20 January to 10 February 2020. We estimated key epidemiological parameters such as reporting delay (i.e., time from symptom onset to confirmation), incubation period, and serial interval by fitting probability distributions to the data based on the maximum likelihood estimation. We also estimated the basic reproduction number (R0) using the renewal equation, which allows for the transmissibility to differ between imported and locally transmitted cases. There were 16 imported and 12 locally transmitted cases, and secondary transmissions per case were higher for the imported cases than the locally transmitted cases (nine vs. three cases). The mean reporting delays were estimated to be 6.76 days (95% CI: 4.53, 9.28) and 2.57 days (95% CI: 1.57, 4.23) for imported and locally transmitted cases, respectively. The mean incubation period was estimated to be 5.53 days (95% CI: 3.98, 8.09) and was shorter than the mean serial interval of 6.45 days (95% CI: 4.32, 9.65). The R0 was estimated to be 0.40 (95% CI: 0.16, 0.99), accounting for the local and imported cases. The fewer secondary cases and shorter reporting delays for the locally transmitted cases suggest that contact tracing of imported cases was effective at reducing further transmissions, which helped to keep R0 below one and the overall transmissions small.

Natural maize phytochemicals for control of maize mycoflora and aflatoxigenic fungi

2009 ◽  
Vol 2 (3) ◽  
pp. 305-312 ◽  
Author(s):  
A. Nesci ◽  
S. Marín ◽  
M. Etcheverry ◽  
V. Sanchis
Keyword(s):  
Ferulic Acid ◽  
Incubation Period ◽  
Cinnamic Acid ◽  
Inhibitory Effect ◽  
Aspergillus Section Flavi ◽  
Aflatoxigenic Fungi ◽  
Incubation Periods ◽  
Maize Grain ◽  
Aflatoxin B ◽  
Aspergillus Section

This research was undertaken to evaluate the effects of the natural phytochemicals trans-cinnamic acid (CA) alone at concentrations of 20 and 25 mM, ferulic acid (FA) at concentration of 30 mM and two mixtures, CA-FA (20+30 mM) and CA-FA (25+30 mM) on natural maize mycoflora, Aspergillus section Flavi population and aflatoxin B1 production. These studies were carried out in maize grain in relation to a water activity of 0.99, 0.97 and 0.94. CA at 25 mM and the mixture CA-FA (25+30 mM) were the most effective treatments at inhibiting natural maize mycoflora at all aw assayed after 11 and 35 days of incubation at 25 °C. In general, 20 mM CA caused complete inhibition of Aspergillus section Flavi population at all aw values tested during all incubation period without an additional inoculum. 20 mM CA and 25 mM CA showed the major inhibitory effect on aflatoxin B1 accumulation of control and Aspergillus section Flavi additionally inoculated during all incubation periods. The data showed that CA and FA could be considered as effective fungitoxicants for natural maize mycoflora and aflatoxigenic fungi in the aw range 0.99 to 0.94. The information obtained shows promise for controlling aflatoxigenic fungi in stored maize.

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