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 COVID19-Global: A shiny application to perform a global comparative data visualization for the SARS-CoV-2 epidemic

2020 ◽  
Author(s):  
Aurelio Tobias ◽  
pau satorra ◽  
Joan Valls ◽  
Cristian Tebe
Keyword(s):  
Data Visualization ◽  
Web Application ◽  
Case Fatality ◽  
Global Scale ◽  
Comparative Data ◽  
Epidemiological Surveillance ◽  
Fatality Rate ◽  
Daily Data ◽  
Online Web ◽  
Shiny Application

Data visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. The COVID19-Global online web application systematically produces daily updated data visualization and analysis of the SARS-CoV-2 epidemic on a global scale. It collects automatically daily data on COVID-19 diagnosed cases and mortality worldwide from January 1st, 2020 onwards. We have implemented comparative data visualization between countries for the most common indicators in epidemiological surveillance to follow an epidemic: attack rate, population fatality rate, case fatality rate, and basic reproduction number. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

scholarly journals COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cristian Tebé ◽  
Joan Valls ◽  
Pau Satorra ◽  
Aurelio Tobías
Keyword(s):  
Data Visualization ◽  
Web Application ◽  
Case Fatality ◽  
Epidemiological Surveillance ◽  
Fatality Rate ◽  
Specific Data ◽  
User Friendly ◽  
And Control ◽  
Shiny Application ◽  
Country Specific

Abstract Background Data analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. Results Data on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number. Conclusions The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

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 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 Another vision of the situation of the COVID-19 pandemic in Mexico during 2020

2021 ◽  
pp. 1-9
Author(s):  
Sergio Isaac De La Cruz Hernández
Keyword(s):  
Health System ◽  
Case Fatality Rate ◽  
Public Health System ◽  
Case Fatality ◽  
Sentinel Surveillance ◽  
Epidemiological Surveillance ◽  
Fatality Rate ◽  
The Public ◽  
Mexican Government ◽  
High Positivity Rate

Abstract The number of coronavirus disease 2019 (COVID-19) cases and deaths registered in Mexico during 2020 could be underestimated, due to the sentinel surveillance adopted in this country. Some consequences of following this type of epidemiological surveillance were the high case fatality rate and the high positivity rate for COVID-19 shown in Mexico in 2020. During this year, the Mexican Ministry of Health only considered cases from the public health system, which followed this sentinel surveillance, but did not consider those cases from the private health system. To better understand this pandemic, it is important to include all the results obtained by all the institutions capable of testing for COVID-19, thus the Mexican Government could make good decisions to protect the population from this disease.

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