Pitting the Gumbel and logistic growth models against one another to model COVID-19 spread

AbstractIn this paper, we investigate briefly the appropriateness of the widely used logistic growth curve modeling with focus on COVID-19 spread, from a data-driven perspective. Specifically, we suggest the Gumbel growth model for behaviour of COVID-19 cases in European countries in addition to the United States of America (US), for better detecting the growth and prediction. We provide a suitable fit and predict the growth of cases for some selected countries as illustration. Our contribution will stimulate the correct growth spread modeling for this pandemic outbreak.

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Forecasting Novel Corona Positive Cases in India using Truncated Information: A Mathematical Approach

10.1101/2020.04.29.20085175 ◽

2020 ◽

Author(s):

Brijesh P. Singh

Keyword(s):

United States ◽

Growth Model ◽

United States Of America ◽

Initial Phase ◽

The United States ◽

The Other ◽

Logistic Growth ◽

Mathematical Approach ◽

First Case ◽

Logistic Growth Model

AbstractNovel corona virus is declared as pandemic and India is struggling to control this from a massive attack of death and destruction, similar to the other countries like China, Europe, and the United States of America. India reported 2545 cases novel corona confirmed cases as of April 2, 2020 and out of which 191 cases were reported recovered and 72 deaths occurred. The first case of novel corona is reported in India on January 30, 2020. The growth in the initial phase is following exponential. In this study an attempt has been made to model the spread of novel corona infection. For this purpose logistic growth model with minor modification is used and the model is applied on truncated information on novel corona confirmed cases in India. The result is very exiting that till date predicted number of confirmed corona positive cases is very close to observed on. The time of point of inflexion is found in the end of the April, 2020 means after that the increasing growth will start decline and there will be no new case in India by the end of July, 2020.

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Association mining based approach to analyze COVID-19 response and case growth in the United States

Scientific Reports ◽

10.1038/s41598-021-96912-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Satya Katragadda ◽

Raju Gottumukkala ◽

Ravi Teja Bhupatiraju ◽

Azmyin Md. Kamal ◽

Vijay Raghavan ◽

...

Keyword(s):

United States ◽

Data Mining ◽

Growth Curve ◽

Face Mask ◽

The United States ◽

Quality Data ◽

Logistic Growth ◽

Association Mining ◽

Rule Mining ◽

The Common

AbstractContaining the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.

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Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model

Global Health Research and Policy ◽

10.1186/s41256-020-00152-5 ◽

2020 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Ding-Geng Chen ◽

Xinguang Chen ◽

Jenny K. Chen

Keyword(s):

United States ◽

Growth Model ◽

The United States ◽

Logistic Growth ◽

Logistic Growth Model

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Logistic growth curve modeling of US energy production and consumption

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2018.07.049 ◽

2018 ◽

Vol 96 ◽

pp. 46-57 ◽

Cited By ~ 11

Author(s):

Tyler M. Harris ◽

Jay P. Devkota ◽

Vikas Khanna ◽

Pragnya L. Eranki ◽

Amy E. Landis

Keyword(s):

Growth Curve ◽

Energy Production ◽

Growth Curve Modeling ◽

Logistic Growth ◽

Production And Consumption ◽

Curve Modeling

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Non-Linear fitting of Sigmoidal Growth Curves to predict a maximum limit to the total number of COVID-19 cases in the United States. (Preprint)

10.2196/preprints.19571 ◽

2020 ◽

Author(s):

Carlos Dutra Sr

Keyword(s):

United States ◽

Growth Models ◽

Growth Curves ◽

The United States ◽

World Health ◽

Logistic Growth ◽

Linear Fitting ◽

Nonlinear Fitting ◽

Non Linear ◽

Maximum Limit

UNSTRUCTURED In the present work is used non-linear fitting of the "Gompert" and "Logistic" growth models to the number of total COVID-19 cases from the United States as a country and individually by states. The methodology allowed us to estimate that the maximum limit for the total number of cases of COVID-19 patients such as those registered with the World Health Organization will be approximately one million and one hundred thousand cases to the United States. Up to 04/19/20 the models indicate that United States reached 70% of this maximum number of "total cases" and the United States will reach 95% of this limit by 05/14/2020. The application of the nonlinear fitting of growth curves to the individual data of each American state showed that only 25% of them did not reach, on 04/19/20, the percentage of 59% of the maximum limit of "total cases" and that 17 of the 50 states still will not have reached 95% of that limit on 05/14/20.

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Non-Linear Fitting of Sigmoidal Growth Curves to predict a maximum limit to the total number of COVID-19 cases in the United States

10.1101/2020.04.22.20074898 ◽

2020 ◽

Cited By ~ 1

Author(s):

Carlos Maximiliano Dutra

Keyword(s):

United States ◽

Growth Models ◽

Growth Curves ◽

The United States ◽

World Health ◽

Logistic Growth ◽

Linear Fitting ◽

Nonlinear Fitting ◽

Non Linear ◽

Maximum Limit

AbstractIn the present work is used non-linear fitting of the “Gompert” and “Logistic” growth models to the number of total COVID-19 cases from the United States as a country and individually by states. The methodology allowed us to estimate that the maximum limit for the total number of cases of COVID-19 patients such as those registered with the World Health Organization will be approximately one million and one hundred thousand cases to the United States. Up to 04/19/20 the models indicate that United States reached 70% of this maximum number of “total cases” and the United States will reach 95% of this limit by 05/14/2020. The application of the nonlinear fitting of growth curves to the individual data of each American state showed that only 25% of them did not reach, on 04/19/20, the percentage of 59% of the maximum limit of “total cases” and that 17 of the 50 states still will not have reached 95% of that limit on 05/14/20.

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