Mathematical Models and Numerical Methods - Lecture Notes | MATH 2250, Study notes of Linear Algebra

Material Type: Notes; Professor: Bornholdt; Class: Linear Algebra and Differential Equations; Subject: Mathematics; University: Utah State University; Term: Unknown 1989;

Typology: Study notes

Pre 2010

Uploaded on 07/30/2009

koofers-user-zht
koofers-user-zht 🇺🇸

9 documents

1 / 4

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
MATH 2250 Linear Algebra and Differential Equations Chapter 2
Chapter 2 Mathematical Models and Numerical Methods
2.1 Population Models
Birth rate and death rate are defined as follows:
)(t
is the number of births per unit of population per unit of time at time t;
)(t
is the number of births per unit of population per unit of time at time t;
Therefore, the total number of births in a population per unit time is
P
.
If the birth rate
)(t
and death rate
)(t
of a given population are constant, then under ideal
conditions, the time rate of change in population is proportional to the population itself:
kP
dt
dP
.
However, there are a few practical issues for this model.
Conditions are likely not ideal and change in time
If conditions change, then the birth and death rates will likely change in time
More fundamentally, population is not a continuous process (you cannot have 13.675
people)
Nonetheless, a continuous solution allows us to more accurately model the discrete (integer
valued) behavior. It is the dynamic character of change that we want to address in this section.
Specifically, if the birth rate
)(t
and death rate
)(t
are changing, then we have
)()]()([ tPtt
t
P
for small values of
t
.
In the limiting case,
P
dt
dP ][
.
Bounded Populations and the Logistic Model
A common situation is where a population has a limiting capacity due to restricted resources and
space. In this case, the limiting population, M, plays a significant role. For purposes of
illustration, suppose a population satisfies the differential equation
.
We can solve this by separating variables, but before we do, let’s examine the slope field for
this differential equation.
Clearly, if P = 0 or P = 10,
0
dt
dP
, so these values correspond to constant or equilibrium
solutions. That is, if the population is ever zero, it remains zero. Likewise, if the population is
ever 10, it remains 10 for all time.
Furthermore, if 0 < P < 10, the product of P and (10 – P) is positive, so the population is
increasing in this case so solution curves in this region approach P = 10 (not P = 0).
pf3
pf4

Partial preview of the text

Download Mathematical Models and Numerical Methods - Lecture Notes | MATH 2250 and more Study notes Linear Algebra in PDF only on Docsity!

Chapter 2 Mathematical Models and Numerical Methods

2.1 Population Models

Birth rate and death rate are defined as follows:

  ( t )is the number of births per unit of population per unit of time at time t;

 ^ ( t ) is the number of births per unit of population per unit of time at time t;

Therefore, the total number of births in a population per unit time is

 P

If the birth rate ^ ( t )and death rate ^ ( t )of a given population are constant, then under ideal

conditions, the time rate of change in population is proportional to the population itself:

kP

dt

dP

However, there are a few practical issues for this model.

 Conditions are likely not ideal and change in time

 If conditions change, then the birth and death rates will likely change in time

 More fundamentally, population is not a continuous process (you cannot have 13.

people)

Nonetheless, a continuous solution allows us to more accurately model the discrete (integer

valued) behavior. It is the dynamic character of change that we want to address in this section.

Specifically, if the birth rate

 ( t )

and death rate

 ( t )

are changing, then we have

[ ( t ) ( t )] P ( t )

t

P

for small values of  t.

In the limiting case,

P

dt

dP

[   ].

Bounded Populations and the Logistic Model

A common situation is where a population has a limiting capacity due to restricted resources and

space. In this case, the limiting population, M, plays a significant role. For purposes of

illustration, suppose a population satisfies the differential equation

P ( 10 P )

dt

dP  

We can solve this by separating variables, but before we do, let’s examine the slope field for

this differential equation.

Clearly, if P = 0 or P = 10, ^0

dt

dP

, so these values correspond to constant or equilibrium

solutions. That is, if the population is ever zero, it remains zero. Likewise, if the population is

ever 10, it remains 10 for all time.

Furthermore, if 0 < P < 10, the product of P and (10 – P ) is positive, so the population is

increasing in this case so solution curves in this region approach P = 10 (not P = 0).

On the other hand, for P > 10, product of P and (10 – P ) is negative, so the population is

decreasing, and hence, solutions in this region approach P = 10 (not infinity).

Here is the corresponding slope field for P (^10 P )

dt

dP

  : (sketch in class)

Now solve the differential equation letting P 0 denote the initial population when t = 0.

Separating variables gives

dt

P P

dP

We evaluate the left side using partial fractions (see online link for partial fractions):

dP

P

B

P

A

P P

dP

and determine that

A  B . Therefore, the left hand side becomes

(ln ln| 10 |)

10

P P

dP

P P

dP

P

B

P

A

P P

dP

Combining with the integral of the right side gives

1 | 10 |

ln

t c

P

P

Multiply through by 10 and then exponentiate both sides to obtain

t Ce

P

P

10

where

1 10 c

C  e

At this point, look at the slope field above. Notice that the negative sign corresponds to the

solutions where P > 10. Furthermore, if t = 0 and

0

P 

, we see that

C

P

P

0

0

3.) The logistic equation can be used to describe a population in which a disease is spreading.

Here P^ ( t ) denotes the number of individuals in a constant-size susceptible population M who

are infected with a certain contagious and incurable disease. The disease is spread by chance

encounters. Then

P ' ( t )

should be proportional to the product of the number P of individuals

having the disease and the number M  P of those not having it, and hence, kP (^ M P )

dt

dP

4.) The mathematical description of the spread of a rumor in a population of M individuals is the

same.

All of these examples result in a similar differential equation.

Example: Suppose that the population P (^ t ) (in millions) of a country satisfies the differential

equation kP (^200 P )

dt

dP

  with k constant. Its population in 1940 was 100 million and was

then growing at the rate of 1 million per year. Predict this country’s population for the year

Solution: We will directly refer to the solution given (above) by

kMt P M P e

MP

P t   

( )

( )

0 0

0

where M = 200 million,

0

P 

million and k is found by letting

dt

dP

million per year

when t = 0 (1940):

1 / 10 , 000

1 ( 100 )( 200 100 )

 

k

k

Therefore,

50 100 100

20 , 000

( ) t

e

P t

For the year 2000, t = 60, so we have

  1. 7

100 100

20 , 000

( 60 )

50

60

 

e

P

million people.