Mathematical Modeling for Predicting Population Of Malaria (CASE STUDY AMINU KANO TEACHING HOSPITAL)
| Format: Ms Word | 1-5 Chapters | Table of Content|
INSTANT PROJECT MATERIAL DOWNLOAD
Study Level: BTech, BSc, BEng, BA, HND, ND or NCE
Amount: ₦4,000.00
ABSTRACT
This project work titled ”mathematical modeling for prediction population of malaria infection” is to aim at providing a comprehensive and highlight on the importance of mathematical modeling in population prediction. As it is natural to think of the population changing from season to season, the study of population growth is one of the problems encountered by both physical and social scientist, to predict both present and future population.
Mathematical and experimentalist are increasingly becoming more interested in historical data and rates at which the population is growing. This leads to the development of logistics and exponential models among many others. However, in this project work, only the exponential growth and decay is used to predict the adult, children and both adult and children population with malaria. It contains the analysis (prediction) of adults, children and both adults and children population with malaria and their corresponding charts representation like pie chart, bar chart and graphs. The data used in analyzing the model was obtain from Aminu Kano Teaching Hospital of the years 2015 and 2020.
Table of Contents
DECLARATION.. ii
CERTIFICATION.. iii
DEDICATION.. iv
ACKNOWLEDGEMENTS. v
ABSTRACT. vi
Table of Contents. vii
CHAPTER ONE. 1
1.0 Introduction. 1
1.1 General Concept of Mathematical Modeling. 3
1.3 Fundamentals Principles of Mathematical Modeling. 4
1.4 Characteristics of a Good Model 4
1.5 Mathematical Modeling Using Linear Differential Equation. 5
1.6 Population Model. 5
1.7 Aim and objectives of the study1. 7
1.8 Significance of the Study. 7
1.9 Scope and Limitation. 8
1.10 Definition of Some Terms. 8
1.10.1 Equation. 8
1.10.2 Variable. 8
1.10.3 Differential Equation. 8
1.10.4 Ordinary Differential Equation. 9
1.10.5 Order of Differential Equation. 9
1.10.6 First Order Differential Equation. 9
1.10.7 Derivative. 10
1.10.8 Modeling. 10
1.10.9 Population. 10
1.10.10 Growth rate. 10
1.10.11 Graphical Representation. 10
CHAPTER TWO.. 11
2.1 Introduction. 11
CHAPTER THREE. 15
RESEARCH METHODOLOGY.. 15
CHAPTER FOUR. 17
DATA PRESENTATION AND ANALYSIS. 17
4.1 Data Representation. 17
Table 4.1 health management information population distribution of 2015 and 2020. 17
4.2 Analysis of Adult Population with Malaria. 18
Table 4.2 Adult population with malaria. 21
Figure 4.1 pie chart distribution of adult with malaria. 22
Fig 4.2 bar chart representing adult population with malaria. 22
Fig 4.3 graphical representation of adult with malaria. 23
4.3 Analysis of Children Population with Malaria. 23
Table 4.3 children population with malaria. 27
Fig 4.4 pie chart distributions of children with malaria. 28
Fig 4.5 bar chart representation of children with malaria. 29
Fig 4.6 graphical representation of adults with malaria. 30
4.4 Analysis of both Adult and Children population with malaria. 30
Table 4.4 both adult and children population with malaria. 34
Fig 4.7 pie chart distribution of both adults and children with malaria. 35
CHAPTER FIVE. 38
SUMMARY, CONCLUSION AND RECOMMENDATION.. 38
5.1 Summary. 38
5.2 Conclusions. 38
5.3 Recommendations. 39
REFERENCES. 40
CHAPTER ONE
INTRODUCTION
1.0 Introduction
Malaria is one of the most fatal diseases in the world. The symptoms that characterizes malaria may have been observed as far back as the prehistoric period, through the classical era but it was not until the European renaissance period that the name malaria was derived from the Medieval Italian word, mal aria meaning \bad air”, thinking that the foul vapours emanating from the stagnate water and swamps was the cause of fever, a major symptom of the disease.
A brief historical overview of the disease shows that some descriptions of what seemed to be the disease symptoms are given in the historical records of some early civilizations. The Chinese record, Huangdi Neijing describes the disease as repeated fever paroxysm that causes enlargement of the spleen with the potential of generating an epidemic. Ateminisinin combination treatment, a front line drug adopted by the World Health Organization for the treatment of malaria came from a Chinese plant, Qing-hao. This was discovered about 2300 years ago when it was used to treat acute intermittent fever episodes. An account of the disease is also given in the ancient Egyptian medical Papyri. For instance, the ancient Hindus of India ascribe the disease to the bite of a certain insect. Ancient Greeks, including Homer, Empedocles and Hippocrates also referred to the disease as having characteristics of intermittent fever causing enlarged spleens seen in people living in marshy places. It is believed by some researchers that malaria must have been responsible for the fall of the Roman Empire following an archaeological discovery of the presence of malaria in the bones of a Roman child who died 1500 years ago. The cause of malaria was not known from the down of history until later part of the 19th century when Charles Laveran discovered the malaria parasite in human blood in Africa. Few years later, Giovanni Grassi and Raimondo Filetti used the word plasmodium to name the malaria parasite and in 1897, Ronald Ross demonstrated that plasmodium parasite can be transmitted from infected human to mosquitoes.
Malaria forecasting can be an invaluable tool for malaria control and elimination efforts. A public health practitioner developed a simple forecasting method, which led to the first early-warning system of malaria. Forecasting methods for malaria have advanced since that early work, but the utility of more sophisticated models for clinical and public health decision making is not always evident. The accuracy of forecasts is a critical factor in determining the practical value of a forecasting system. The variability in methods is the strength of malaria forecasting, as it allows for tailored approaches to specific settings and contexts. There should also be continued effort to develop new methods although common forecasting accuracy measures are essential as they will help determine the optimal approach with existing and future methods.
When performing forecasting, it is important to understand the assumptions of forecast models and to understand the advantages and disadvantages of each. Forecast accuracy should always be measured on reserved data and common forecasting measures should be used to facilitate comparison between studies. One should explore non-climate predictors, including transmission reducing interventions, as well as different forecasting approaches based upon the same data.
This project work is mainly concerned with the use of first ordinary differential equation in modeling and predictions of population of malaria patients. Many people are interested in a way populations grow and in determining what factors influence their growth. Knowledge of this kind is important in studies of bacteria growth, wildlife management, ecological and harvesting. Some models for population growth are very simple while others can be sophisticated.
Consider a population of bacteria by a simple cell division. We assume that the growth is proportional to the population present. It is natural to think of the population changing from season to season, as such, the state government needs to know what would be the total population of the state, local government in the next n-years ahead for effective and efficient planning in terms of budgeting, resources allocation, social institution, social welfare and good governance.
It is important to explain not only what population modeling is, but also why it is worth doing.
The objective is to provide an approach to formulating and tackling problems in terms of mathematics. The process of building an effective mathematical model take skills, imagination and objective evaluation of mathematical tools, so that all the problem can be obtained with good model which is then translated into useful solution to real problem.
USE THIS MATERIALS AS A GUIDE FOR YOUR PERSONAL RESEARCH WORK (IF PROPERLY CITED)
PAY ₦3,000 HERE TO DOWNLOAD MATERIALS
Account Number: 0709546102
Access Bank: Savings
Account Name: Emmanuel Idorenyin Samuel.