On Stability Equilibrium Analysis of Endemic MalariaIOSR Journals
This paper considers the stability equilibrium of malaria in a varying population. We established the
Disease free equilibrium and the endemic equilibrium and carried out the stability analysis of the Disease free
equilibrium state (the state of complete eradication of malaria from the population).
I appreciate any answer. Thank you. For each of the following descri.pdfamiteksecurity
I appreciate any answer. Thank you. For each of the following description!) formulate a
mathematical model as a system of differential equations. In each case give a suitable
compartment diagram and define any parameters or symbols that you introduce that were not
mentioned as part of the question. Consider a model for the spread of a disease where lifelong
immunity is attained after catching the disease. The susceptibles are continuously vaccinated at a
per-capita rate mu against the disease. Develop differential equations for the number of
susceptibles S(t). the number of infectives I(t). the number vaccinated V(t) and the number
recovered. R(t). assuming all who recovered from the infection become immune for life. The
infectious disease Dengue fever is spread by infected mosquitoes that transmit the disease to
humans when they bite susceptible humans. Similarly, when a susceptible mosquito bites an
infected human the mosquito becomes infected Assume that humans cannot directly infect other
humans nor infected mosquitoes infect other mosquitoes. Let Sm(t) and Im(t) denote the number
of susceptible and infected mosquitoes respectively and let S h(t) and I h(t) denote the member of
susceptible and infected humans. Assume that there are no birth or deaths of humans over the
time-scale of interest but that there is a per-capita birth-rate of mosquitoes 6 and per-capita death
rate of mosquitoes of a. Also, assume that humans are infected for a period of gamma-1 and then
recover with immunity. Mosquitoes do not recover. (Note: typically gamma-1 is about. 2 weeks
for dengue fever).
Solution
The number of Vaccinated people (V(t)) is a simple one.
dV/dt = mu * S(t), where S(t) is the number of susceptable people.
A certain percentage of the people who need to be vaccinated are vaccinated every year, and this
will slow down as less people need to be vaccinated.
Susceptable people are decreasing over time, once you have been vaccinated, become sick, or
recover you are set for life.
S(t) = 1 - V(t) - R(t) - I(t)
The growth of the infective population depends on how infective the disease is, but it is also
dependant on how many people can be infected still. Let\'s say every infective person infects
beta percent of other vulnerable people, and they recover x days later.
dI/dt = [beta * I(t) * S(t)] - I(t-x)
The number of recovered people will follow the number of infective people but unlike infective
people they remain recovered
dR/dt = I(t-x)
All of these equations use a total population size of one, you are modelling the percentage of the
population that has fallen under these conditions. Eventually, the equations should reach these
final end conditions when t = infinity:
S(t) = 0, V(t) + R(t) = 1, and I(t) = 0.
Part two is just using these equations and applying them..
On Stability Equilibrium Analysis of Endemic MalariaIOSR Journals
This paper considers the stability equilibrium of malaria in a varying population. We established the
Disease free equilibrium and the endemic equilibrium and carried out the stability analysis of the Disease free
equilibrium state (the state of complete eradication of malaria from the population).
I appreciate any answer. Thank you. For each of the following descri.pdfamiteksecurity
I appreciate any answer. Thank you. For each of the following description!) formulate a
mathematical model as a system of differential equations. In each case give a suitable
compartment diagram and define any parameters or symbols that you introduce that were not
mentioned as part of the question. Consider a model for the spread of a disease where lifelong
immunity is attained after catching the disease. The susceptibles are continuously vaccinated at a
per-capita rate mu against the disease. Develop differential equations for the number of
susceptibles S(t). the number of infectives I(t). the number vaccinated V(t) and the number
recovered. R(t). assuming all who recovered from the infection become immune for life. The
infectious disease Dengue fever is spread by infected mosquitoes that transmit the disease to
humans when they bite susceptible humans. Similarly, when a susceptible mosquito bites an
infected human the mosquito becomes infected Assume that humans cannot directly infect other
humans nor infected mosquitoes infect other mosquitoes. Let Sm(t) and Im(t) denote the number
of susceptible and infected mosquitoes respectively and let S h(t) and I h(t) denote the member of
susceptible and infected humans. Assume that there are no birth or deaths of humans over the
time-scale of interest but that there is a per-capita birth-rate of mosquitoes 6 and per-capita death
rate of mosquitoes of a. Also, assume that humans are infected for a period of gamma-1 and then
recover with immunity. Mosquitoes do not recover. (Note: typically gamma-1 is about. 2 weeks
for dengue fever).
Solution
The number of Vaccinated people (V(t)) is a simple one.
dV/dt = mu * S(t), where S(t) is the number of susceptable people.
A certain percentage of the people who need to be vaccinated are vaccinated every year, and this
will slow down as less people need to be vaccinated.
Susceptable people are decreasing over time, once you have been vaccinated, become sick, or
recover you are set for life.
S(t) = 1 - V(t) - R(t) - I(t)
The growth of the infective population depends on how infective the disease is, but it is also
dependant on how many people can be infected still. Let\'s say every infective person infects
beta percent of other vulnerable people, and they recover x days later.
dI/dt = [beta * I(t) * S(t)] - I(t-x)
The number of recovered people will follow the number of infective people but unlike infective
people they remain recovered
dR/dt = I(t-x)
All of these equations use a total population size of one, you are modelling the percentage of the
population that has fallen under these conditions. Eventually, the equations should reach these
final end conditions when t = infinity:
S(t) = 0, V(t) + R(t) = 1, and I(t) = 0.
Part two is just using these equations and applying them..
Population ecology is a field of scientific research that examines the dynamics of populations of living organisms within a given environment. It involves the study of various aspects of populations, including their growth, distribution, density, age structure, and the factors that affect these attributes. Key components of population ecology include:
Population Dynamics: Population ecologists study how the size of a population changes over time. This involves examining birth rates (natality), death rates (mortality), immigration, and emigration.
Population Distribution: Understanding how individuals in a population are spatially distributed is essential. Populations can be clumped, evenly dispersed, or randomly distributed in a habitat.
Population Density: This refers to the number of individuals of a species per unit area or volume of habitat. Population density can have significant ecological and environmental implications.
Age Structure: The age distribution within a population can provide insights into its growth potential and reproductive capacity. It can help in predicting future population trends.
Population Growth Models: Population ecologists use mathematical models to describe and predict population growth, such as exponential and logistic growth models.
Limiting Factors: Population growth is limited by various factors, including availability of resources, predation, competition, disease, and environmental conditions. Population ecologists study how these factors influence population dynamics.
Carrying Capacity: The carrying capacity of an environment is the maximum population size that can be sustained by available resources without causing environmental degradation or resource depletion.
Interactions: Populations do not exist in isolation. Interactions with other species, such as predation, competition, and mutualism, are essential considerations in population ecology.
Conservation and Management: Population ecology plays a critical role in the conservation and management of endangered species and ecosystems. It helps in making informed decisions to protect and sustainably manage populations.
Research Methods: Population ecologists employ various field and laboratory techniques, including population censuses, mark and recapture studies, and modeling, to gather data and understand population dynamics.
Measurements of disease.pptx by Dr. Amit gangwaramitgangwar4511
the various measures of morbidity and mortality such as prevalence, incidence, and mortality rate.
mapping and application of measures of diseases.
rate, ratio, and proportion which are the basic tools to measure the disease in a a population.
Relationship between prevalence and incidence rate.
Complex dynamical behaviour of Disease Spread in a PlantHerbivore System with...iosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Population Ecology Lecture by Salman SaeedSalman Saeed
Population Ecology lecture for Biology, Botany, Zoology, Medical and Chemistry Students by Salman Saeed lecturer Botany University College of Management and Sciences Khanewal, Pakistan.
About Author: Salman Saeed
Qualification: M.SC (Botany), M. Phil (Biotechnology) from BZU Multan.
M. Ed & B. Ed from GCU Faisalabad, Pakistan.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Sensitivity Analysis of the Dynamical Spread of Ebola Virus DiseaseAI Publications
The deterministic epidemiological model of (S, E, Iu, Id, R) were studied to gain insight into the dynamical spread of Ebola virus disease. Local and global stability of the model are explored for disease-free and endemic equilibria. Sensitivity analysis is performed on basic reproduction number to check the importance of each parameter on the transmission of Ebola disease. Positivity solution is analyzed for mathematical and epidemiological posedness of the model. Numerical simulation was analyzed by MAPLE 18 software using embedded Runge-Kutta method of order (4) which shows the parameter that has high impact in the spread of the disease spread of Ebola virus disease.
Morbidity (from Latin morbidus: sick, unhealthy) refers to having a disease or a symptom of disease, or to the amount of disease within a population.
Any departure, subjective or objective from a state of physiological well being.
Morbidity also refers to medical problems caused by a treatment.
It is usually represented or estimated using prevalence or incidence.
Population ecology is a field of scientific research that examines the dynamics of populations of living organisms within a given environment. It involves the study of various aspects of populations, including their growth, distribution, density, age structure, and the factors that affect these attributes. Key components of population ecology include:
Population Dynamics: Population ecologists study how the size of a population changes over time. This involves examining birth rates (natality), death rates (mortality), immigration, and emigration.
Population Distribution: Understanding how individuals in a population are spatially distributed is essential. Populations can be clumped, evenly dispersed, or randomly distributed in a habitat.
Population Density: This refers to the number of individuals of a species per unit area or volume of habitat. Population density can have significant ecological and environmental implications.
Age Structure: The age distribution within a population can provide insights into its growth potential and reproductive capacity. It can help in predicting future population trends.
Population Growth Models: Population ecologists use mathematical models to describe and predict population growth, such as exponential and logistic growth models.
Limiting Factors: Population growth is limited by various factors, including availability of resources, predation, competition, disease, and environmental conditions. Population ecologists study how these factors influence population dynamics.
Carrying Capacity: The carrying capacity of an environment is the maximum population size that can be sustained by available resources without causing environmental degradation or resource depletion.
Interactions: Populations do not exist in isolation. Interactions with other species, such as predation, competition, and mutualism, are essential considerations in population ecology.
Conservation and Management: Population ecology plays a critical role in the conservation and management of endangered species and ecosystems. It helps in making informed decisions to protect and sustainably manage populations.
Research Methods: Population ecologists employ various field and laboratory techniques, including population censuses, mark and recapture studies, and modeling, to gather data and understand population dynamics.
Measurements of disease.pptx by Dr. Amit gangwaramitgangwar4511
the various measures of morbidity and mortality such as prevalence, incidence, and mortality rate.
mapping and application of measures of diseases.
rate, ratio, and proportion which are the basic tools to measure the disease in a a population.
Relationship between prevalence and incidence rate.
Complex dynamical behaviour of Disease Spread in a PlantHerbivore System with...iosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Population Ecology Lecture by Salman SaeedSalman Saeed
Population Ecology lecture for Biology, Botany, Zoology, Medical and Chemistry Students by Salman Saeed lecturer Botany University College of Management and Sciences Khanewal, Pakistan.
About Author: Salman Saeed
Qualification: M.SC (Botany), M. Phil (Biotechnology) from BZU Multan.
M. Ed & B. Ed from GCU Faisalabad, Pakistan.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Sensitivity Analysis of the Dynamical Spread of Ebola Virus DiseaseAI Publications
The deterministic epidemiological model of (S, E, Iu, Id, R) were studied to gain insight into the dynamical spread of Ebola virus disease. Local and global stability of the model are explored for disease-free and endemic equilibria. Sensitivity analysis is performed on basic reproduction number to check the importance of each parameter on the transmission of Ebola disease. Positivity solution is analyzed for mathematical and epidemiological posedness of the model. Numerical simulation was analyzed by MAPLE 18 software using embedded Runge-Kutta method of order (4) which shows the parameter that has high impact in the spread of the disease spread of Ebola virus disease.
Morbidity (from Latin morbidus: sick, unhealthy) refers to having a disease or a symptom of disease, or to the amount of disease within a population.
Any departure, subjective or objective from a state of physiological well being.
Morbidity also refers to medical problems caused by a treatment.
It is usually represented or estimated using prevalence or incidence.
Pada Transformasi Laplace bag. kedua, sifat-sifat transformasi laplace yang lebih mendalam dan khusus akan dipelajari. Sifat-sifat ini akan banyak digunakan dalam penerapan metode transformasi laplade dalam menyelesaikan masalah nilai awal dengan persamaan diferensial yang yang berkaitan dengan fungsi-fungsi tangga (piecewise function)
Transformasi Laplace adalah transformasi yang sering digunakan untuk menyelesaikan masalah syarat awal. Metode penyelesaian persamaan diferensial biasa menggunakan transformasi laplace terbukti cukup ampuh digunakan untuk menyelesaikan berbagai masalah nilai awal.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
2. AN EPIDEMIC MODEL FOR INFLUENZA
Population divided into three groups:
S(t) : those Susceptible to catching the disease
I(t) : Those infected with the disease and capable of spreading it
R(t) : Those who have recovered and are immune from the disease
Assumption :
The populations of susceptibles and contagious infectives are large
so that random differences between individuals can be neglected.
Birth and death in this model are ignore
The disease spread by contact.
The latent period are neglected, settingg it equal to zero
People who recover from the disease are then immune.
At any time, the population is homogeneously mixed, i.e we assume
that contagious infectives and susceptibles are always randomly
distributed over the area in which the population lives.
3. COMPARTMENT DIAGRAM
infectives recoveredsusceptibles
recoveredinfected
𝑟𝑎𝑡𝑒 𝑜𝑓
𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑛𝑜.
𝑠𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒𝑠
= −
𝑟𝑎𝑡𝑒
𝑠𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒𝑠
𝑖𝑛𝑓𝑒𝑐𝑡𝑒𝑑
𝑟𝑎𝑡𝑒 𝑜𝑓
𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑛𝑜.
𝑖𝑛𝑓𝑒𝑐𝑡𝑖𝑣𝑒𝑠
=
𝑟𝑎𝑡𝑒
𝑠𝑢𝑠𝑐𝑒𝑝𝑡𝑖𝑏𝑙𝑒𝑠
𝑖𝑛𝑓𝑒𝑐𝑡𝑒𝑑
−
𝑟𝑎𝑡𝑒
𝑖𝑛𝑓𝑒𝑐𝑡𝑖𝑣𝑒𝑠
ℎ𝑎𝑣𝑒 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑒𝑑
𝑟𝑎𝑡𝑒 𝑜𝑓
𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑛𝑜.
𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑒𝑑
=
𝑟𝑎𝑡𝑒
𝑖𝑛𝑓𝑒𝑐𝑡𝑖𝑣𝑒𝑠
ℎ𝑎𝑣𝑒 𝑟𝑒𝑐𝑜𝑣𝑒𝑟𝑒𝑑
𝑑𝑆
𝑑𝑡
= −𝛽𝑆 𝑡 𝐼(𝑡)
𝑑𝐼
𝑑𝑡
= 𝛽𝑆 𝑡 𝐼 𝑡 − 𝛾𝐼(𝑡)
𝑑𝑅
𝑑𝑡
= 𝛾𝐼(𝑡)
• 𝛽 is called the transmission coefficient or infection rate
• 𝛾 is recovery rate or removal rate. (𝛾−1
is average time that an
individual infectious)
𝑆 0 = 𝑠0, 𝐼 0 = 𝑖0, 𝑅 0 = 0
4. THE BASIC REPRODUCTION NUMBER
The basic reproduction number 𝑅0 is defined as the number of new
secondary infections resulting from a single infectious individual placed in a
completely susceptible population, over the time that individual is infectious.
If 𝑅0 < 1 we would expect the disease outbreak to die out (I(t) to decrease)
If 𝑅0 > 1 then it would increase initially
5. SIMULATION
𝑅0 =
𝛽𝑆(0)
𝛾
If we vaccinate a proportion 𝑝 of the population of susceptible then this
means that the basic reproduction number changes to
𝑅 𝑣 =
1 − 𝑝 𝛽𝑆(0)
𝛾
= (1 − 𝑝)𝑅0
To eradicate the disease, 𝑅 𝑣 < 1, then 𝑝 > 1 −
1
𝑅0
.
6. ENDEMIC DISEASE
𝑎 denote the natural per capita death rate of population
𝑏 denote the natural per capita birth rate of population
𝑁 𝑡 = 𝑆 𝑡 + 𝐼 𝑡 + 𝑅 𝑡
𝑑𝑁
𝑑𝑡
= 𝑏 − 𝑎 𝑁
If 𝑏 = 𝑎 the the population remains constant.
I(t) R(t)S(t)
recoveredinfectedbirths
deaths deaths deaths
𝑑𝑆
𝑑𝑡
= 𝑏𝑁 − 𝛽𝑆𝐼 − 𝑎𝑆
𝑑𝐼
𝑑𝑡
= 𝛽𝑆𝐼 − 𝛾𝐼 − 𝑎𝐼
𝑑𝑅
𝑑𝑡
= 𝛾𝐼 − 𝑎𝑅
7. PREDATORS AND PREY
Assumption :
The population are large, sufficiently large to neglect random
differences between individuals.
The effect of pestiside initially are ignored
There are only two populations, the predator and the prey, which
affect the ecosystem.
The prey population grows exponentially in the absence of predator.
PREY
PREDATORS
Natural births
births
Natural deaths
Deaths from predators
Natural deaths
8. SYSTEM OF EQUATION
Let 𝛽1 = 𝑏1 − 𝑎1, −𝛼2 = 𝑏2 − 𝑎2, dan 𝑐2 = 𝑓𝑐1𝑑𝑋
𝑑𝑡
= 𝑏1 𝑋 − 𝑎1 𝑋 − 𝑐1 𝑋𝑌
𝑑𝑌
𝑑𝑡
= 𝑏2 𝑌 + 𝑓𝑐1 𝑋𝑌 − 𝑎2 𝑌
𝑑𝑋
𝑑𝑡
= 𝛽1 𝑋 − 𝑐1 𝑋𝑌
𝑑𝑌
𝑑𝑡
= 𝑐2 𝑋𝑌 − 𝛼2 𝑌
𝑐1 dan 𝑐2 are known as interaction parameters
The systems of equations is called the Lotka-Voltera predator-prey systems
10. COMPETING SPECIES
Assumption :
The population to be sufficiently large so that random fluctuations
can be ignored without consequences
The two species model reflects the ecosystem sufficiently accurately.
Each population grows exponentially in the absence of the other
competitor
Species X
Species Y
births
births deaths
deaths
𝑑𝑋
𝑑𝑡
= 𝛽1 𝑋 − 𝑐1 𝑋𝑌
𝑑𝑌
𝑑𝑡
= 𝛽2 𝑌 − 𝑐2 𝑋𝑌
𝛽1 and 𝛽2 where per capita growth rates which
incorporating deaths (independent of the other species)
𝑐1 and 𝑐2 are the interaction parameters
Gause’s Equations
12. MODEL OF BATTLE
Assumption :
The number of soldiers to be sufficiently large so that we can neglect
random differences between them
There are no reinforcements and no operational losses (i.e due to
desertion or disease)
Red soldiers
Blue soldiers
deaths
deaths
For aimed fire the rate of soldiers wounded is proportional to the number of
enemy soldiers only
For random fire the rate at which soldiers are wounded is proportional to
both numbers of soldiers
𝑑𝑅
𝑑𝑡
= −𝑎1 𝐵
𝑑𝐵
𝑑𝑡
= −𝑎2 𝑅
𝑎1 and 𝑎2 are measure the effectiveness of the
blue army and red army respectively (called
attrition coefficients)