scholarly journals Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data

Epidemics ◽  
2021 ◽  
pp. 100482
Author(s):  
Shi Zhao ◽  
Biao Tang ◽  
Salihu S Musa ◽  
Shujuan Ma ◽  
Jiayue Zhang ◽  
...  
Keyword(s):  
Latent Period ◽  
Contact Tracing ◽  
Generation Interval

scholarly journals Establishment, contagiousness, and initial spread of SARS-CoV-2 in Canada

FACETS ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 180-194
Author(s):  
Martin Krkošek ◽  
Madeline Jarvis-Cross ◽  
Kiran Wadhawan ◽  
Isha Berry ◽  
Jean-Paul R. Soucy ◽  
...  
Keyword(s):  
Latent Period ◽  
Bayesian Framework ◽  
Contact Tracing ◽  
Transmission Model ◽  
Infectious Period ◽  
Median Reduction ◽  
Community Transmission

This study empirically quantifies dynamics of SARS-CoV-2 establishment and early spread in Canada. We developed a transmission model that was simulation tested and fitted in a Bayesian framework to timeseries of new cases per day prior to physical distancing interventions. A hierarchical version was fitted to all provinces simultaneously to obtain average estimates for Canada. Across scenarios of a latent period of 2–4 d and an infectious period of 5–9 d, the R0 estimate for Canada ranges from a minimum of 3.0 (95% CI: 2.3–3.9) to a maximum of 5.3 (95% CI: 3.9–7.1). Among provinces, the estimated commencement of community transmission ranged from 3 d before to 50 d after the first reported case and from 2 to 25 d before the first reports of community transmission. Among parameter scenarios and provinces, the median reduction in transmission needed to obtain R0 < 1 ranged from 46% (95% CI: 43%–48%) to 89% (95% CI: 88%–90%). Our results indicate that local epidemics of SARS-CoV-2 in Canada entail high levels of stochasticity, contagiousness, and observation delay, which facilitates rapid undetected spread and requires comprehensive testing and contact tracing for its containment.

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 Intrinsic and realized generation intervals in infectious-disease transmission

2015 ◽  
Vol 282 (1821) ◽  
pp. 20152026 ◽  
Author(s):  
David Champredon ◽  
Jonathan Dushoff
Keyword(s):  
Public Health ◽  
Mathematical Models ◽  
Disease Transmission ◽  
Interval Data ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Infectious Disease Transmission ◽  
Generation Interval ◽  
Public Health Strategies ◽  
The Relationship

The generation interval is the interval between the time when an individual is infected by an infector and the time when this infector was infected. Its distribution underpins estimates of the reproductive number and hence informs public health strategies. Empirical generation-interval distributions are often derived from contact-tracing data. But linking observed generation intervals to the underlying generation interval required for modelling purposes is surprisingly not straightforward, and misspecifications can lead to incorrect estimates of the reproductive number, with the potential to misguide interventions to stop or slow an epidemic. Here, we clarify the theoretical framework for three conceptually different generation-interval distributions: the ‘intrinsic’ one typically used in mathematical models and the ‘forward’ and ‘backward’ ones typically observed from contact-tracing data, looking, respectively, forward or backward in time. We explain how the relationship between these distributions changes as an epidemic progresses and discuss how empirical generation-interval data can be used to correctly inform mathematical models.

scholarly journals Inferring generation-interval distributions from contact-tracing data

2019 ◽  
Author(s):  
Sang Woo Park ◽  
David Champredon ◽  
Jonathan Dushoff
Keyword(s):  
Spatial Scales ◽  
Observation Time ◽  
Disease Spread ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Spatial Effects ◽  
Individual Level ◽  
Generation Interval ◽  
Effective Generation ◽  
Interval Distribution

AbstractGeneration intervals, defined as the time between when an individual is infected and when that individual infects another person, link two key quantities that describe an epidemic: the reproductive number,, and the rate of exponential growth,r. Generation intervals are often measured through contact tracing by identifying who infected whom. We study how observed intervals differ from “intrinsic” intervals that could be estimated by tracing individual-level infectiousness, and identify both spatial and temporal effects, including censoring (due to observation time), and the effects of susceptible depletion at various spatial scales. Early in an epidemic, we expect the variation in the observed generation intervals to be mainly driven by the censoring and the population structure near the source of disease spread; therefore, we predict that correcting observed intervals for the effect of temporal censoring butnotfor spatial effects will provide a spatially informed “effective” generation-interval distribution, which will correctly linkrand. We develop and test statistical methods for temporal corrections of generation intervals, and confirm our prediction using individual-based simulations on an empirical network.

scholarly journals Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020

2020 ◽  
Vol 25 (17) ◽  
Author(s):  
Tapiwa Ganyani ◽  
Cécile Kremer ◽  
Dongxuan Chen ◽  
Andrea Torneri ◽  
Christel Faes ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Reproduction Number ◽  
Credible Interval ◽  
Sensitivity Analyses ◽  
Contact Tracing ◽  
Generation Interval ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Distribution Sensitivity ◽  
Interval Distribution

Background Estimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies. Aim We estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased. Methods We 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 serial interval, proportions of pre-symptomatic transmission and reproduction numbers. Results The mean generation interval was 5.20 days (95% credible interval (CrI): 3.78–6.78) for Singapore and 3.95 days (95% CrI: 3.01–4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32–67) for Singapore and 62% (95% CrI: 50–76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information. Conclusion High estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.

scholarly journals Inferring generation-interval distributions from contact-tracing data

2020 ◽  
Vol 17 (167) ◽  
pp. 20190719 ◽  
Author(s):  
Sang Woo Park ◽  
David Champredon ◽  
Jonathan Dushoff
Keyword(s):  
Initial Rate ◽  
Spatial Scales ◽  
Observation Time ◽  
Disease Spread ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Spatial Effects ◽  
Individual Level ◽  
Generation Interval ◽  
Interval Distribution

Generation intervals, defined as the time between when an individual is infected and when that individual infects another person, link two key quantities that describe an epidemic: the initial reproductive number, R initial , and the initial rate of exponential growth, r . Generation intervals can be measured through contact tracing by identifying who infected whom. We study how realized intervals differ from ‘intrinsic’ intervals that describe individual-level infectiousness and identify both spatial and temporal effects, including truncating (due to observation time), and the effects of susceptible depletion at various spatial scales. Early in an epidemic, we expect the variation in the realized generation intervals to be mainly driven by truncation and by the population structure near the source of disease spread; we predict that correcting realized intervals for the effect of temporal truncation but not for spatial effects will provide the initial forward generation-interval distribution, which is spatially informed and correctly links r and R initial . We develop and test statistical methods for temporal corrections of generation intervals, and confirm our prediction using individual-based simulations on an empirical network.

scholarly journals Cyclic exit strategies to suppress COVID-19 and allow economic activity

2020 ◽  
Author(s):  
Omer Karin ◽  
Yinon M. Bar-On ◽  
Tomer Milo ◽  
Itay Katzir ◽  
Avi Mayo ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Latent Period ◽  
Economic Activity ◽  
Reproduction Number ◽  
Economic Consequences ◽  
Contact Tracing ◽  
Cyclic Schedule ◽  
Economic Sectors ◽  
Part Time ◽  
Exit Strategies

Many countries have applied lockdown that helped suppress COVID-19, but with devastating economic consequences. Here we propose exit strategies from lockdown that provide sustainable, albeit reduced, economic activity. We use mathematical models to show that a cyclic schedule of 4-day work and 10-day lockdown, or similar variants, can prevent resurgence of the epidemic while providing part-time employment. The cycle pushes the reproduction number R below one by reduced exposure time and by exploiting the virus latent period: those infected during work days reach peak infectiousness during lockdown days. The number of work days can be adapted in response to observations. Throughout, full epidemiological measures need to continue including hygiene, physical distancing, compartmentalization, testing and contact tracing. This conceptual framework, when combined with other interventions to control the epidemic, can offer the beginnings of predictability to many economic sectors.

scholarly journals Serial interval and generation interval for respectively the imported and local infectors estimated using reported contact-tracing data of COVID-19 in China

2020 ◽  
Author(s):  
Menghui Li ◽  
Kai Liu ◽  
Yukun Song ◽  
Ming Wang ◽  
Jinshan Wu
Keyword(s):  
Reproduction Number ◽  
Contact Tracing ◽  
Time Varying ◽  
Generation Interval ◽  
2Nd Generation ◽  
Serial Interval ◽  
Interval Censored ◽  
Imported Cases ◽  
Emerging Virus ◽  
Censored Likelihood

AbstractBackgroundsThe emerging virus, COVID-19, has caused a massive out-break worldwide. Based on the publicly available contact-tracing data, we identified 337 transmission chains from 10 provinces in China and estimated the serial interval (SI) and generation interval (GI) of COVID-19 in China.MethodsInspired by possibly different values of the time-varying reproduction number for the imported cases and the local cases in China, we divided all transmission events into three subsets: imported (the zeroth generation) infecting 1st-generation locals, 1st-generation locals infecting 2nd-generation locals, and others transmissions among 2+ generations. The corresponding SI (GI) is respec-tively denoted as , and . A Bayesian approach with doubly interval-censored likelihood is employed to fit the lognormal, gamma, and Weibull distribution function of the SI and GI using the identified 337 transmission chains.FindingsIt is found that the estimated , and , thus overall both SI and GI decrease when generation increases.

The Clinical, Roentgenological and Morphological Effects of an Acute Exposure of Phosgene on the Lungs of Dogs

1971 ◽  
Vol 29 ◽  
pp. 236-237
Author(s):  
R. F. Bils ◽  
W. F. Diller ◽  
F. Huth
Keyword(s):  
Latent Period ◽  
Chemical Industry ◽  
Toxic Substance ◽  
Lung Edema ◽  
X Rays ◽  
Beagle Dogs ◽  
Male And Female ◽  
Morphological Alterations ◽  
Before And After ◽  
Electron Microscopes

Phosgene still plays an important role as a toxic substance in the chemical industry. Thiess (1968) recently reported observations on numerous cases of phosgene poisoning. A serious difficulty in the clinical handling of phosgene poisoning cases is a relatively long latent period, up to 12 hours, with no obvious signs of severity. At about 12 hours heavy lung edema appears suddenly, however changes can be seen in routine X-rays taken after only a few hours' exposure (Diller et al., 1969). This study was undertaken to correlate these early changes seen by the roengenologist with morphological alterations in the lungs seen in the'light and electron microscopes.Forty-two adult male and female Beagle dogs were selected for these exposure experiments. Treated animals were exposed to 94.5-107-5 ppm phosgene for 10 min. in a 15 m3 chamber. Roentgenograms were made of the thorax of each animal before and after exposure, up to 24 hrs.

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

Sign in / Sign up

Export Citation Format

Share Document