scholarly journals Early reports of epidemiological parameters of the COVID-19 pandemic

2021 ◽  
Vol 12 (2) ◽  
pp. 65-81
Author(s):  
Keeley Allen ◽  
Ame Elizabeth Parry ◽  
Kathryn Glass
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Rapid Assessment ◽  
Control Measures ◽  
Inclusion Criteria ◽  
Early Analysis ◽  
Outbreak Response ◽  
Median Length ◽  
Serial Interval ◽  
The Mean

Background: The emergence of a new pathogen requires a rapid assessment of its transmissibility, to inform appropriate public health interventions. Methods: The peer-reviewed literature published between 1 January and 30 April 2020 on COVID-19 in PubMed was searched. Estimates of the incubation period, serial interval and reproduction number for COVID-19 were obtained and compared. Results: A total of 86 studies met the inclusion criteria. Of these, 33 estimated the mean incubation period (4–7 days) and 15 included estimates of the serial interval (mean 4–8 days; median length 4–5 days). Fifty-two studies estimated the reproduction number. Although reproduction number estimates ranged from 0.3 to 14.8, in 33 studies (63%), they fell between 2 and 3. Discussion: Studies calculating the incubation period and effective reproduction number were published from the beginning of the pandemic until the end of the study period (30 April 2020); however, most of the studies calculating the serial interval were published in April 2020. The calculated incubation period was similar over the study period and in different settings, whereas estimates of the serial interval and effective reproduction number were setting-specific. Estimates of the serial interval were shorter at the end of the study period as increasing evidence of pre-symptomatic transmission was documented and as jurisdictions enacted outbreak control measures. Estimates of the effective reproduction number varied with the setting and the underlying model assumptions. Early analysis of epidemic parameters provides vital information to inform the outbreak response.

scholarly journals Transmission interval estimates suggest pre-symptomatic spread of COVID-19

2020 ◽  
Author(s):  
Lauren C. Tindale ◽  
Michelle Coombe ◽  
Jessica E. Stockdale ◽  
Emma S. Garlock ◽  
Wing Yin Venus Lau ◽  
...  
Keyword(s):  
Incubation Period ◽  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Interval Estimates ◽  
Secondary Infections ◽  
Serial Interval ◽  
Incubation Periods ◽  
Key Variables ◽  
The Mean ◽  
Interval Distribution

AbstractBackgroundAs the COVID-19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic. We determine the incubation period and serial interval distribution for transmission clusters in Singapore and in Tianjin. We infer the basic reproduction number and identify the extent of pre-symptomatic transmission.MethodsWe collected outbreak information from Singapore and Tianjin, China, reported from Jan.19-Feb.26 and Jan.21-Feb.27, respectively. We estimated incubation periods and serial intervals in both populations.ResultsThe mean incubation period was 7.1 (6.13, 8.25) days for Singapore and 9 (7.92, 10.2) days for Tianjin. Both datasets had shorter incubation periods for earlier-occurring cases. The mean serial interval was 4.56 (2.69, 6.42) days for Singapore and 4.22 (3.43, 5.01) for Tianjin. We inferred that early in the outbreaks, infection was transmitted on average 2.55 and 2.89 days before symptom onset (Singapore, Tianjin). The estimated basic reproduction number for Singapore was 1.97 (1.45, 2.48) secondary cases per infective; for Tianjin it was 1.87 (1.65, 2.09) secondary cases per infective.ConclusionsEstimated serial intervals are shorter than incubation periods in both Singapore and Tianjin, suggesting that pre-symptomatic transmission is occurring. Shorter serial intervals lead to lower estimates of R0, which suggest that half of all secondary infections should be prevented to control spread.

scholarly journals On realized serial and generation intervals given control measures: The COVID-19 pandemic case

2021 ◽  
Vol 17 (3) ◽  
pp. e1008892
Author(s):  
Andrea Torneri ◽  
Pieter Libin ◽  
Gianpaolo Scalia Tomba ◽  
Christel Faes ◽  
James G. Wood ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Transmission Probability ◽  
Theoretical Explanation ◽  
Control Measures ◽  
Strong Impact ◽  
Generation Interval ◽  
Incidence Data ◽  
Intervention Measures ◽  
Serial Interval ◽  
The Mean

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

scholarly journals Epidemiologic Characteristics of COVID-19 in Guizhou Province, China

2021 ◽  
Vol 15 (03) ◽  
pp. 389-397
Author(s):  
Kaike Ping ◽  
Mingyu Lei ◽  
Yun Gou ◽  
Zhongfa Tao ◽  
Guanghai Yao ◽  
...  
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Asymptomatic Carrier ◽  
Control Measures ◽  
Guizhou Province ◽  
Serial Interval ◽  
Two Peaks ◽  
Epidemiologic Characteristics ◽  
Close Contacts

Introduction: At the end of 2019, the COVID-19 broke out, and spread to Guizhou province in January of 2020. Methodology: To acquire the epidemiologic characteristics of COVID-19 in Guizhou province, we collected data from 169 laboratory-confirmed COVID-19 related cases. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and the effective reproduction number. We also presented two representative case studies in Guizhou province: Case Study 1 was an example of the asymptomatic carrier; while Case Study 2 was an example of a large and complex infection chain that involved four different regions, spanning three provinces and eight families. Results: Two peaks in the incidence distribution associated with COVID-19 in Guizhou province were related to the 6.04 days (95% CI: 5.00 – 7.10) of incubation period and 6.14±2.21 days of serial interval. We also discussed the effectiveness of the control measures based on the instantaneous effective reproduction number that was a constantly declining curve. Conclusions: As of February 2, 2020, the estimated effective reproduction number was below 1, and no new cases were reported since February 26. These showed that Guizhou Province had achieved significant progress in preventing the spread of the epidemic. The medical isolation of close contacts was consequential. Meanwhile, the asymptomatic carriers and the super-spreaders must be isolated in time, who would cause a widespread infection.

scholarly journals Epidemiology of a bubonic plague outbreak in Glasgow, Scotland in 1900

2019 ◽  
Vol 6 (1) ◽  
pp. 181695 ◽  
Author(s):  
Katharine R. Dean ◽  
Fabienne Krauer ◽  
Boris V. Schmid
Keyword(s):  
Yersinia Pestis ◽  
Reproduction Number ◽  
Control Measures ◽  
High Rate ◽  
Bubonic Plague ◽  
The Third ◽  
Serial Interval ◽  
The Mean ◽  
First Time ◽  
Epidemiological Parameters

On 3 August 1900, bubonic plague ( Yersinia pestis ) broke out in Glasgow for the first time during the Third Pandemic. The local sanitary authorities rigorously tracked the spread of the disease and they found that nearly all of the 35 cases could be linked by contact with a previous case. Despite trapping hundreds of rats in the area, there was no evidence of a rat epizootic and the investigators speculated that the outbreak could be due to human-to-human transmission of bubonic plague. Here we use a likelihood-based method to reconstruct transmission trees for the outbreak. From the description of the outbreak and the reconstructed trees, we infer several epidemiological parameters. We found that the estimated mean serial interval was 7.4–9.2 days and the mean effective reproduction number dropped below 1 after implementation of control measures. We also found a high rate of secondary transmissions within households and observations of transmissions from individuals who were not terminally septicaemic. Our results provide important insights into the epidemiology of a bubonic plague outbreak during the Third Pandemic in Europe.

scholarly journals Epidemiology of a bubonic plague outbreak in Glasgow, Scotland in 1900

2018 ◽  
Author(s):  
Katharine R. Dean ◽  
Fabienne Krauer ◽  
Boris V. Schmid
Keyword(s):  
Yersinia Pestis ◽  
Reproduction Number ◽  
Control Measures ◽  
High Rate ◽  
Bubonic Plague ◽  
The Third ◽  
Serial Interval ◽  
The Mean ◽  
First Time ◽  
Epidemiological Parameters

AbstractOn August 3, 1900, bubonic plague (Yersinia pestis) broke out in Glasgow for the first time during the Third Pandemic. The local sanitary authorities rigorously tracked the spread of the disease and they found that nearly all of the 35 cases could be linked by contact with a previous case. Despite trapping hundreds of rats in the area, there was no evidence of a rat epizootic and the investigators speculated that the outbreak could be due to human-to-human transmission of bubonic plague. Here we use a likelihood-based method to reconstruct transmission trees for the outbreak. From the description of the outbreak and the reconstructed trees, we infer several epidemiological parameters. We found that the estimated mean serial interval was 7.4 days and the mean effective reproduction number dropped below 1 after implementation of control measures. We also found that there was a high rate of secondary transmissions within households and observations of transmissions from individuals who were not terminally septicemic. Our results provide important insights into the epidemiology of plague that are useful for modeling current and historic plague epidemics.

scholarly journals An improved method to estimate the effective reproduction number of the COVID-19 pandemic: lessons from its application in Greece

2020 ◽  
Author(s):  
Theodore Lytras ◽  
Vana Sypsa ◽  
Demosthenes Panagiotakos ◽  
Sotirios Tsiodras
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Surveillance Data ◽  
Control Measures ◽  
Population Mobility ◽  
Improved Method ◽  
Mobility Patterns ◽  
Routine Surveillance ◽  
Serial Interval ◽  
Tourism Activity

Introduction: Monitoring the time-varying effective reproduction number Rt is crucial for assessing the evolution of the COVID-19 pandemic. We present an improved method to estimate Rt and its application to routine surveillance data from Greece. Methods: Our method extends that of Cori et al (2013), adding Bayesian imputation of missing symptom onset dates, imputation of infection times using an external estimate of the incubation period, and an adjustment for reporting delay. To facilitate its use, we provide an R software package named "bayEStim". We applied the method to COVID-19 surveillance data from Greece, and examined the resulting Rt estimates in relation to control measures applied, in order to assess their effectiveness. We also associated Rt, as a measure of transmissibility, to population mobility as recorded in Google data and to ambient temperature. We used a serial interval between 4 and 7.5 days, and a median incubation period of 5.1 days. Results: In Greece Rt fell rapidly as the first control measures were introduced, dropping below 1 at least a week before a full lockdown came into effect. In mid-July Rt started increasing again, as increased mobility associated with tourism activity was observed. Each 10% of increase in relative mobility increased Rt by 8.1% (95% CrI 6.1-10.2%), whereas each unit celsius of temperature increase decreased Rt by 4.6% (95% CrI 5.4-13.7%). Conclusions: Mobility patterns significantly affect Rt. Most of the reduction in COVID-19 transmissibility in Greece occurred already before the lockdown, likely as a result of decreased population mobility. Lower viral transmissibility in summer does not appear sufficient to counterbalance the increased mobility due to tourism. Monitoring Rt is an essential component of COVID-19 surveillance, and it is crucial for correctly assessing the effect of control measures.

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.

scholarly journals China’s effective control and other countries’ uncharted challenge against COVID-19: an epidemiological and modelling study

2020 ◽  
Author(s):  
Lingling Zheng ◽  
Qin Kang ◽  
Weiyao Liao ◽  
Xiujuan Chen ◽  
Shuai Huang ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Reproduction Number ◽  
Case Reports ◽  
Epidemiological Data ◽  
Control Measures ◽  
Time Dependent ◽  
Effective Control ◽  
List Type ◽  
Global Epidemic ◽  
Serial Interval

AbstractBackgroundOn the present trajectory, COVID is inevitably becoming a global epidemic, leading to concerns regarding the pandemic potential in China and other countries.ObjectiveIn this study, we use the time-dependent reproduction number (Rt) to comprise the COVID transmissibility across different countries.MethodsWe used data from Jan 20, 2019, to Feb 29, 2020, on the number of newly confirmed cases, obtained from the reports published by the CDC, to infer the incidence of infectious over time. A two-step procedure was used to estimate the Rt. The first step used data on known index-secondary cases pairs, from publicly available case reports, to estimate the serial interval distribution. The second step estimated the Rt jointly from the incidence data and the information data in the first step. Rt was then used to simulate the epidemics across all major cities in China and typical countries worldwide.ResultsBased on a total of 126 index-secondary cases pairs from 4 international regions, we estimated that the serial interval for SARS-2-CoV was 4.18 (IQR 1.92 – 6.65) days. Domestically, Rt of China, Hubei province, Wuhan had fallen below 1.0 on 9 Feb, 10 Feb and 13 Feb (Rt were 0.99±0.02, 0.99±0.02 and 0.96±0.02), respectively. Internationally, as of 26 Feb, statistically significant periods of COVID spread (Rt >1) were identified for most regions, except for Singapore (Rt was 0.92±0.17).ConclusionsThe epidemic in China has been well controlled, but the worldwide pandemic has not been well controlled. Worldwide preparedness and vulnerability against COVID-19 should be regarded with more care.What is already known on this subject?The basic reproduction number (R0) and the-time-dependent reproduction number (Rt) are two important indicators of infectious disease transmission. In addition, Rt as a derivative of R0 could be used to assess the epidemiological development of the disease and effectiveness of control measures. Most current researches used data from earlier periods in Wuhan and refer to the epidemiological features of SARS, which are possibly biased. Meanwhile, there are fewer studies discussed the Rt of COVID-19. Current clinical and epidemiological data are insufficient to help us understand the full view of the potential transmission of this disease.What this study adds?We use up-to-data observation of the serial interval and cases arising from local transmission to calculate the Rt in different outbreak level area and every province in China as well as five-top sever outbreak countries and other overseas. By comparing the Rt, we discussed the situation of outbreak around the world.

scholarly journals Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

2020 ◽  
Author(s):  
Robert Challen ◽  
Ellen Brooks-Pollock ◽  
Krasimira Tsaneva-Atanasova ◽  
Leon Danon
Keyword(s):  
Incubation Period ◽  
Time Delays ◽  
Hospital Admissions ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Model Parameters ◽  
Small Data ◽  
Generation Interval ◽  
Serial Interval ◽  
The Impact

AbstractThe serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1–6.2) and SD 4.2 (95% CrI 3.9–4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3–5.41) and SD 1.7 (95% CrI 1.0–2.6) days (fitted gamma distribution), and the incubation period with a mean 5.5 (95% CrI 5.1–5.8) and SD 4.9 (95% CrI 4.5–5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 transmission.

scholarly journals Dynamics of COVID-19 pandemic at constant and time-dependent contact rates

2020 ◽  
Vol 15 ◽  
pp. 28 ◽  
Author(s):  
Marek Kochańczyk ◽  
Frederic Grabowski ◽  
Tomasz Lipniacki
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Exponential Phase ◽  
Contact Rate ◽  
Epidemic Spread ◽  
Early Exponential Phase ◽  
Contact Rates ◽  
Serial Interval ◽  
Hypothetical Scenario ◽  
Multiplication Coefficient

We constructed a simple Susceptible−Exposed–Infectious–Removed model of the spread of COVID-19. The model is parametrised only by the average incubation period, τ, and two rate parameters: contact rate, β, and exclusion rate, γ. The rates depend on nontherapeutic interventions and determine the basic reproduction number, R0 = β/γ, and, together with τ, the daily multiplication coefficient in the early exponential phase, θ. Initial R0 determines the reduction of β required to contain the spread of the epidemic. We demonstrate that introduction of a cascade of multiple exposed states enables the model to reproduce the distributions of the incubation period and the serial interval reported by epidemiologists. Using the model, we consider a hypothetical scenario in which β is modulated solely by anticipated changes of social behaviours: first, β decreases in response to a surge of daily new cases, pressuring people to self-isolate, and then, over longer time scale, β increases as people gradually accept the risk. In this scenario, initial abrupt epidemic spread is followed by a plateau and slow regression, which, although economically and socially devastating, grants time to develop and deploy vaccine or at least limit daily cases to a manageable number.

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