Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter 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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Pitting the Gumbel and logistic growth models against one another to model COVID-19 spread

10.1101/2020.05.24.20111633 ◽

2020 ◽

Author(s):

Keunyoung Yoo ◽

Mohammad Arashi ◽

Andriette Bekker

Keyword(s):

United States ◽

Growth Model ◽

Growth Curve ◽

United States Of America ◽

Growth Models ◽

The United States ◽

Growth Curve Modeling ◽

Data Driven ◽

Logistic Growth ◽

Curve Modeling

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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Stability and Hopf bifurcation for a regulated logistic growth model with discrete and distributed delays

Communications in Nonlinear Science and Numerical Simulation ◽

10.1016/j.cnsns.2009.03.006 ◽

2009 ◽

Vol 14 (12) ◽

pp. 4292-4303 ◽

Cited By ~ 11

Author(s):

Shengle Fang ◽

Minghui Jiang

Keyword(s):

Hopf Bifurcation ◽

Growth Model ◽

Distributed Delays ◽

Logistic Growth ◽

Logistic Growth Model

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Optimal Harvesting in the Stochastic Logistic Growth Model with Finite Horizon

SIAM Journal on Control and Optimization ◽

10.1137/110850943 ◽

2013 ◽

Vol 51 (3) ◽

pp. 2335-2355 ◽

Cited By ~ 1

Author(s):

Hiroaki Morimoto

Keyword(s):

Growth Model ◽

Finite Horizon ◽

Logistic Growth ◽

Optimal Harvesting ◽

Logistic Growth Model

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Extended logistic growth model for heterogeneous populations

10.1101/231100 ◽

2017 ◽

Author(s):

Wang Jin ◽

Scott W McCue ◽

Matthew J Simpson

Keyword(s):

Cell Proliferation ◽

Growth Model ◽

Numerical Solutions ◽

Cellular Level ◽

Logistic Growth ◽

Logistic Growth Model ◽

Living Tissues ◽

Discrete Mathematical Model ◽

The Continuum

AbstractCell proliferation is the most important cellular-level mechanism responsible for regulating cell population dynamics in living tissues. Modern experimental procedures show that the proliferation rates of individual cells can vary significantly within the same cell line. However, in the mathematical biology literature, cell proliferation is typically modelled using a classical logistic equation which neglects variations in the proliferation rate. In this work, we consider a discrete mathematical model of cell migration and cell proliferation, modulated by volume exclusion (crowding) effects, with variable rates of proliferation across the total population. We refer to this variability as heterogeneity. Constructing the continuum limit of the discrete model leads to a generalisation of the classical logistic growth model. Comparing numerical solutions of the model to averaged data from discrete simulations shows that the new model captures the key features of the discrete process. Applying the extended logistic model to simulate a proliferation assay using rates from recent experimental literature shows that neglecting the role of heterogeneity can, at times, lead to misleading results.

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Logistic Growth Model of the COVID-19 Pandemic to Decide When to Start the Lockdown

Review of Computer Engineer Studies ◽

10.18280/rces.070202 ◽

2020 ◽

Vol 7 (2) ◽

pp. 26-30

Author(s):

Salwa Lagdali ◽

Abdelali Saidi

Keyword(s):

Growth Model ◽

Logistic Growth ◽

Logistic Growth Model

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A Neoclassical Growth Model for Population Dynamics in a Homogeneous Habitat

10.1115/imece2001/htd-24415 ◽

2001 ◽

Author(s):

Peter Vadasz ◽

Alisa S. Vadasz

Keyword(s):

Experimental Data ◽

Growth Model ◽

Logistic Growth ◽

Lag Phase ◽

Growth Stages ◽

Initial Growth ◽

Neoclassical Model ◽

Wide Range ◽

Logistic Growth Model ◽

Special Case

Abstract A neoclassical model is proposed for the growth of cell and other populations in a homogeneous habitat. The model extends on the Logistic Growth Model (LGM) in a non-trivial way in order to address the cases where the Logistic Growth Model (LGM) fails short in recovering qualitative as well as quantitative features that appear in experimental data. These features include in some cases overshooting and oscillations, in others the existence of a “Lag Phase” at the initial growth stages, as well as an inflection point in the “In curve” of the population size. The proposed neoclassical model recovers also the Logistic Growth Curve as a special case. Comparisons of the solutions obtained from the proposed neoclassical model with experimental data confirm its quantitative validity, as well as its ability to recover a wide range of qualitative features captured in experiments.

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