Project Abstract

Abstract
The discrete logistic model is a widely used mathematical framework for studying population dynamics. In this research, we conduct a critical analysis of the discrete logistic model in the context of structured populations. Structured populations refer to populations that can be divided into distinct subgroups based on specific characteristics, such as age, size, or spatial distribution. The incorporation of population structure into mathematical models allows for a more realistic representation of real-world populations. We first review the basic principles of the discrete logistic model, which describes how population size changes over discrete time intervals based on birth and death rates. We then discuss the extension of the model to include population structure, considering factors such as age-specific fertility and mortality rates, spatial dispersal, and interactions between different subgroups within the population. One of the key advantages of using structured population models is their ability to capture the complexities of real populations, such as age-dependent growth rates and spatial heterogeneity. By incorporating these additional factors into the model, researchers can gain a more nuanced understanding of population dynamics and make more accurate predictions about future population trends. We also explore the challenges and limitations of structured population models, including data requirements for parameter estimation, computational complexity, and the sensitivity of model outcomes to underlying assumptions. Despite these challenges, structured population models provide valuable insights into population dynamics and can help inform conservation efforts, disease control strategies, and resource management decisions. Through a series of case studies and sensitivity analyses, we demonstrate the practical utility of structured population models in various ecological and epidemiological contexts. We highlight the importance of carefully selecting model parameters, validating model predictions against empirical data, and considering the implications of model uncertainty on decision-making processes. Overall, our critical analysis of the discrete logistic model in the context of structured populations underscores the importance of accounting for population heterogeneity and spatial structure in population dynamics research. By improving the realism and predictive accuracy of mathematical models, researchers can make more informed decisions about managing and conserving populations in a rapidly changing world.

Project Overview

INTRODUCTION

The well-known logistic differential equation was originally proposed by the Belgian mathematician Pierre-François Verhulst (1804–1849) in 1838, in order to describe the growth of a population under the assumptions that the rate of growth of the population was proportional to the existing population and the amount of available resources.

When this scenario is "translated" into mathematics, it results to the differential equation

-   (i)

where t denotes time, P0 is the initial population, and r, k are constants associated with the growth rate and the carrying capacity of the population

Although, it can be considered as a simple differential equation, in the sense that it is completely solvable by use of elementary techniques of the theory of differential equations, it has tremendous and numerous applications in various fields. The first application was already mentioned, and it is connected with population problems, and more generally, problems in ecology. Other applications appear in problems of chemistry, linguistics, medicine (especially in modelling the growth of tumors), pharmacology (especially in the production of antibiotic medicines), epidemiology, atmospheric pollution, flow in a river, and so forth.

Nowadays, the logistic differential equation can be found in many biology textbooks and can be considered as a cornerstone of ecology. However, it has also received much criticism by several ecologists.

However, as it often happens in applications, when modelling a realistic problem, one may decide to describe the problem in terms of differential equations or in terms of difference equations. Thus, the initial value problem which describes the population problem studied by Verhulst, could be formulated instead as an initial value problem of a difference equation. Also, there is a great literature on topics regarding discrete analogues of the differential calculus. In this context, the general difference equation

............................. (ii)

has been known as the discrete logistic equation and it serves as an analogue to the initial value problem.

There are several ways to "end up" with (ii) starting (i) from or and some are:

by iterating the function, F(x) = µX(1 - X), ,   which gives rise to the difference equation Xn+1 = µXn (1 - Xn)
by discretizing using a forward difference scheme for the derivative, which gives rise to the difference equation     where , being the step size of the scheme, or
by "translating" the population problem studied by Verhulst in terms of differences: if Pn is the population under study at time , its growth is indicated by . Thus, the following initial value problem appears:


Notice of course that all three equations are special cases of (i)

AIMS AND OBECTIVES

This study is being conducted to critically analyse the Discrete Logistic model and structured populations. At the end of the study; we should be able to

Ø Analyse logistic models under different circumstances and values of the rate of population growth.
Ø Solve some problems involving the application of the Discrete logistic model to real life situations and draw conclusions from the solutions of such
Ø Construct models for some structured populations and their behaviours
Ø Solve problems relating the structured populations to real life situations.