scholarly journals Application Of The Hybrid Method Nonlinear Regression With Modified Logistic Growth Model - Exponential Double Smoothing For Forecasting Covid-19 Cases In Indonesia And Armenia

2020 ◽  
Vol 17 (2) ◽  
pp. 252-266
Author(s):  
Andy Rezky Pratama Syam Arez
Keyword(s):  
Growth Model ◽  
Nonlinear Regression ◽  
Virus Disease ◽  
Decision Makers ◽  
Logistic Growth ◽  
Forecasting Method ◽  
Logistic Growth Model ◽  
Double Smoothing ◽  
The Government ◽  
Second Wave

Since the first cases of Covid-19 (Corona Virus Disease-19) infection were officially recognized and recorded in Indonesia on March 2, 2020 and March 1, 2020 in Armenia, the addition of new cases has not shown any indication of sloping. The relatively high number of new cases indicates that Indonesia has not yet passed the peak of the pandemic. As for Armenia, the addition of new cases indicates a new pandemic peak to be faced. In these conditions, an important question for decision makers (the Government) to find answers to is when and at what level of total cases will the COVID-19 pandemic end in Indonesia or the second wave in Armenia. Based on this, the forecasting method of Hybrid Nonlinear Regression With Modified Logistic Growth Model - Double Smoothing Exponential and Classical methods is used to predict the Covid-19 cases that occur in Indonesia and Armenia. Based on the model formed, the peak of Covid-19 cases in Indonesia is predicted to occur on November 26, 2020, with the number of cases reaching 5968 cases. As for Armenia, the peak of Covid-19 cases will occur on November 15, 2020, with the number of cases reaching 3098 cases. Covid-19 in both countries is predicted to decline and be constant in 2021. For the country, Indonesia is predicted to begin to stabilize and be controlled in July - August 2021. As for Armenia, Covid-19 is predicted to be under control and approaching 0 cases in February - March 2021.

scholarly journals Application Of The Hybrid Method Nonlinear Regression With Modified Logistic Growth Model-Exponential Double Smoothing For Forecasting Covid-19 Cases In Indonesia And Armenia

Khazanah ◽  
2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Andy Rezky Pratama Syam ◽  
Keyword(s):  
Growth Model ◽  
Nonlinear Regression ◽  
Hybrid Method ◽  
Good Method ◽  
Logistic Growth ◽  
Forecasting Method ◽  
Logistic Growth Model ◽  
Double Smoothing ◽  
The Government ◽  
Second Wave

Since the first cases of Covid-19 infection were officially recognized and recorded in Indonesia on March 2, 2020 and March 1, 2020 in Armenia, the addition of new cases has not shown any indication of sloping. The relatively high number of new cases indicates that Indonesia has not yet passed the peak of the pandemic. As for Armenia, the addition of new cases indicates a new pandemic peak to be faced. In these conditions, an important question for decision makers (the Government) to find answers to is when and at what level of total cases will the COVID-19 pandemic end in Indonesia or the second wave in Armenia. Forecasting method of Hybrid Nonlinear Regression With Modified Logistic Growth Model - Double Smoothing Exponential and Classical methods is used to predict the Covid-19 cases that occur in Indonesia and Armenia. Based on the model formed, the peak of Covid-19 cases in Indonesia is predicted to occur on November 26, 2020, with the number of cases reaching 5968 cases. As for Armenia, the peak of Covid-19 cases will occur on November 15, 2020, with the number of cases reaching 3098 cases. Covid-19 in both countries is predicted to decline and be constant in 2021. For the country, Indonesia is predicted to begin to stabilize and be controlled in July - August 2021. As for Armenia, Covid-19 is predicted to be under control and approaching 0 cases in February - March 2021. Forecasting models for the Covid-19 cases in Indonesia and Armenia are different, where for the Covid-19 case in Indonesia the Nonlinear Regression with Logistic Growth Model can be used and for the country of Armenia, the Nonlinear Regression with Modified Logistic Growth Model must be used because it has 2 peak cases. Hybrid method is a very good method for optimizing forecast results. Its application in the Covid-19 case in Indonesia and in Armenia shows that the Hybrid method produces a better MAPE value than the Nonlinear Regression with Logistic Growth Model alone or the Exponential Double Smoothing method alone

Regression Analysis of Observed Foundation Settlement by Modified Logistic Growth Model

2011 ◽  
Vol 250-253 ◽  
pp. 2583-2587
Author(s):  
Yu Qi Li ◽  
Huan Zhang ◽  
Yi Ran Liu
Keyword(s):  
Regression Analysis ◽  
Growth Model ◽  
Nonlinear Regression ◽  
Logistic Growth ◽  
Model Parameters ◽  
Foundation Settlement ◽  
Geological Condition ◽  
Nonlinear Regression Analysis ◽  
Geological Conditions ◽  
Logistic Growth Model

Logistic model is modified through introducing the pseudo construction settlement. Based on the observed settlement data of foundation in Yangshan deepwater port project, Logistic growth model and modified Logistic growth model are used for nonlinear regression analysis of foundation settlement respectively. It is indicated that the fitting curves by using modified Logistic growth model agree better with the observed settlement values than those by using Logistic growth model and that the correlation coefficients by using modified Logistic growth model are also bigger. Model parameters of different geological conditions obtained by nonlinear regression analysis can be used for significant reference to foundation settlement prediction of similar geological condition in other deepwater port.

scholarly journals Progression of COVID-19 in Indian States - Forecasting Endpoints Using SIR and Logistic Growth Models

2020 ◽  
Author(s):  
Bhoomika Malhotra ◽  
Vishesh Kashyap
Keyword(s):  
Growth Model ◽  
Sir Model ◽  
Logistic Growth ◽  
Indian States ◽  
Health Crisis ◽  
Social Distancing ◽  
First Case ◽  
Logistic Growth Model ◽  
The Government ◽  
The Impact

COVID-19 has led to the most widespread public health crisis in recent history. The first case of the disease was detected in India on 31 January 2019, and confirmed cases stand at 74,281 as of 13 May 2020. Mathematical modeling can be utilized to forecast the final numbers as well as the endpoint of the disease in India and its states, as well as assess the impact of social distancing measures. In the present work, the Susceptible-Infected-Recovered (SIR) model and the Logistic Growth model have been implemented to predict the endpoint of COVID-19 in India as well as three states accounting for over 55% of the total cases - Maharashtra, Gujarat and Delhi. The results using the SIR model indicate that the disease will reach an endpoint in India on 12 September, while Maharashtra, Gujarat and Delhi will reach endpoints on 20 August, 30 July and 9 September respectively. Using the Logistic Regression model, the endpoint for India is predicted on 23 July, while that for Maharashtra, Gujarat and Delhi is 5 July, 23 June and 10 August respectively. It is also observed that the case numbers predicted by the SIR model are greater than those for the Logistic Growth model in each case. The results suggest that the lockdown enacted by the Government of India has had only a moderate impact on the spread of COVID-19, and emphasize the need for firm implementation of social distancing guidelines.

scholarly journals Standard and anomalous second waves in the COVID-19 pandemic

2021 ◽  
Author(s):  
Giovani L. Vasconcelos ◽  
Arthur A. Brum ◽  
Francisco A. G. Almeida ◽  
Antônio M. S. Macêdo ◽  
Gerson C. Duarte-Filho ◽  
...  
Keyword(s):  
Growth Model ◽  
Excellent Agreement ◽  
Empirical Data ◽  
Extreme Values ◽  
Logistic Function ◽  
Time Dependent ◽  
Logistic Growth ◽  
Model Parameters ◽  
Logistic Growth Model ◽  
Second Wave

ABSTRACTWe apply a generalised logistic growth model, with time dependent parameters, to describe the fatality curves of the COVID-19 disease for several countries that exhibit a second wave of infections. The model parameters vary as a function of time according to a logistic function, whose two extreme values, i.e., for early and late times, characterise the first and second waves, respectively. We show that the theoretical curves are in excellent agreement with the empirical data for all cases considered. The model also allows for predictions about the time of occurrence and relative severity of the second wave, in comparison to the first wave. It is shown furthermore that the COVID-19 second waves can be generically classified in two main types, namely, standard and anomalous second waves, according as to whether the second wave starts well after or still during the first wave, respectively. We have also observed that the standard second waves tend, in their majority, to be more severe than the corresponding first wave, whereas for anomalous second waves the opposite occurs.

scholarly journals Extended logistic growth model for heterogeneous populations

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.

A Neoclassical Growth Model for Population Dynamics in a Homogeneous Habitat

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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