scholarly journals A chaotic growth model of agricultural population

2002 ◽  
Vol 47 (1) ◽  
pp. 97-103
Author(s):  
Vesna Jablanovic ◽  
Nada Lakic
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Population Movement ◽  
Formal Framework ◽  
The World ◽  
Logistic Growth Model ◽  
Autoregression Models ◽  
Set Up ◽  
The Stability ◽  
Chaotic Growth

Using the autoregression models, the paper considers movement of agricultural population. Irregular movement of agricultural population can be analyzed within the formal framework of the chaotic growth model. The basic aims of this paper are: firstly, to set up a chaotic growth model of agricultural population; and secondly, to analyze the stability of agricultural population movement according to the presented logistic growth model in the world and eight group of countries in the period 1967-1997.

scholarly journals Forecasting the Stability of COVID-19 on Indian Dataset with Prophet Logistic Growth Model 

2020 ◽  
Author(s):  
Nishtha Phutela ◽  
Arushi G Bakshi ◽  
Sunil Gupta ◽  
Goldie Gabrani
Keyword(s):  
Infectious Disease ◽  
Mathematical Models ◽  
Growth Model ◽  
Logistic Growth ◽  
The World ◽  
Logistic Growth Model ◽  
The Stability

Abstract Recently COVID-2019, a highly infectious disease has been declared as Pandemic by WHO, and since then the researchers all over the world are making attempts to predict the likely progression of this pandemic using various mathematical models. In this paper, we are using logistic growth model to find out the stability of this pandemic and Prophet Model to forecast the total number of confirmed cases that would be caused by COVID-19 in India.

scholarly journals Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world

2020 ◽  
Vol 101 (3) ◽  
pp. 1561-1581 ◽  
Author(s):  
Ke Wu ◽  
Didier Darcet ◽  
Qian Wang ◽  
Didier Sornette
Keyword(s):  
Growth Model ◽  
Western Europe ◽  
Control Measures ◽  
Logistic Growth ◽  
Global Pandemic ◽  
The World ◽  
Containment Measures ◽  
Logistic Growth Model ◽  
The Impact ◽  
Scenario Projections

Abstract Started in Wuhan, China, the COVID-19 has been spreading all over the world. We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that have been or are undergoing major outbreaks. We dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, we made scenario projections on the development of the outbreak in other countries. We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectories in western countries, in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay. We identified three groups of countries in different level of outbreak progress, and provide informative implications for the current global pandemic.

scholarly journals Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

2020 ◽  
Author(s):  
Ke Wu ◽  
Didier Darcet ◽  
Qian Wang ◽  
Didier Sornette
Keyword(s):  
South Korea ◽  
Growth Model ◽  
Significant Risk ◽  
Control Measures ◽  
Upper And Lower Bounds ◽  
Logistic Growth ◽  
Italian Population ◽  
The World ◽  
Logistic Growth Model ◽  
The Impact

AbstractBackgroundthe COVID-19 has been successfully contained in China but is spreading all over the world. We use phenomenological models to dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We use the experience from China to analyze the calibration results on Japan, South Korea, Iran, Italy and Europe, and make future scenario projections.Methodswe calibrate the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model to the reported number of infected cases from Jan. 19 to March 10 for the whole of China, 29 provinces in China, four severely affected countries and Europe as a whole. The different models provide upper and lower bounds of our scenario predictions.ResultsWe quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogenous situations across provinces. Based on Chinese experience, we identify a high risk in Japan with estimated total confirmed cases as of March 25 being 1574 (95% CI: [880, 2372]), and 5669 (95% CI: [988, 11340]) by June. For South Korea, we expect the number of infected cases to approach the ceiling, 7928 (95% CI: [6341, 9754]), in 20 days. We estimate 0.15% (95% CI: [0.03%, 0.30%]) of Italian population to be infected in a positive scenario. We would expect 114867 people infected in Europe in 10 days, in a negative but probable scenario, corresponding to 0.015% European population.ConclusionsThe extreme containment measures implemented by China were very effective with some instructive variations across provinces. For other countries, it is almost inevitable to see the continuation of the outbreak in the coming months. Japan and Italy are in serious situations with no short-term end to the outbreak to be expected. There is a significant risk concerning the upcoming July 2020 Summer Olympics in Tokyo. Iran’s situation is highly uncertain with unclear and negative future scenarios, while South Korea is approaching the end of the outbreak. Both Europe and the USA are at early stages of the outbreak, posing significant health and economic risks to the world in absence of serious measures.

Activities: Activities for the Logistic Growth Model; or, Invasion of the Killer Moths

1997 ◽  
Vol 90 (7) ◽  
pp. 588-597
Author(s):  
Robert (Bob) Iovinelli
Keyword(s):  
Growth Model ◽  
Exponential Function ◽  
Exponential Growth ◽  
Rapid Growth ◽  
Insect Population ◽  
Logistic Growth ◽  
The World ◽  
Logistic Growth Model ◽  
Teacher's Guide ◽  
Better Than

Teacher's Guide: When students begin to study exponential growth and they see a model for the rapid growth of, say, an insect population, they may wonder, “Why has this little bug not taken over the world if it can grow so fast? It is a reasonable question that allows the introduction of a function that models the situation better than the straightforward exponential function. The growth of the population of a species, when first introduced into an environment, can be subdivided into several different stages only one of which is exponential.

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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