The Linear Model Formulation of a Multitype Branching Process Applied to Population Dynamics

1974 ◽  
Vol 69 (347) ◽  
pp. 662-664 ◽  
Author(s):  
Manfred Deistler ◽  
Gustav Feichtinger
Keyword(s):  
Population Dynamics ◽  
Linear Model ◽  
Branching Process ◽  
Model Formulation ◽  
Multitype Branching Process

scholarly journals Demographic Dynamics in Multitype Populations with Migrations

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 246
Author(s):  
Manuel Molina-Fernández ◽  
Manuel Mota-Medina
Keyword(s):  
Mathematical Modeling ◽  
Population Dynamics ◽  
Branching Process ◽  
Probability Model ◽  
Biological Systems ◽  
Research Work ◽  
General Setting ◽  
Process Theory ◽  
Multitype Branching Process ◽  
Demographic Dynamics

This research work deals with mathematical modeling in complex biological systems in which several types of individuals coexist in various populations. Migratory phenomena among the populations are allowed. We propose a class of mathematical models to describe the demographic dynamics of these type of complex systems. The probability model is defined through a sequence of random matrices in which rows and columns represent the various populations and the several types of individuals, respectively. We prove that this stochastic sequence can be studied under the general setting provided by the multitype branching process theory. Probabilistic properties and limiting results are then established. As application, we present an illustrative example about the population dynamics of biological systems formed by long-lived raptor colonies.

Branching processes and functional differential equations determining steady-size distributions in cell populations

1995 ◽  
Vol 32 (01) ◽  
pp. 1-10
Author(s):  
Ziad Taib
Keyword(s):  
Differential Equation ◽  
Branching Process ◽  
Branching Processes ◽  
Functional Differential Equations ◽  
Cell Populations ◽  
Size Distributions ◽  
Multitype Branching Process ◽  
Functional Differential ◽  
Intuitive Interpretation ◽  
Infinite Sum

The functional differential equation y′(x) = ay(λx) + by(x) arises in many different situations. The purpose of this note is to show how it arises in some multitype branching process cell population models. We also show how its solution can be given an intuitive interpretation as the probability density function of an infinite sum of independent but not identically distributed random variables.

QUALITATIVE PROPERTIES OF A POPULATION DYNAMICS SYSTEM DESCRIBING PREGNANCY

2005 ◽  
Vol 15 (04) ◽  
pp. 507-554 ◽  
Author(s):  
G. FRAGNELLI ◽  
P. MARTINEZ ◽  
J. VANCOSTENOBLE
Keyword(s):  
Population Dynamics ◽  
Asymptotic Behavior ◽  
Linear Model ◽  
Nonlinear Model ◽  
Total Population ◽  
Type A ◽  
Qualitative Properties

We study a model of population dynamics describing pregnancy: our model is composed by an equation describing the evolution of the total population, and an equation describing the evolution of pregnant individuals. These equations are of course coupled: one coupling expresses that the total population varies with the number of born people, and another coupling says that the number of fecundated individuals depends on the total population. We study three models of that type: a linear model without diffusion, a nonlinear model without diffusion and a linear model with diffusion. For these three models, we study precisely the qualitative properties and the asymptotic behavior of the solutions.

scholarly journals Galton–Watson and branching process representations of the normalized Perron–Frobenius eigenvector

2019 ◽  
Vol 23 ◽  
pp. 797-802
Author(s):  
Raphaël Cerf ◽  
Joseba Dalmau
Keyword(s):  
Markov Chain ◽  
Probability Measure ◽  
Branching Process ◽  
Invariant Probability Measure ◽  
Classical Formula ◽  
Multitype Branching Process ◽  
Primitive Matrix ◽  
Watson Process

Let A be a primitive matrix and let λ be its Perron–Frobenius eigenvalue. We give formulas expressing the associated normalized Perron–Frobenius eigenvector as a simple functional of a multitype Galton–Watson process whose mean matrix is A, as well as of a multitype branching process with mean matrix e(A−I)t. These formulas are generalizations of the classical formula for the invariant probability measure of a Markov chain.

scholarly journals The extinction time of a subcritical branching process related to the SIR epidemic on a random graph

2015 ◽  
Vol 52 (04) ◽  
pp. 1195-1201 ◽  
Author(s):  
Peter Windridge
Keyword(s):  
Random Graph ◽  
Epidemic Model ◽  
Branching Process ◽  
Large Population ◽  
Extinction Time ◽  
Exponential Tail ◽  
Sir Epidemic Model ◽  
Multitype Branching Process ◽  
Type Distribution ◽  
Sir Epidemic

We give an exponential tail approximation for the extinction time of a subcritical multitype branching process arising from the SIR epidemic model on a random graph with given degrees, where the type corresponds to the vertex degree. As a corollary we obtain a Gumbel limit law for the extinction time, when beginning with a large population. Our contribution is to allow countably many types (this corresponds to unbounded degrees in the random graph epidemic model, as the number of vertices tends to∞). We only require a second moment for the offspring-type distribution featuring in our model.

Conditions for extinction in certain bisexual Galton–Watson branching processes

1984 ◽  
Vol 21 (02) ◽  
pp. 414-418
Author(s):  
David M. Hull
Keyword(s):  
Branching Process ◽  
Branching Processes ◽  
Multitype Branching Process ◽  
Extinction Probabilities ◽  
Multitype Branching Processes ◽  
Necessary And Sufficient

A multitype branching process, the n-family community mating process, is introduced for the purpose of comparing extinction probabilities with those of bisexual Galton–Watson branching processes. Consideration of known properties of standard multitype branching processes leads to conditions which are both necessary and sufficient for extinction in a bisexual Galton–Watson branching process. An application is then made to the counterexample of the author's earlier paper.

A linear model of population dynamics

2016 ◽  
Vol 30 (15) ◽  
pp. 1541008 ◽  
Author(s):  
A. A. Lushnikov ◽  
A. I. Kagan
Keyword(s):  
Population Dynamics ◽  
Asymptotic Analysis ◽  
Population Growth ◽  
Master Equation ◽  
Bessel Function ◽  
Population Size ◽  
Linear Model ◽  
Linear Functions ◽  
Modified Bessel Function ◽  
Death Rates

The Malthus process of population growth is reformulated in terms of the probability [Formula: see text] to find exactly [Formula: see text] individuals at time [Formula: see text] assuming that both the birth and the death rates are linear functions of the population size. The master equation for [Formula: see text] is solved exactly. It is shown that [Formula: see text] strongly deviates from the Poisson distribution and is expressed in terms either of Laguerre’s polynomials or a modified Bessel function. The latter expression allows for considerable simplifications of the asymptotic analysis of [Formula: see text].

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