scholarly journals COVID19 - A Correlation Study of Infection Fatality Rate vs Age

2021 ◽  
Author(s):  
JAYDIP DATTA
Keyword(s):  
Critical Care ◽  
Regression Analysis ◽  
Logistic Model ◽  
Case Fatality Rate ◽  
Linear Regression Analysis ◽  
Case Fatality ◽  
Correlation Study ◽  
Asymptomatic Infection ◽  
Fatality Rate ◽  
Special Case

Abstract In this article one of the most important epidemiological parameter ie Infection fatality rate [ 1 ] is correlated with age of the population through a sigmoid statistics of Logistic model. The IFR is a special case of case fatality rate ( CFR ) . The CFR ( 1 ) is termed as the number of deaths due to symptomatic Covid infection within entire population per unit time . The IFR is a special case of CFR where number of deaths to be considered as total number of deaths due to symptomatic as well as asymptomatic infection within the same population per unit time .The sigmoid fit can also be approximated to modified quadratic fit [ 4-5 ]. CFR can be more specifically correlated to comorbidities [8 ]through linear regression analysis. co morbidities due to SARS-COV-2 infection for different chronic diseases like heart , Lung , Kidney , related chronic failure are analysed by a significant Pearson statistics ( 10 ) are discussed here . The IFR can be realised from mild to hospitalisation under ICU , critical care and finally severity to death( 9,12).

scholarly journals COVID19 - A Correlation Study of Infection Fatality Rate vs Age

2020 ◽  
Author(s):  
JAYDIP DATTA
Keyword(s):  
Logistic Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Correlation Study ◽  
Asymptomatic Infection ◽  
Entire Population ◽  
Fatality Rate ◽  
Special Case

Abstract In this review one of the most important epidemiological parameter ie Infection fatality rate ( 1 ) is correlated with age of the population through a sigmoid statistics of Logistic model. The IFR is a special case of case fatality rate ( CFR ) . The CFR ( 1 ) is termed as the number of deaths due to symptomatic Covid infection within entire population per unit time . The IFR is a special case of CFR where number of deaths to be considered as total number of deaths due to symptomatic as well as asymptomatic infection within the same population per unit time .The sigmoid fit can also be approximated to modified quadratic fit [ 4,5 ].

scholarly journals COVID19 - A Correlation Study of Infection Fatality Rate vs Age

2020 ◽  
Author(s):  
JAYDIP DATTA
Keyword(s):  
Logistic Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Correlation Study ◽  
Asymptomatic Infection ◽  
Entire Population ◽  
Fatality Rate ◽  
Special Case

Abstract In this review one of the most important epidemiological parameter ie Infection fatality rate ( 1 ) is correlated with age of the population through a sigmoid statistics of Logistic model. The IFR is a special case of case fatality rate ( CFR ) . The CFR ( 1 ) is termed as the number of deaths due to symptomatic Covid infection within entire population per unit time . The IFR is a special case of CFR where number of deaths to be considered as total number of deaths due to symptomatic as well as asymptomatic infection within the same population per unit time .The sigmoid fit can also be approximated to modified quadratic fit [ 4,5 ].

scholarly journals INFECTION FATALITY RATE OF COVID19 – A LOGISTIC MODEL

2020 ◽  
Author(s):  
JAYDIP DATTA
Keyword(s):  
Data Analysis ◽  
Logistic Model ◽  
Case Fatality ◽  
Asymptomatic Infection ◽  
Entire Population ◽  
Fatality Rate ◽  
Case Fatality Ratio ◽  
Special Case

In this review one of the most important epidemiological parameter ie Infection fatality ratio ( 1 ) is correlated with age of the population through a sigmoid statistics of Logistic odel. The IFR is a special case of case fatality ratio ( CFR ) . The CFR ( 1 ) is termed as the number of deaths due to symptomatic Covid infection within entire population per unit time . The IFR is a special case of CFR where number of deaths to be cons-idered as total number of deaths due symptomatic as well as asymptomatic infection within the same population per unit time .The data analysis of IFRvs Age of the population shows a significant correlation ( r ) of Sigmoid model ( 3-4 ) .

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 Novel Uses of Three-Parameter Logistic Models and First-Derivative Models for the Coronavirus Disease (COVID-19) Epidemic in the United States, in Three Distinct Scenarios

2020 ◽  
Author(s):  
Bishoy T. Samuel
Keyword(s):  
Logistic Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Logistic Models ◽  
The United States ◽  
Fatality Rate ◽  
First Derivative ◽  
The Third ◽  
Variable Versus ◽  
Growth Deceleration

Abstract Background:Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020.Methods:Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases.Results:All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11.Conclusion:Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

scholarly journals Novel uses of three-parameter logistic models and first-derivative models for the Coronavirus Disease (COVID-19) epidemic in the United States, in three distinct scenarios

2020 ◽  
Author(s):  
Bishoy T. Samuel
Keyword(s):  
Logistic Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Logistic Models ◽  
The United States ◽  
Fatality Rate ◽  
First Derivative ◽  
The Third ◽  
Variable Versus ◽  
Growth Deceleration

Abstract Background Forecasting the current coronavirus disease (COVID-19) epidemic in the United States necessitates novel mathematical models for accurate predictions. This paper examines novel uses of three-parameter logistic models and first-derivative models through three distinct scenarios that have not been examined in the literature as of July 14, 2020. Methods Using publicly available data, statistical software was used to conduct a non-linear least-squares estimate to generate a three-parameter logistic model, with a subsequently generated first-derivative model. In the first scenario a logistic model was used to examine the natural log of COVID-19 cases as the dependent variable (versus day number), on July 11 and May 1. Independent t-test analyses were used to test comparative coefficient differences across models. In the second scenario, a first-derivative model was derived from a base three-parameter logistic model for April 27, examining time to peak mortality and decrease in case fatality rate. In the third scenario, a first-derivative model of mortality through July 11 as the dependent variable, versus confirmed cases, was generated to look at case fatality rate relative to increasing cases. Results All models generated were statistically significant with R2 > 99%. The logistic models in the first scenario best predicted time to growth deceleration in the natural log of cases in the U.S. (slowing of exponential growth), estimated at March 11, 2020. For the May 1 data, independent t-test analyses of comparative coefficients across models were useful to track improvements from implemented public health measures. The first-derivative model in the second scenario on April 27, when the epidemic was more controlled, showed peak mortality around April 12-13, with a case fatality rate of < 1,000 deaths and trending down. The first-derivative model in the third scenario estimated a near-zero case fatality rate to occur at 4 million confirmed cases. It has not been affected by fluctuations in mortality from June 29 through July 11. Conclusion Three-parameter logistic models and first-derivative models have utility in predicting time to growth deceleration, and case fatality rates relative to cases. They can objectively assess improvements of implemented epidemiologic measures and have applicable public health safety implications.

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