scholarly journals Incubation period of COVID-19 in the “live-house” cluster of accurately known infection events and delay time from symptom onset of public reporting observed in cases in Osaka, Japan

2020 ◽  
Author(s):  
Toshihisa Tomie
Keyword(s):  
Infectious Disease ◽  
Latent Period ◽  
Incubation Period ◽  
Symptom Onset ◽  
Delay Time ◽  
Public Reporting ◽  
Period Distribution

AbstractThe incubation period of an infectious disease is very important for control of the disease but estimating the period is not easy because the date of infection is not easy to identify. Accurate incubation period distribution by examining cases in the cluster generated in “live-houses” in Osaka, Japan with known infection events is reported. The distribution of the latent period is also estimated. The modes of incubation and latent periods of COVID-19 in Japan are 4.1 days and 3.3 days, respectively. The mode of the delay time from the onset to reporting is estimated to be 4.7 days, telling that the effects of interventions show up in the number of infections two weeks later after the measures.

scholarly journals Estimating the generation interval for COVID-19 based on symptom onset data

2020 ◽  
Author(s):  
Ganyani Tapiwa ◽  
Kremer Cécile ◽  
Chen Dongxuan ◽  
Torneri Andrea ◽  
Faes Christel ◽  
...  
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Contact Tracing ◽  
Transmission Network ◽  
Generation Interval ◽  
Period Distribution ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Modelling Studies ◽  
Interval Distribution

AbstractBackgroundEstimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution.ConclusionsEstimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.

scholarly journals Epidemiological parameters of coronavirus disease 2019: a pooled analysis of publicly reported individual data of 1155 cases from seven countries

2020 ◽  
Author(s):  
Shujuan Ma ◽  
Jiayue Zhang ◽  
Minyan Zeng ◽  
Qingping Yun ◽  
Wei Guo ◽  
...  
Keyword(s):  
Latent Period ◽  
Incubation Period ◽  
Symptom Onset ◽  
Severe Disease ◽  
Individual Data ◽  
Robust Estimates ◽  
Upper Limit ◽  
Serial Interval ◽  
Epidemiological Parameters ◽  
Time Point

SummaryBackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has been declared a pandemic by the World Health Organization, while several key epidemiological parameters of the disease remain to be clarified. This study aimed to obtain robust estimates of the incubation period, upper limit of latent period (interval between infector’s exposure and infectee’s exposure), serial interval, time point of exposure (the day of infectee’s exposure to infector relative to the latter’s symptom onset date) and basic reproduction number (R0) of COVID-19.MethodsBetween late February and early March of 2020, the individual data of laboratory confirmed cases of COVID-19 were retrieved from 10728 publicly available reports released by the health authorities of and outside China and from 1790 publications identified in PubMed and CNKI. To be eligible, a report had to contain the data that allowed for estimation of at least one parameter. As relevant data mainly came from clustering cases, the clusters for which no evidence was available to establish transmission order were all excluded to ensure accuracy of estimates. Additionally, only the cases with an exposure period spanning 3 days or less were included in the estimation of parameters involving exposure date, and a simple method for determining exposure date was adopted to ensure the error of estimates be small (< 0.3 day). Depending on specific parameters, three or four of normal, lognormal, Weibull, and gamma distributions were fitted to the datasets and the results from appropriate models were presented.FindingsIn total, 1155 cases from China, Japan, Singapore, South Korea, Vietnam, Germany and Malaysia were included for the final analysis. The mean and standard deviation were 7.44 days and 4.39 days for incubation period, 2.52 days and 3.95 days for the upper limit of latent period, 6.70 days and 5.20 days for serial interval, and −0.19 day (i.e., 0.19 day before infector’s symptom onset) and 3.32 days for time point of exposure. R0 was estimated to be 1.70 and 1.78 based on two different formulas. For 39 (6.64%) cases, the incubation periods were longer than 14 days. In 102 (43.78%) infector-infectee pairs, transmission occurred before infectors’ symptom onsets. In 27 (3.92%) infector-infectee pairs, infectees’ symptom onsets occurred before those of infectors. Stratified analysis showed that incubation period and serial interval were consistently longer for those with less severe disease and for those whose primary cases had less severe disease. Asymptomatic transmission was also observed.InterpretationThis study obtained robust estimates of several key epidemiological parameters of COVID-19. The findings support current practice of 14-day quarantine of persons with potential exposure, but also suggest that longer monitoring periods might be needed for selected groups. The estimates of serial interval, time point of exposure and latent period provide consistent evidence on pre-symptomatic transmission. This together with asymptomatic transmission and the generally longer incubation and serial interval of less severe cases suggests a high risk of long-term epidemic in the absence of appropriate control measures.FundingThis work received no funding from any source.

#87: Association Between Healthcare-Associated Respiratory Viral Infections and Length of Stay at a Pediatric Hospital

2021 ◽  
Vol 10 (Supplement_1) ◽  
pp. S15-S15
Author(s):  
Zachary Most ◽  
Michael Sebert ◽  
Patricia Jackson ◽  
Trish M Perl
Keyword(s):  
Length Of Stay ◽  
Incubation Period ◽  
Respiratory Infection ◽  
Symptom Onset ◽  
Viral Infections ◽  
Pediatric Hospital ◽  
Time Period ◽  
Life Years ◽  
Respiratory Viral Infections ◽  
Healthcare Associated

Abstract Background Healthcare-associated infections (HAI) are major preventable causes of morbidity and mortality. While there are fewer overall HAI in children, there is a greater potential impact in disability-adjusted life years. Healthcare-associated respiratory viral infections (HARVI) are not frequently tracked within institutions, yet the risk for such infections in pediatric hospitals is very high. Recent data demonstrate large inter-hospital variability of HARVI incidence that may depend on various factors including the number of immunocompromised patients in the hospital and the presence of shared rooms. We hypothesize that the burden of healthcare-associated respiratory viral infections and their impact on the length of stay (LOS) is substantial at a large urban pediatric hospital. Methods A cohort of all children with any HARVI admitted to a large urban pediatric hospital between July 2017 and June 2018 were included after obtaining IRB approval. We defined a HARVI as a respiratory infection with an onset of symptoms while the patient was hospitalized meeting three criteria: A positive microbiologic test for one of 8 viruses, presence of symptoms of a respiratory infection, and onset of symptoms after admission beyond the minimum incubation period for each virus. Infections with symptom onset after admission beyond the maximum incubation period were considered definite hospital onset whereas others were considered possible hospital onset. The electronic medical record provided data on demographics, underlying medical conditions, hospital length of stay prior to infection and hospital unit of infection, and consequences and outcome of HARVI. The at-risk population for calculation of the incidence of HARVI was all admitted patient-days at the hospital over this time period. Results Between July 2017 and June 2018 the incidence of HARVI (definite or possible hospital onset) was 1.2 infections per 1,000 admitted patient-days (60% due to rhinovirus/enterovirus, 12% due to respiratory syncytial virus, and 9% due to influenza). Overall, 48% of patients were under 2 years of age, 18% were between 2 and 5 years of age, and 34% were over 5 years of age. Twenty-one percent were immunocompromised and 35% had underlying lung disease. The median length of stay prior to symptom onset was 11 days (IQR 5–36 days) and the median total length of stay was 30 days (IQR 15–82.5 days). Eight individuals had more than one HARVI over this time period. Nineteen percent were transferred to the intensive care unit and 7% died during their hospital admission Conclusion HARVI occurs frequently in a pediatric hospital and often in patients with underlying comorbidities. The risk for HARVI increases substantially with increased length of stay. Such data support the need for tracking HARVI in high-risk institutions.

Factors Associated with Resistance to Puccinia arachidis

1982 ◽  
Vol 9 (2) ◽  
pp. 96-97 ◽  
Author(s):  
S. S. Sokhi ◽  
J. S. Jhooty
Keyword(s):  
Latent Period ◽  
Incubation Period ◽  
Infection Frequency ◽  
Rust Infection ◽  
Factors Associated ◽  
Puccinia Arachidis ◽  
Lower Infection

Abstract Peanut rust infection frequency, uredial size, incubation period and latent period was studied on 47 genotypes. Seventeen genotypes namely NCAC 17133-RF, PI,259747, PI,393643, PI,381622, PI,390593, PI,390595, PI,393517, PI,405132, J-11, Jh-352, 39–2, J1–24, 2704, US-74 and MK-374 showed a lower infection frequency and smaller uredosori, longer incubation and latent periods.

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.

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%.

Predictions of the aids epidemic in the U.K.: The use of the back projection method

1989 ◽  
Vol 325 (1226) ◽  
pp. 123-134 ◽  
Keyword(s):  
Incubation Period ◽  
Projection Method ◽  
Hiv Infections ◽  
Back Projection ◽  
Aids Epidemic ◽  
Period Distribution ◽  
Highly Sensitive ◽  
The Future ◽  
Extreme Distributions

The results of this analysis illustrate three points. First, that for predictions of AIDS cases four to five years into the future, the back projection method is largely insensitive to the assumption one makes for the incubation period distribution. The two extreme distributions considered represent the fast and slow extremes of incubation period distribution usually proposed; distributions that lie between these two give predictions within the range of predictions that the two generate. The estimated number of new HIV infections, however, is highly sensitive to the assumed incubation period distribution; prediction of AIDS cases in the long term will be similarly sensitive.

scholarly journals A Delay Differential Equation approach to model the COVID-19 pandemic

2021 ◽  
Author(s):  
Ilya Kiselev ◽  
I.R. Akberdin ◽  
F.A. Kolpakov
Keyword(s):  
Infectious Disease ◽  
Incubation Period ◽  
Population Group ◽  
Mass Action ◽  
Virus Spread ◽  
Disease Epidemiology ◽  
Epidemic Dynamics ◽  
Infectious Disease Epidemiology ◽  
Mass Action Law ◽  
Delay Differential

SEIR (Susceptible - Exposed - Infected - Recovered) approach is a classic modeling method that has frequently been applied to the study of infectious disease epidemiology. However, in the vast majority of SEIR models and models derived from them transitions from one population group to another are described using the mass-action law which assumes population homogeneity. That causes some methodological limitations or even drawbacks, particularly inability to reproduce observable dynamics of key characteristics of infection such as, for example, the incubation period and progression of the disease's symptoms which require considering different time scales as well as probabilities of different disease trajectories. In this paper, we propose an alternative approach to simulate the epidemic dynamics that is based on a system of differential equations with time delays to precisely reproduce a duration of infectious processes (e.g. incubation period of the virus) and competing processes like transition from infected state to the hospitalization or recovery. The suggested modeling approach is fundamental and can be applied to the study of many infectious disease epidemiology. However, due to the urgency of the COVID-19 pandemic we have developed and calibrated the delay-based model of the epidemic in Germany and France using the BioUML platform. Additionally, the stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions implemented in corresponding countries to contain the virus spread. The numerical analysis of the calibrated model demonstrates that adequate simulation of each new wave of the SARS-CoV-2 virus spread requires dynamic changes in the parameter values during the epidemic like reduction of the population adherence to non-pharmaceutical interventions or enhancement of the infectivity parameter caused by an emergence of novel virus strains with higher contagiousness than original one. Both models may be accessed and simulated at https://gitlab.sirius-web.org/covid-19/dde-epidemiology-model utilizing visual representation as well as Jupyter Notebook.

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