scholarly journals The collective wisdom in the COVID-19 research: comparison and synthesis of epidemiological parameter estimates in preprints and peer-reviewed articles

2020 ◽  
Author(s):  
Yuejiao Wang ◽  
Zhidong Cao ◽  
Dajun Zeng ◽  
Qingpeng Zhang ◽  
Tianyi Luo
Keyword(s):  
Incubation Period ◽  
Natural Science ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Infectious Period ◽  
Parameter Estimates ◽  
Research Papers ◽  
Collective Wisdom ◽  
Epidemiological Parameters

Background Research papers related to COVID-19 have exploded. We aimed to explore the academic value of preprints through comparing with peer-reviewed publications, and synthesize the parameter estimates of the two kinds of literature. Method We collected papers regarding the estimation of four key epidemiological parameters of the COVID-19 in China: the basic reproduction number (R0), incubation period, infectious period, and case-fatality-rate (CFR). PubMed, Google Scholar, medRxiv, bioRxiv, arRxiv, and SSRN were searched by 20 March, 2020. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Further, four parameters were synthesized by bootstrap, and their validity was verified by susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model based on the context of China. Findings 106 papers were included for analysis. The distributions of four parameters in two literature groups were close, despite that the timeliness of preprints was better. Four parameter estimates changed over time. Synthesized estimates of R0 (3.18, 95% CI 2.85-3.53), incubation period (5.44 days, 95% CI 4.98-5.99), infectious period (6.25 days, 95% CI 5.09-7.51), and CFR (4.51%, 95% CI 3.41%-6.29%) were obtained from the whole parameters space, all with p<0.05. Their validity was evaluated by simulated cumulative cases of SEIRDC model, which matched well with the onset cases in China. Interpretation Preprints could reflect the changes of epidemic situation sensitively, and their academic value shouldn't be neglected. Synthesized results of literatures could reduce the uncertainty and be used for epidemic decision making. Funding The National Natural Science Foundation of China and Beijing Municipal Natural Science Foundation.

scholarly journals New data on clinical features, incubation period, and case fatality rate of SARS in Hong Kong and Canada

2003 ◽  
Vol 7 (19) ◽  
Author(s):  
P Horby
Keyword(s):  
Hong Kong ◽  
Severe Acute Respiratory Syndrome ◽  
Incubation Period ◽  
Clinical Features ◽  
Clinical Data ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Fatality Rate ◽  
New Findings ◽  
Epidemiological Parameters

New findings arising from the outbreaks of severe acute respiratory syndrome (SARS) in Hong Kong and Canada have been reported in two early online publications (1, 2). The authors of the Hong Kong paper used demographic, epidemiological, and clinical data from 1425 SARS cases reported up to 28 April to describe the epidemic in Hong Kong and to estimate key epidemiological parameters. The paper from Canada reviews the clinical features and outcomes of 144 SARS patients admitted to hospital between 7 March and 10 April in Toronto.

scholarly journals COVID19-Tracker: A shiny app to perform comprehensive data visualisation for SARS-CoV-2 epidemic in Spain

2020 ◽  
Author(s):  
Aurelio Tobías ◽  
Joan Valls ◽  
Pau Satorra ◽  
Cristian Tebé
Keyword(s):  
Data Visualization ◽  
Web Application ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Epidemiological Surveillance ◽  
Data Visualisation ◽  
Short Term ◽  
Daily Data ◽  
Shiny App

AbstractData visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of the SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases, and mortality from February 24th, 2020 onwards. Several analyses have been developed to visualize data trends and estimating short-term projections; to estimate the case fatality rate; to assess the effect of the lockdown measures on the trends of incident data; to estimate infection time and the basic reproduction number; and to analyse the excess of mortality. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.

scholarly journals SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

2020 ◽  
Author(s):  
Avaneesh Singh ◽  
Manish Kumar Bajpai
Keyword(s):  
Basic Reproduction Number ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Hospital Beds ◽  
The Impact ◽  
Over Time ◽  
The Basic Reproduction Number

We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

scholarly journals Omicron Impact in India: An Early Analysis of the Ongoing COVID-19 Third Wave

2022 ◽  
Author(s):  
Rajesh Ranjan
Keyword(s):  
Rapid Growth ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Sir Model ◽  
Third Wave ◽  
Fatality Rate ◽  
Sequencing Data ◽  
The Third ◽  
Second Wave

India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare India is currently experiencing the third wave of COVID-19, which began on around 28 Dec. 2021. Although genome sequencing data of a sufficiently large sample is not yet available, the rapid growth in the daily number of cases, comparable to South Africa, United Kingdom, suggests that the current wave is primarily driven by the Omicron variant. The logarithmic regression suggests the growth rate of the infections during the early days in this wave is nearly four times than that in the second wave. Another notable difference in this wave is the relatively concurrent arrival of outbreaks in all the states; the effective reproduction number (Rt) although has significant variations among them. The test positivity rate (TPR) also displays a rapid growth in the last 10 days in several states. Preliminary estimates with the SIR model suggest that the peak to occur in late January 2022 with peak caseload exceeding that in the second wave. Although the Omicron trends in several countries suggest a decline in case fatality rate and hospitalizations compared to Delta, a sudden surge in active caseload can temporarily choke the already stressed healthcare infrastructure. Therefore, it is advisable to strictly adhere to COVID-19 appropriate behavior for the next few weeks to mitigate an explosion in the number of infections.

scholarly journals Short-Term Projection of COVID 19 Cases in Kenya using an Exponential Model

2020 ◽  
Author(s):  
Samuel Kiruri Kirichu
Keyword(s):  
Growth Rate ◽  
Real Time ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Daily Growth Rate ◽  
Daily Growth ◽  
The Real ◽  
Time Reproduction ◽  
First Case

Abstract Introduction: The COVID-19 disease has spread to over 200 countries and territories since the first case was recorded in Wuhan, China in December 2019. In Kenya, the first case of COVID-19 was recorded on 13th March 2020 and since then over five thousand cases have been confirmed as of 26th June 2020. In the same period, one hundred and forty four mortality cases had been recorded in the country. With the rapid changing situation, timely and reliable data is required for monitoring, planning and rapid decision making with an aim of reversing the already deteriorating situation (economic, health, learning among others) in the country. Methods: The study used the exponential growth model to estimate the daily growth rate and the real-time-effective reproduction number. The study also estimated the naïve and the adjusted Case Fatality Rates. Results: The naïve-Case Fatality Rate of 26th June 2020 which was the 106 day after the first case was confirmed in Kenya was estimated as 2.5% while the adjusted Case Fatality Rate with a lag of 2 days was estimated as 2.6%. The daily exponential growth rate was estimated as 0.22 while the real-time reproduction number as of 26th June 2020 was estimated as 1.28 [95% CI: 1.27 – 1.29]. Conclusion: The daily growth rate and the real-time reproduction number indicated that the outbreak was still growing as of the time of analysis.

scholarly journals Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis

2020 ◽  
Author(s):  
Wenqing He ◽  
Grace Y. Yi ◽  
Yayuan Zhu
Keyword(s):  
Confidence Interval ◽  
Basic Reproduction Number ◽  
Incubation Time ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Meta Analysis ◽  
Case Fatality ◽  
Asymptomatic Infection ◽  
Fatality Rate ◽  
The Basic Reproduction Number

AbstractThe coronavirus disease 2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscripts conduct a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% interval (2.41, 3.90), the average incubation time to be 5.08 days with the 95% confidence interval (4.77, 5.39) (in day), the asymptomatic infection rate to be 46% with the 95% confidence interval (18.48%, 73.60%), and the case fatality rate to be 2.72% with 95% confidence interval (1.29%, 4.16%) where asymptomatic infections are accounted for.

scholarly journals Characterizing key attributes of the epidemiology of COVID-19 in China: Model-based estimations

2020 ◽  
Author(s):  
Houssein H. Ayoub ◽  
Hiam Chemaitelly ◽  
Ghina R Mumtaz ◽  
Shaheen Seedat ◽  
Susanne F. Awad ◽  
...  
Keyword(s):  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Principal Factor ◽  
Transmission Dynamics ◽  
Contact Rate ◽  
Uncertainty Interval ◽  
Infection Age ◽  
China Model ◽  
Novel Coronavirus

AbstractBackgroundA novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China in late 2019. The resulting disease, Coronavirus Disease 2019 (COVID-2019), soon became a pandemic. This study aims to characterize key attributes of the epidemiology of this infection in China.MethodsAn age-stratified mathematical model was constructed to describe the transmission dynamics and estimate the age-specific differences in the biological susceptibility to the infection, age-assortativeness in transmission mixing, case fatality rate (CFR), and transition in rate of infectious contacts (and reproduction number R0) following introduction of mass interventions.ResultsThe model estimated the infectious contact rate in early epidemic at 0.59 contacts per day (95% uncertainty interval (UI)=0.48-0.71). Relative to those 60-69 years of age, susceptibility to the infection was only 0.06 in those ≤19 years, 0.34 in 20-29 years, 0.57 in 30-39 years, 0.69 in 40-49 years, 0.79 in 50-59 years, 0.94 in 70-79 years, and 0.88 in ≥80 years. The assortativeness in transmission mixing by age was very limited at 0.004 (95% UI=0.002-0.008). Final CFR was 5.1% (95% UI=4.8-5.4%). R0 rapidly declined from 2.1 (95% UI=1.8-2.4) to 0.06 (95% UI=0.05-0.07) following onset of interventions.ConclusionAge appears to be a principal factor in explaining the patterns of COVID-19 transmission dynamics in China. The biological susceptibility to the infection seems limited among children, intermediate among young to mid-age adults, but high among those >50 years of age. There was no evidence for differential contact mixing by age, consistent with most transmission occurring in households rather than in schools or workplaces.

scholarly journals The Comparison of Epidemiological Characteristics Between Confirmed and Clinically Diagnosed Cases With COVID-19 in Wuhan, China

2020 ◽  
Author(s):  
Fang Shi ◽  
Haoyu Wen ◽  
Rui Liu ◽  
Jianjun Bai ◽  
Fang Wang ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Clinical Diagnosis ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
Fatality Rate ◽  
Elderly Men ◽  
Epidemiological Characteristics ◽  
Over Time

Abstract Background: To put COVID-19 patients into hospital timely, the clinical diagnosis had been implemented in Wuhan in the early outbreak. Here we compared the epidemiological characteristics of laboratory-confirmed and clinically diagnosed cases with COVID-19 in Wuhan.Methods: Demographics, case severity and outcomes of 29886 confirmed cases and 21960 clinically diagnosed cases reported between December 2019 and February 24, 2020, were compared. The risk factors were estimated, and the effective reproduction number of SARS-CoV-2 (Rt) was also calculated.Results: The interval between symptom onset and diagnosis of confirmed and clinically diagnosed cases reduced gradually as time went by, and the proportion of severe and critical cases as well as case fatality rates of the two groups all decreased over time. The proportion of severe and critical cases (21.5% vs 14.0%, P<0.0001) and case fatality rates (5.2% vs 1.2%, P<0.0001) of confirmed cases were all higher than those of clinically diagnosed cases. Risk factors for death we observed in all two groups were older age, male, severe or critical cases. Rt showed a downward trend after the lockdown of Wuhan, it dropped below 1.0 on February 6 among confirmed cases, and February 8 among clinically diagnosed cases.Conclusion: Public health responses taken in Wuhan, including clinical diagnosis, have contributed to slow transmission. In cases where testing kits are insufficient, clinical diagnosis is effective, which is helpful to quarantine or treat infected cases as soon as possible, and prevent the epidemic from worsening. To decrease the case fatality rate of COVID-19, it is necessary to strengthen early warning and intervention of severe and critical elderly men.

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