I appreciate any answer. Thank you. For each of the following descri.pdf
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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..
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![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.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
There are N individuals in a population, some of whomhave a certai.pdf
There are N individuals in a population, some of whom
have a certain infection that spreads as follows. Contacts between two members of this
population occur in accordance with a Poisson process having rate λ. When a contact occurs
it is equally likely to involve any of the (N choose 2)
pairs of individuals in the population. If a contact
involves an infected and a noninfected individual, then the noninfected individual becomes
infected with probability p. Once infected, and individual remains infected throughout. Let
{X (t)} denote the number of infected members of the population at time t.
i)
{X (t)} is a pure birth process. Find the birth rates λi for each state i, and write down
the Q matrix.
ii) Starting with a single infected individual, ï¬nd an expression for the expected time
until all individuals are infected.
iii) Suppose now that each infected individual loses his or her infection independently at
rate µ. (Or you can imagine that each individual once becoming infected, remains so for an
exponential distributed length of time, independently of other infected members.) Assume
that individuals do not gain immunity. Is this a birth and death chain, and if so, what are
the birth and death rates?
Solution
www.tdx.cat/bitstream/10803/31943/2/tasc.pdf.txt?.
This project will investigate the SIR model and use numeric methods t.pdf
This project will investigate the SIR model and use numeric methods to find solutions to the
system of coupled, non-linear differential equations. We will work through the derivation of the
model and some assumptions. You will need to use technology, in the form of a spreadsheet
(Excel/Google Sheets) or computer code (C, C++, Python, Java, etc) to obtain approximate
numeric solutions. Some Background The SIR model is a very useful compartmental model to
help understand the spread of disease through a population during some given time. The
mathematical model has three compartments: Susceptible members of the population (S),
Infected members of the population (I), and Recovered-or Removed-members of the population
(R). As with any mathematical model we will make some assumptions that we should be mindful
of when using this in a "real-world" context. Lets begin with some initial information. 1. We will
assume, for simplicity, that the number of susceptible individuals in a population at some time t
can be given by the function S(t), and further that each of the infected and removed individuals
will be given by functions I(t) and R(t) respectively. 2. We can also assume that for a population
N,S(t)+I(t)+R(t)=N. Explain why this is a reasonable assumption. 3. We will also assume that the
population we are studying is closed, that is we never add to the susceptible members and that
once removed, you will not be susceptible. Explain why this simplification may not reflect a
real-world scenario. Deriving some differential equations: First, lets look at how the susceptible
population might be changing. We will fix a model parameter, called , that will be the number of
daily contacts each infected person has with a susceptible person resulting in disease infection.
We can write it like this: dtdS=NS(t)I(t) Explain why this is reasonable. Why is there an I(t) ?
Why do we have NS(t) ? Why is it negative?
For reasons of convenience, instead of considering the change in numbers of each compartment
we will look at the change in proportion of each compartment, so we can say that:
s(t)=NS(t)i(t)=NI(t)r(t)=NR(t) We will get the following changes: s(t)+i(t)+r(t)=1 and the
differential equation for change in proportion of susceptible will be: dtds=s(t)i(t) Now consider
the change in removed proportion. For this we will need to introduce another parameter, , which
you can think of as a fixed proportion of the infected members who will be recovered/removed
each day or as the recovery rate: 1/ (days to recover). So we get: dtdr=i(t) We know that
s(t)+i(t)+r(t)=1, so it follows that if we take the derivative with respect to t we get:
dtd(s(t)+i(t)+r(t))=dtd(1) Write the resulting equation below: Use the equation you found to
solve for dtdi and substitute to find dtdi in terms of the model parameters and , and the functions
s(t),i(t),r(t). Write it below:
Together these three equations give a system of coupled, non-linear differential equations. Fill in
the missing e.
Modelo matemático: el confinamiento disminuye significativamente la velocidad...
Los investigadores Mikel Casares (UPNA) y Hashmat Khan (Universidad de Carleton, Canadá) estudian la evolución de la epidemia y las consecuencias de las medidas de aislamiento para la población española
Recommended
There are N individuals in a population, some of whomhave a certai.pdf
There are N individuals in a population, some of whom
have a certain infection that spreads as follows. Contacts between two members of this
population occur in accordance with a Poisson process having rate λ. When a contact occurs
it is equally likely to involve any of the (N choose 2)
pairs of individuals in the population. If a contact
involves an infected and a noninfected individual, then the noninfected individual becomes
infected with probability p. Once infected, and individual remains infected throughout. Let
{X (t)} denote the number of infected members of the population at time t.
i)
{X (t)} is a pure birth process. Find the birth rates λi for each state i, and write down
the Q matrix.
ii) Starting with a single infected individual, ï¬nd an expression for the expected time
until all individuals are infected.
iii) Suppose now that each infected individual loses his or her infection independently at
rate µ. (Or you can imagine that each individual once becoming infected, remains so for an
exponential distributed length of time, independently of other infected members.) Assume
that individuals do not gain immunity. Is this a birth and death chain, and if so, what are
the birth and death rates?
Solution
www.tdx.cat/bitstream/10803/31943/2/tasc.pdf.txt?.
This project will investigate the SIR model and use numeric methods t.pdf
This project will investigate the SIR model and use numeric methods to find solutions to the
system of coupled, non-linear differential equations. We will work through the derivation of the
model and some assumptions. You will need to use technology, in the form of a spreadsheet
(Excel/Google Sheets) or computer code (C, C++, Python, Java, etc) to obtain approximate
numeric solutions. Some Background The SIR model is a very useful compartmental model to
help understand the spread of disease through a population during some given time. The
mathematical model has three compartments: Susceptible members of the population (S),
Infected members of the population (I), and Recovered-or Removed-members of the population
(R). As with any mathematical model we will make some assumptions that we should be mindful
of when using this in a "real-world" context. Lets begin with some initial information. 1. We will
assume, for simplicity, that the number of susceptible individuals in a population at some time t
can be given by the function S(t), and further that each of the infected and removed individuals
will be given by functions I(t) and R(t) respectively. 2. We can also assume that for a population
N,S(t)+I(t)+R(t)=N. Explain why this is a reasonable assumption. 3. We will also assume that the
population we are studying is closed, that is we never add to the susceptible members and that
once removed, you will not be susceptible. Explain why this simplification may not reflect a
real-world scenario. Deriving some differential equations: First, lets look at how the susceptible
population might be changing. We will fix a model parameter, called , that will be the number of
daily contacts each infected person has with a susceptible person resulting in disease infection.
We can write it like this: dtdS=NS(t)I(t) Explain why this is reasonable. Why is there an I(t) ?
Why do we have NS(t) ? Why is it negative?
For reasons of convenience, instead of considering the change in numbers of each compartment
we will look at the change in proportion of each compartment, so we can say that:
s(t)=NS(t)i(t)=NI(t)r(t)=NR(t) We will get the following changes: s(t)+i(t)+r(t)=1 and the
differential equation for change in proportion of susceptible will be: dtds=s(t)i(t) Now consider
the change in removed proportion. For this we will need to introduce another parameter, , which
you can think of as a fixed proportion of the infected members who will be recovered/removed
each day or as the recovery rate: 1/ (days to recover). So we get: dtdr=i(t) We know that
s(t)+i(t)+r(t)=1, so it follows that if we take the derivative with respect to t we get:
dtd(s(t)+i(t)+r(t))=dtd(1) Write the resulting equation below: Use the equation you found to
solve for dtdi and substitute to find dtdi in terms of the model parameters and , and the functions
s(t),i(t),r(t). Write it below:
Together these three equations give a system of coupled, non-linear differential equations. Fill in
the missing e.
Modelo matemático: el confinamiento disminuye significativamente la velocidad...
Los investigadores Mikel Casares (UPNA) y Hashmat Khan (Universidad de Carleton, Canadá) estudian la evolución de la epidemia y las consecuencias de las medidas de aislamiento para la población española
Mathematics Model Development Deployment of Dengue Fever Diseases by Involve ...
Dengue virus is one of virus that cause deadly disease
was dengue fever. This virus was transmitted through bite of
Aedes aegypti female mosquitoes that gain virus infected by
taking food from infected human blood, then mosquitoes
transmited pathogen to susceptible humans. Suppressed the
spread and growth of dengue fever was important to avoid
and prevent the increase of dengue virus sufferer and
casualties. This problem can be solved with studied
important factors that affected the spread and equity of
disease by sensitivity index. The purpose of this research
were to modify mathematical model the spread of dengue
fever be SEIRS-ASEI type, to determine of equilibrium
point, to determined of basic reproduction number, stability
analysis of equilibrium point, calculated sensitivity index, to
analyze sensitivity, and to simulate numerical on
modification model. Analysis of model obtained disease free
equilibrium (DFE) point and endemic equilibrium point. The
numerical simulation result had showed that DFE, stable if
the basic reproduction number is less than one and endemic
equilibrium point was stable if the basic reproduction
number is more than one.
Simulation rules are almost the same to what I coded using Python bu.pdf
Simulation rules are almost the same to what I coded using Python but with one interesting
addition a partial immunity (see details below). The main difference between the Python version
and R-version of the code would be the use of vectorization. You should use as little as possible
of loops. In fact, there should be just one loop for the days-count. If anyone tries to create the
exact copy of my Python code but in R, the grade will be quite low. Below are rules that should
help you building a simulation model:
1. We assume that there are N_population citizens in a population. N_population is an input
parameter. During development and testing you can have it small, but your code should be able
to handle a reasonably large value for population size.
2. Every citizen has a health state healthy, sick, dead (or you can code it as 0, 1, 2). When we
start, all citizens are alive and healthy
3. To start the pandemic, you randomly mark a small number of citizens as sick there can be a
parameter for the number of initially infected citizens. You can start with 1 or 2 initial sick cases.
4. One iteration is one day. During the day, every citizen can meet a random number (say
between 0 and 20 inclusive) of randomly selected citizens. You dont really need to control all
citizens as we are interested in meetings of sick people only. We dont care how many healthy
people meet each other.
5. Every sick citizen can stay sick and infectious for 10 days, hence you should have some
counter for each sick citizen. After 10 days a sick citizen becomes healthy and stops spreading
the virus
6. Obviously, dead citizens cannot become sick, they dont meet anyone and, as a result, cannot
infect anyone.
7. During the day every sick citizen has a probability to die (mortality rate) from the disease with
probability 0.5% (quite low probability).
8. If a sick citizen does not die, then they can meet other people as per the rule 4 above, and if
they meet a healthy person, that person might become sick too and start infecting other people
starting from the next day (that is an infection day is day 0 of their sickness). The probability for
a citizen of becoming sick (infection rate) after a contact is 30% (this is quite high).
9. (New rule!) After surviving the infection and getting healthy, the person becomes immune.
This is a partial immunity. It does not make the person invincible but reduces the chance of
infection in future meetings with sick people. Take the immunity coefficient as 0.1. That is, a
probability of being infected for immune person is ten times lower than for the person without an
immunity. The infection rate for the immune person is the original infection rate multiplied by
the immunity coefficient, e.g. (0.3*0.1).
For the test, you can set immunity coefficient equal 1, which means no benefits of immunity and
the result should be the same as in Python example I presented.
10. You should run this simulation for a number of days (iterations) and store each da.
Interacting Population Models
We describe some interacting population model, such as epidemic model, predator prey, battle model, and civilization model
The local and global stability of the disease free equilibrium in a co infect...
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.
Ijetr021115
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
Ijetr021115
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 Disease
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.
Ijsea04031004
Ebola hemorrhagic fever is a disease caused by one of five different Ebola viruses. Four of the strains can cause severe illness in humans and animals. Humans can be infected by other humans if they come in contact with body fluids from an infected person or contaminated objects from infected persons. Humans can also be exposed to the virus, for example, by butchering infected animals. Deadly human Ebola outbreaks have been confirmed in the following countries: Democratic Republic of the Congo (DRC), Gabon, South Sudan, Ivory Coast, Uganda, and Republic of the Congo (ROC), Guinea and Liberia. In this sense, it is of vital importance to analysis the history data and predicts its propagation. More specifically, a model based k-means algorithm to determine the optimal locations of virus delivery is constructed and tested Using Mab-lab programming. By experiment, we find that our model can work well and lead to a relatively accurate prediction, which can help the government forecast the epidemic spread more efficiently
LastWeekPresentation
DISEASE EFFECTS ON REPRODUCTION CAN CAUSE POPULATION CYCLES IN SEASONAL ENVIRONMENTS
Modelisation of Ebola Hemoragic Fever propagation in a modern city
The study of epidemic disease has always been a topic where biological issues mix with social ones.
The aim of this presentation was to modelize in Python language the propagation of Ebola Hemoragic Fever in a modern city thus using SIR model based on Ordinary Differential Equations system and also to produce an amazing Cellular Automaton.
Background- Suppose we want to model the spread of an infectious disea.pdf
Background: Suppose we want to model the spread of an infectious disease through a population.
In the previous assignment, we did this with a discrete model. Here, we'll formulate things as a
continuous model. We'll keep track of the numbers of individuals susceptible to the disease S ( t
) , infected by the disease I ( t ) , and recovered from the disease R ( t ) at time t (measured in
days). Infected individuals have a chance to transmit the disease upon contact with a susceptible
individual at a rate > 0 . Infected individuals also recover from the infection at a rate > 0 . We
assume that all N individuals in the population produce offspring at a rate b > 0 and these
newborns add to the pool of susceptible individuals. Finally, individuals in all disease states can
die due to natural causes at a rate equal to the birth rate b . This description can be captured by
the following system of equations: d t d S = b N S I b S d t d I = S I I b I d t d R = I b R Problems
Problem 1 [3 points]: Given that S , I , and R are measured in number of people and t is
measured in days, what must be the units of b , , and ? Justify your answer. Problem 2 [3 points]:
Let N ( t ) = S ( t ) + I ( t ) + R ( t ) be the total population size at any point in time t . If the total
population size remains constant for all time (i.e., N ( t ) = N for all t , where N is a constant) we
say we have a "closed population". Show that the population in our model is closed. (Hint: Think
about the rate of change d t d N of the population size and what must be true for the population
to be constant.).
Consider a disease that is spreading throughout the United States- suc (1).pdf
Consider a disease that is spreading throughout the United States, such as COVID-19. The
Centers for Disease Control and Prevention is interested in knowing and experimenting with a
model for this new disease before it actually becomes a real epidemic. Let us consider in a
remote, closed village community, the village population is divided into three categories:
susceptible (S), infected (1), and removed ( R ) . We make the following assumptions for our
model: - No one enters or leaves the community, and there is no contact outside the community.
- Each person is susceptible S (able to catch this new flu); infected I (currently has the flu and
can spread the flu); or removed R (already had the flu and will not get it again, which includes
death). - Initially, every person is either S or I. - Once someone gets the flu this year, they cannot
get the flu again. - The average length of the disease is 5/3 weeks ( 1 and 2/3 weeks), over which
time the person is deemed infected and can spread the disease. - Our time period for the model
will be per week. The model we will consider is called the SIR model. Let's assume the
following definitions for our variables: S ( t ) = number in the population susceptible after period
t I ( t ) = number infected after period t R ( t ) = number removed after period t Let's start our
modeling process with R ( t ) . Our assumption for the length of time someone has the flu is 5/3
weeks. Thus, 3/5 or 60% of the infected people will be removed each week: R ( t + 1 ) = R ( t ) +
0. 6 ( t ) The value 0.6 is called the removal rate per week. It represents the proportion of the
infected persons who are removed from infection each week. I(t) will have terms that both
increase and decrease its amount over time. It is decreased by the number of people removed
each week 0.6 ( t ) . It is increased by the number of susceptible people who come into contact
with infected people and catch the disease: k S ( t ) I ( t ) . We define k as the rate at which the
disease is spread, or the transmission coefficient. We assume, initially, this rate k is a constant
value that can be found from the initial conditions. Assume we have a population of 1000
residing in the village. Initially, the village nurse found 5 cases 0 infection were reported to the
village clinic: U ( 0 ) = 5 and S ( 0 ) = 995 After one week, the total number infected with the
virus is 9 . We compute k as follows: I ( 0 ) = 5 I ( 1 ) = I ( 0 ) 0.6 1 ( 0 ) + k 1 ( 0 ) s ( 0 ) 9 = 5
0.6 5 + k 5 995 k = 0.001407 Let's consider S ( t ) . This number is decreased only by the number
that becomes infected. S ( t + 1 ) = S ( t ) k s ( t ) 1 ( t ) Our coupled model is thus R ( t + I ) = R (
t ) + 0. 6 I ( t ) I ( t + 1 ) = I ( t ) 0. 6 I ( t ) + 0.001407 I ( t ) S ( t ) S ( t + 1 ) = S ( t ) 0.001407 S
( t ) I ( t ) I ( 0 ) = 5 S ( 0 ) = 995 R ( 0 ) = 0 The SIR model can be solved iteratively and viewed
graphically. Let's iterate the solution and obtain the graph to observe the .
Dynamics and Control of Infectious Diseases (2007) - Alexander Glaser
See also:
- https://food4innovations.blog/2020/03/26/montecarlo-simulaties-tonen-aan-wat-de-onzekerheid-is-en-dat-we-minimaal-1600-maar-misschien-wel-2000-2500-ic-plaatsen-nodig-hebben/
I have a square which consists of i by i equally sized boxes for a t.pdf
I have a square which consists of i by i equally sized boxes for a total of N=i*i boxes. In how
many ways could I randomly throw n objects or particles into these N boxes? (where n < N and
only 1 object fits in each box)
Solution
C(N,n) =
N!/n!(N-n)!.
I have a test tomorrow for statistics and need to know the basic inf.pdf
I have a test tomorrow for statistics and need to know the basic information of how to work these
kinds of problems and anything else that will help me.
4. ANOVA
Solution
Anova is used to test more than 2 means in the different sample group.
For example,
Ho:1=2=3 (null hypothesis)
Ha:at least one mean is not equal. (alternative hypothesis).
More Related Content
Similar to I appreciate any answer. Thank you. For each of the following descri.pdf
Mathematics Model Development Deployment of Dengue Fever Diseases by Involve ...
Dengue virus is one of virus that cause deadly disease
was dengue fever. This virus was transmitted through bite of
Aedes aegypti female mosquitoes that gain virus infected by
taking food from infected human blood, then mosquitoes
transmited pathogen to susceptible humans. Suppressed the
spread and growth of dengue fever was important to avoid
and prevent the increase of dengue virus sufferer and
casualties. This problem can be solved with studied
important factors that affected the spread and equity of
disease by sensitivity index. The purpose of this research
were to modify mathematical model the spread of dengue
fever be SEIRS-ASEI type, to determine of equilibrium
point, to determined of basic reproduction number, stability
analysis of equilibrium point, calculated sensitivity index, to
analyze sensitivity, and to simulate numerical on
modification model. Analysis of model obtained disease free
equilibrium (DFE) point and endemic equilibrium point. The
numerical simulation result had showed that DFE, stable if
the basic reproduction number is less than one and endemic
equilibrium point was stable if the basic reproduction
number is more than one.
Simulation rules are almost the same to what I coded using Python bu.pdf
Simulation rules are almost the same to what I coded using Python but with one interesting
addition a partial immunity (see details below). The main difference between the Python version
and R-version of the code would be the use of vectorization. You should use as little as possible
of loops. In fact, there should be just one loop for the days-count. If anyone tries to create the
exact copy of my Python code but in R, the grade will be quite low. Below are rules that should
help you building a simulation model:
1. We assume that there are N_population citizens in a population. N_population is an input
parameter. During development and testing you can have it small, but your code should be able
to handle a reasonably large value for population size.
2. Every citizen has a health state healthy, sick, dead (or you can code it as 0, 1, 2). When we
start, all citizens are alive and healthy
3. To start the pandemic, you randomly mark a small number of citizens as sick there can be a
parameter for the number of initially infected citizens. You can start with 1 or 2 initial sick cases.
4. One iteration is one day. During the day, every citizen can meet a random number (say
between 0 and 20 inclusive) of randomly selected citizens. You dont really need to control all
citizens as we are interested in meetings of sick people only. We dont care how many healthy
people meet each other.
5. Every sick citizen can stay sick and infectious for 10 days, hence you should have some
counter for each sick citizen. After 10 days a sick citizen becomes healthy and stops spreading
the virus
6. Obviously, dead citizens cannot become sick, they dont meet anyone and, as a result, cannot
infect anyone.
7. During the day every sick citizen has a probability to die (mortality rate) from the disease with
probability 0.5% (quite low probability).
8. If a sick citizen does not die, then they can meet other people as per the rule 4 above, and if
they meet a healthy person, that person might become sick too and start infecting other people
starting from the next day (that is an infection day is day 0 of their sickness). The probability for
a citizen of becoming sick (infection rate) after a contact is 30% (this is quite high).
9. (New rule!) After surviving the infection and getting healthy, the person becomes immune.
This is a partial immunity. It does not make the person invincible but reduces the chance of
infection in future meetings with sick people. Take the immunity coefficient as 0.1. That is, a
probability of being infected for immune person is ten times lower than for the person without an
immunity. The infection rate for the immune person is the original infection rate multiplied by
the immunity coefficient, e.g. (0.3*0.1).
For the test, you can set immunity coefficient equal 1, which means no benefits of immunity and
the result should be the same as in Python example I presented.
10. You should run this simulation for a number of days (iterations) and store each da.
Interacting Population Models
We describe some interacting population model, such as epidemic model, predator prey, battle model, and civilization model
The local and global stability of the disease free equilibrium in a co infect...
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.
Ijetr021115
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
Ijetr021115
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 Disease
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.
Ijsea04031004
Ebola hemorrhagic fever is a disease caused by one of five different Ebola viruses. Four of the strains can cause severe illness in humans and animals. Humans can be infected by other humans if they come in contact with body fluids from an infected person or contaminated objects from infected persons. Humans can also be exposed to the virus, for example, by butchering infected animals. Deadly human Ebola outbreaks have been confirmed in the following countries: Democratic Republic of the Congo (DRC), Gabon, South Sudan, Ivory Coast, Uganda, and Republic of the Congo (ROC), Guinea and Liberia. In this sense, it is of vital importance to analysis the history data and predicts its propagation. More specifically, a model based k-means algorithm to determine the optimal locations of virus delivery is constructed and tested Using Mab-lab programming. By experiment, we find that our model can work well and lead to a relatively accurate prediction, which can help the government forecast the epidemic spread more efficiently
LastWeekPresentation
DISEASE EFFECTS ON REPRODUCTION CAN CAUSE POPULATION CYCLES IN SEASONAL ENVIRONMENTS
Modelisation of Ebola Hemoragic Fever propagation in a modern city
The study of epidemic disease has always been a topic where biological issues mix with social ones.
The aim of this presentation was to modelize in Python language the propagation of Ebola Hemoragic Fever in a modern city thus using SIR model based on Ordinary Differential Equations system and also to produce an amazing Cellular Automaton.
Background- Suppose we want to model the spread of an infectious disea.pdf
Background: Suppose we want to model the spread of an infectious disease through a population.
In the previous assignment, we did this with a discrete model. Here, we'll formulate things as a
continuous model. We'll keep track of the numbers of individuals susceptible to the disease S ( t
) , infected by the disease I ( t ) , and recovered from the disease R ( t ) at time t (measured in
days). Infected individuals have a chance to transmit the disease upon contact with a susceptible
individual at a rate > 0 . Infected individuals also recover from the infection at a rate > 0 . We
assume that all N individuals in the population produce offspring at a rate b > 0 and these
newborns add to the pool of susceptible individuals. Finally, individuals in all disease states can
die due to natural causes at a rate equal to the birth rate b . This description can be captured by
the following system of equations: d t d S = b N S I b S d t d I = S I I b I d t d R = I b R Problems
Problem 1 [3 points]: Given that S , I , and R are measured in number of people and t is
measured in days, what must be the units of b , , and ? Justify your answer. Problem 2 [3 points]:
Let N ( t ) = S ( t ) + I ( t ) + R ( t ) be the total population size at any point in time t . If the total
population size remains constant for all time (i.e., N ( t ) = N for all t , where N is a constant) we
say we have a "closed population". Show that the population in our model is closed. (Hint: Think
about the rate of change d t d N of the population size and what must be true for the population
to be constant.).
Consider a disease that is spreading throughout the United States- suc (1).pdf
Consider a disease that is spreading throughout the United States, such as COVID-19. The
Centers for Disease Control and Prevention is interested in knowing and experimenting with a
model for this new disease before it actually becomes a real epidemic. Let us consider in a
remote, closed village community, the village population is divided into three categories:
susceptible (S), infected (1), and removed ( R ) . We make the following assumptions for our
model: - No one enters or leaves the community, and there is no contact outside the community.
- Each person is susceptible S (able to catch this new flu); infected I (currently has the flu and
can spread the flu); or removed R (already had the flu and will not get it again, which includes
death). - Initially, every person is either S or I. - Once someone gets the flu this year, they cannot
get the flu again. - The average length of the disease is 5/3 weeks ( 1 and 2/3 weeks), over which
time the person is deemed infected and can spread the disease. - Our time period for the model
will be per week. The model we will consider is called the SIR model. Let's assume the
following definitions for our variables: S ( t ) = number in the population susceptible after period
t I ( t ) = number infected after period t R ( t ) = number removed after period t Let's start our
modeling process with R ( t ) . Our assumption for the length of time someone has the flu is 5/3
weeks. Thus, 3/5 or 60% of the infected people will be removed each week: R ( t + 1 ) = R ( t ) +
0. 6 ( t ) The value 0.6 is called the removal rate per week. It represents the proportion of the
infected persons who are removed from infection each week. I(t) will have terms that both
increase and decrease its amount over time. It is decreased by the number of people removed
each week 0.6 ( t ) . It is increased by the number of susceptible people who come into contact
with infected people and catch the disease: k S ( t ) I ( t ) . We define k as the rate at which the
disease is spread, or the transmission coefficient. We assume, initially, this rate k is a constant
value that can be found from the initial conditions. Assume we have a population of 1000
residing in the village. Initially, the village nurse found 5 cases 0 infection were reported to the
village clinic: U ( 0 ) = 5 and S ( 0 ) = 995 After one week, the total number infected with the
virus is 9 . We compute k as follows: I ( 0 ) = 5 I ( 1 ) = I ( 0 ) 0.6 1 ( 0 ) + k 1 ( 0 ) s ( 0 ) 9 = 5
0.6 5 + k 5 995 k = 0.001407 Let's consider S ( t ) . This number is decreased only by the number
that becomes infected. S ( t + 1 ) = S ( t ) k s ( t ) 1 ( t ) Our coupled model is thus R ( t + I ) = R (
t ) + 0. 6 I ( t ) I ( t + 1 ) = I ( t ) 0. 6 I ( t ) + 0.001407 I ( t ) S ( t ) S ( t + 1 ) = S ( t ) 0.001407 S
( t ) I ( t ) I ( 0 ) = 5 S ( 0 ) = 995 R ( 0 ) = 0 The SIR model can be solved iteratively and viewed
graphically. Let's iterate the solution and obtain the graph to observe the .
Dynamics and Control of Infectious Diseases (2007) - Alexander Glaser
See also:
- https://food4innovations.blog/2020/03/26/montecarlo-simulaties-tonen-aan-wat-de-onzekerheid-is-en-dat-we-minimaal-1600-maar-misschien-wel-2000-2500-ic-plaatsen-nodig-hebben/
Similar to I appreciate any answer. Thank you. For each of the following descri.pdf (20)
Mathematics Model Development Deployment of Dengue Fever Diseases by Involve ...
Mathematics Model Development Deployment of Dengue Fever Diseases by Involve ...
Simulation rules are almost the same to what I coded using Python bu.pdf
Simulation rules are almost the same to what I coded using Python bu.pdf
The local and global stability of the disease free equilibrium in a co infect...
The local and global stability of the disease free equilibrium in a co infect...
GraphDiseaseSpreadModels-Threshold&FirefighterCapeTown6-10-07
GraphDiseaseSpreadModels-Threshold&FirefighterCapeTown6-10-07
Sensitivity Analysis of the Dynamical Spread of Ebola Virus Disease
Sensitivity Analysis of the Dynamical Spread of Ebola Virus Disease
Modelisation of Ebola Hemoragic Fever propagation in a modern city
Modelisation of Ebola Hemoragic Fever propagation in a modern city
Background- Suppose we want to model the spread of an infectious disea.pdf
Background- Suppose we want to model the spread of an infectious disea.pdf
Consider a disease that is spreading throughout the United States- suc (1).pdf
Consider a disease that is spreading throughout the United States- suc (1).pdf
Dynamics and Control of Infectious Diseases (2007) - Alexander Glaser
Dynamics and Control of Infectious Diseases (2007) - Alexander Glaser
More from amiteksecurity
I have a square which consists of i by i equally sized boxes for a t.pdf
I have a square which consists of i by i equally sized boxes for a total of N=i*i boxes. In how
many ways could I randomly throw n objects or particles into these N boxes? (where n < N and
only 1 object fits in each box)
Solution
C(N,n) =
N!/n!(N-n)!.
I have a test tomorrow for statistics and need to know the basic inf.pdf
I have a test tomorrow for statistics and need to know the basic information of how to work these
kinds of problems and anything else that will help me.
4. ANOVA
Solution
Anova is used to test more than 2 means in the different sample group.
For example,
Ho:1=2=3 (null hypothesis)
Ha:at least one mean is not equal. (alternative hypothesis).
I have a poker hand containing four of a kind. How many possible pok.pdf
I have a poker hand containing four of a kind. How many possible poker hands can be dealt?
Solution
13 types of cards possible hand needs 4 different types: 13*12*11*10 = answer;
each type you select means one less type to choose from, so that is why the numbers are 13, 12,
11, 10. I hope this helps!! :).
i have a question about mode,median, and mean i dont really unstand .pdf
i have a question about mode,median, and mean i dont really unstand them and i have a math test
torrow please help. i\'m tring to study but i dont understand it so how can i study
Solution
Mode is the number that appears the most...median is the number that is in the
middle and mean is all the numbers added up and divided by the number of numbers there are.
I have a homework problem that I do not know how to begin. I must ev.pdf
I have a homework problem that I do not know how to begin. I must evaluate integral (4/3x+7)
dx. I think u= 3x+7 but beyond that I am just lost. Would du = 4x? Please help.
Solution
yeah, u = 3x + 7 du = 3 dx then substitute it, int 4 dx / ( 3x + 7) = int 4 du / 3 u =
4/3 int du/u = 4/3 ln u = 4/3 ln ( 3x + 7) + C.
I have a couple questions for Diff Eq that need to be done on maple .pdf
I have a couple questions for Diff Eq that need to be done on maple and I have no clue on how to
use the program. Can you teach me the basics?
Solution
Maples are variously classified in a family of their own, the Aceraceae, or together
with the Hippocastanaceae included in the family Sapindaceae. Modern classifications, including
the Angiosperm Phylogeny Group system, favour inclusion in Sapindaceae. The type species of
the genus is Acer pseudoplatanus (Sycamore maple).[3] There are approximately 129 species,
most of which are native to Asia,[4] with a number also appearing in Europe, northern Africa,
and North America. Only one species, the poorly studied Acer laurinum, is native to the
Southern Hemisphere.[5] Fifty-four species of maples meet the International Union for
Conservation of Nature criteria for being under threat of extinction in their native habitat.[5] The
word Acer derives from a Latin word meaning \"sharp\" (compare \"acerbic\"), referring to the
characteristic points on maple leaves. It was first applied to the genus by the French botanist
Joseph Pitton de Tournefort in 1700. The earliest known fossil maple is Acer alaskense, from the
Latest Paleocene of Alaska. Horticulture A red maple tree between pine trees. Acer
palmatum (Japanese maple) has over 1,000 cultivars. This cultivar is A. palmatum \'Sango
kaku\', sometimes called \"coralbark maple\". Maples are planted as ornamental trees by
homeowners, businesses and municipalities. Acer platanoides (Norway maple) is especially
popular as it is fast-growing and extremely cold-resistant, though it is also an invasive species in
some regions. Other maples, especially smaller or more unusual species, are popular as specimen
trees.[3] [edit]Cultivars Numerous maple cultivars which have been selected for particular
characteristics can be propagated only by asexual reproduction such as cuttings, tissue culture,
budding or grafting. Acer palmatum (Japanese maple) alone has over 1,000 cultivars, most
selected in Japan, and many of them no longer propagated or not in cultivation in the Western
world. Some delicate cultivars are usually grown in pots and rarely reach heights of more than
50–100 cm. [edit]Bonsai Bonsai \"Roter Fächerahorn\" Maples are a popular choice for the art
of bonsai. Acer palmatum (Japanese maple), Acer buergerianum (Trident maple), Acer ginnala
(Amur maple), Acer campestre (Field maple) and Montpellier maple (A. monspessulanum) are
popular choices and respond well to techniques that encourage leaf reduction and ramification,
but most species can be used.[3][10] [edit]Collections Acer griseum is widely grown for its
decorative bark Maple collections, sometimes called aceretums, occupy space in many gardens
and arboreta around the world including the \"five great W\'s\" in England: Wakehurst Place
Garden, Westonbirt Arboretum, Windsor Great Park, Winkworth Arboretum and Wisley Garden.
In the United States, the aceretum at the Harvard-owned Arnold Arboretum in Bos.
I have a boss, Ralph Poore, who is the consummate Dr. Jekyll and Mr..pdf
I have a boss, Ralph Poore, who is the consummate Dr. Jekyll and Mr. Hyde. On some days,
Poore can be the nicest and
Solution
Roary`s style of Leadership is very harsh, it only demotivates the employees. We should listen to
others also. As we are human beings we can make errors, we cannot be always right. Poore shold
understand that others are also people. We should respect others. The thing which can be handle
calmly cannot handle with anger. Anger only makes the things difficult.
Roary leadership style is not good, it only creates fear in the mind of the others & not the respect.
We should be good human beings as well. There should be strictness sometimes & not all the
times. It only mess the things. He should understand others.
This style of leadership of poore might be appropriate when the workers dont listen to the boss &
they do whatever they want, When workers are not working for the benefit of the organisation
etc, Whean they are cheating the organisation, their boss etc. Being Cruel is not the solution for
every problem.
The first.
I have a few questions - 1. There are 25 people, and 12 clubs. If .pdf
I have a few questions -
1. There are 25 people, and 12 clubs. If a person can join at MOST one club and some may be in
no club. Some clubs will have more members than others. Not paying attention to who
SPECIFICALLY joins each club, how many different ways could the people join the clubs.
ex: club 1 has 2 people, club 2 has 1 person, club 3 has 4 people etc. or club 1 has 4, club 2 has
3... etc
2. A word is composed of 11 uppercase and 10 lowercase letters . The 11 uppercase letters are
grouped together in a block in the middle of the lowercase letters. How many anagrams can be
formed that have the uppercase block in any order in this same location?
THANKS in advance! An explanation would really be appreciated
Solution
1. Think about it in stages. There are 25 people we need to assign to clubs.
Therefore there are 25 stages. Each person can join (12) clubs or no club at all and therefore has
13 options. Person 1: 13 options Person 2: 13 options Person 3: 13 options You get the ideals
So there are 13^25 ways that the people can join the clubs. First think of how many ways you
can arrange the 11 uppercase letters. Stage 1: 11 options Stage 2: 10 options Stage 3: 9 options
because you are using 1 letter every time. So there are 11! ways to arrange the upper case letters.
But, that block can be rearranged within the lowercase letters. L L L L L L L L L L Assuming it
has to be between two of the lower case letters, there are 9 space. So there are 11! * 9 <.
I had heard that, at the start of the Great Depression, JP Morgan an.pdf
I had heard that, at the start of the Great Depression, JP Morgan and other bankers attempted to
prevent the depression by purchasing some of the overpriced stock.
Did the actions of JP Morgan and the other bankers have any effect on the US economy at the
time? Were the effects of the Great Depression mitigated by their actions?
Solution
Not really. While it\'s possible that the Morgan intervention softened the initial stock market
decline, the vast majority of the ultimate collapse in securities prices, commodity prices,
industrial production, and the banking sector took place after the purchases had been liquidated.
First some history: the bankers\' pool intended to lift the stock market was assembled at the J.P.
Morgan & Co. offices on October 24, 1929, \"Black Thursday\", the initial day of market chaos.
They pooled their resources and directed broker Richard Whitney to place high bids on blue chip
stocks at the NYSE to lift the confidence of the markets. This stemmed the crash on Thursday
and led to a rally that continued the next two days. Indeed, the following Sunday\'s New York
Times lauded the \"carefully arranged\" plan to boost the market, and mentioned that the
inclusion of the First National Bank in the buyers\' pool the next day confirmed to Wall Street
that \"the danger of panic had been averted\". Some history of these events is provided in the
Wikipedia article on the crash.
Alas, the Monday and Tuesday immediately following this confident appraisal - October 28 and
29, 1929 - are known as Black Monday and Tuesday, respectively. They experienced the second
and fourth largest daily percentage DJIA declines in history, combining for easily the largest
two-day percentage decline in history. Most newspapers at the time described the bankers\' pool
as waiting to intervene until late Tuesday, when they cut margin requirements and placed buying
orders (see AP article). An AP article describing the crash on Monday opened with \"powerful
financial interests stepped aside today and let the stock market drop...\". Finally, on Wednesday
the bankers\' pool entered with strong buying orders, leading to the third highest daily percentage
DJIA increase in history. The next day the pool apparently stepped away from the markets once
more.
It kept going like this for a little while, with some more volatility in markets over the next few
weeks but eventual stabilization and partial recovery. Finally, the banking group quietly wrapped
up its activities in February 1930, with press accounts mentioning that it \"come out about
even\".
Given all this, it\'s possible that the bankers\' pool offered some initial stabilization of the
markets. (Although even that\'s not clear: from press accounts, it seems just as likely that
volatility was increased by the erratic day-to-day decisions of the pool.) But again, any
significant effect seems unlikely, because the vast majority of the carnage in the Great
Depression took place after the pool had been liquid.
I gotdydx = x2 -2y 2y + dy =x2 dx y =.pdf
I got
dy/dx = x2 -2y 2y + dy =x2 dx y = x
Solution
After the step= -2y
+2y =
Taking L .C. M on left side, we get
=
dy +2ydx = dx
dy = dx - 2ydx
dy = ( -2y) dx
Now integrating on both sides we get,
=
y = 2yx +c , where c is the constant of integration.
i got this so far but when i put it in its showing that it is wrong .pdf
i got this so far but when i put it in its showing that it is wrong 3+ or -square root of -31/4
Solution
by using quadratic, x² - 3/4x = 5/2 + (- 3/4)² x² - 3/4x = (40 + 9)/16 (x - 3/4)² = 49//16
x - 3/4 = ± 7/4 = x - 3/4 - 7/4, = x - 10/4, = x - 5/2, = 2x - 5 = x - 3/4 + 7/4, = x + 4/4, = x + 1
Answer: x = 5/2, - 1; factors: (2x - 5)(x + 1).
I got 0 Limx rightarrow 0 + [lnx]sinxSolutionas lim x-0 we ha.pdf
I got 0 Limx rightarrow 0 + [lnx]sinx
Solution
as lim x->0 we have ln(0+) tends to negative infinity and sinx tends to zero
so we have *0 form
we multiply and divide by x
lim x->0 (xlnx)*(sinx/x)
we know as x->0+ sinx/x ->1
we need to determine where xlnx will go
limx->0+ xlnx = limx->0+(lnx/(1/x))
apply L-hospital rule = limx->0+ (1/x)/(-1/x^2) = limx->0+ -x
so it tends to 0
so product
limx->0+ ln(x) sinx = limx->0+ (xlnx)*(sinx/x) = 1*0 = 0
so yes you are correct.
I dont understand what I marked at the figure belowhow he know t.pdf
I don\'t understand what I marked at the figure below
how he know that 1/2 is subset from B and the same for -3
Solution
we know B is a set of all x which satisfy the equation 2x^2 + 7x + 2 = 3/x since 1/2
and -3 satisfy the above equation , we can say that 1/2 , -3 are the elements of B therefore every
element of A belongs to B hence, A is a subset of B.
I am working on the project using matrix transformations. All of a s.pdf
I am working on the project using matrix transformations. All of a sudden whenever I use the
ListPlot function, the graph it gives me only ranges from -1 to 1 on both axes. I can\'t figure out
how to change the properties. It was working fine before. Any suggestions?
Solution
Change this property where a and b are the intervals of x axis.
DataRange -> {a, b}.
I could use some help symbolizing these sentences, I have doubts abo.pdf
I could use some help symbolizing these sentences, I have doubts about my own answers. Any
help would be appreciated.
Let Fx-x is a female, Mx-x is a male, Sxy-x is sibling of y, Pxy-x is parent of y,
b-Britney,Mxy-x is the spouse of y,Lxy-x likes y,Lx-x is lard dip
1. Some grandfathers are not fathers.
2. Britney\'s niece likes lard dip.
3. Britney has no husband but does have two children.
Solution
1. False, a Grandfather would be (Ex)(Ey)(Ez)(Mx & Pxy & Pyz) and a Father would be
(Ex)(Ey)(Mx & Pxy). To be a grandfather and not a father: (Ex)(Ey)(Ez)(Mx & Pxy & Pyz &
~Pxy). It contradicts itself.
2. To be Britney\'s Neice: (Ex)(Ey)(Sbx & Pxy) and so (Ex)(Ey)(Sbx & Pxy & Lz & Lyz)
3. Britney has no husband : ~(Ex)(Mx & Mxb) and Britney has two kids: (Ey)Pby & (Ez)Pbz.
So, ~(Ex)(Mx & Mxb) & (Ey)Pby & (Ez)Pbz.
I claim I can double your investment in two years. What monthly rate.pdf
I claim I can double your investment in two years. What monthly rate of return am I advertising?
Solution
FV = PV (1+r/n)*n*m
where r= rate of interest
m= period, i.e = monthly =12
n =years = 2year
Let amount invested = X
2X = X(1+r/12)*24
2= (1+0.083333333)
1.0293 -1 = 0.0833
0.3517.
I cant understand how the es have disappeared. how did that ha.pdf
I can\'t understand how the \"e\"s have disappeared. how did that happen during this process?
I have indicated using a yellow box.
thank you. Please be detailed in your answers.
Solution
e^-lamda*e^+lamda Taking common e^(-lambda+lamda) =e^0 1.
I cant find tha answers of some questions in chapter 5 for Financi.pdf
I can\'t find tha answers of some questions in chapter 5 for Financial Accounting: Tools for
Business Decision Making 7th edition by Kimmel, Weygandt and Kieso:-
The question 13.
Also, Brief Exercises 3,4,and 10.
Exrcises 8, 10, and 11.
Problem Set A 2.
BYP 8.
Solution.
I can not tell figure out what the hypothesis is. I am so confused, .pdf
I can not tell figure out what the hypothesis is. I am so confused, please help.
Solution
hypothesisis a proposed explanation for a phenomenon.An hypothesis is a specific
statement of prediction. It describes in concrete (rather than theoretical) terms what you expect
will happen in your study.It is not proved to be correct..
I borrow $100 frrom the bank for one year at 6 simple interest. How.pdf
I borrow $100 frrom the bank for one year at 6% simple interest. How much do I end up aying
for the use of the money?
$100( 1 + (.06/12)^12 = $106.17 . What if the money is compounded continuously ?
Solution
A=100e^(0.06*12)=205.44.
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Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
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Event Link:-
https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-exploring-gemini-ai-and-integration-with-mulesoft/
Agenda
● Java 17 Upgrade Overview
● Why and by when do customers need to upgrade to Java 17?
● Is there any immediate impact to upgrading to Mule Runtime 4.6 and beyond?
● Which MuleSoft products are in scope?
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/
YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Priya Shaw - https://www.linkedin.com/in/priya-shaw
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
Shyam Raj Prasad-
https://www.linkedin.com/in/shyam-raj-prasad/
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- 1. 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
- 2. 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.