Integration of Logistic Model

In summary: C and N. In summary, the given population logistics model, with t as the year, C as the carrying capacity, k as an unknown constant, and y(t) as the population at time t, can be rearranged and integrated to find the population at any given time. Using the given information for t=1987 and t=1999, the equation can be solved for k and used to find the population in 2050 where C=20 and in 2100 where C=10.
  • #1
BraedenP
96
0

Homework Statement



The following is a population logistics model where t is the year, C is the carrying capacity, k is some unknown constant, and y(t) is the population at time t.

I am given [tex]\frac{dy}{dt}=ky\left ( 1-\frac{y}{C} \right)[/tex]
and that [itex]y(t)[/itex] is the population size for the year t.

I am also given that when t=1987, y=5 AND when t=1999, y=6. C is always 20.

I am supposed to figure out (a) the population in 2050 where C=20, and (b) the population in 2100 where C=10.

Homework Equations



The Attempt at a Solution



I rearranged the equation to form: [tex]\frac{1}{y}+\frac{1}{C-Y} dy = k dt[/tex]
and then integrated to get: [tex]ln(y)-ln(C-Y)=kt+N[/tex], (where N is the constant of integration to avoid confusion with C)

This is where things start going wrong. I substituted the "1987" and "5" values into the equation and solved for N, getting: [itex]N=ln(\frac{1}{3})-1987k[/itex] and then substituted this equation back into the equation using the other two values, "1999" and "6":

[tex]ln(\frac{3}{7})=1999k + ln(\frac{1}{3})-1987k[/tex]

I then solved for k, getting: [tex]k=\frac{ln(\frac{9}{7})}{12}[/tex]

All of these logarithms prevent me from calculating a nice clean answer for (a), and I have no clue where to start with regard to (b). Can anyone point me in the right direction?

Thanks!
 
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  • #2
at this point how about
[tex]
ln(y)-ln(C-y)
=ln(\frac{y}{C-y}) = kt + N
[/tex]

[tex]
\implies \frac{y}{C-y} = e^{kt + N}
[/tex]

[tex]
\implies y(1+e^{kt + N}) = Ce^{kt + N}
[/tex]

[tex]
\implies y
= \frac{Ce^{kt + N}}{1+e^{kt + N}}
= \frac{C}{e^{-(kt+N)}+1}
= \frac{C}{Be^{-kt}+1}
[/tex]

where B is a constant
 
Last edited:

What is a logistic model?

A logistic model is a mathematical representation of a population growth or decline that takes into account a limiting factor, such as resource availability or carrying capacity. It is often used to model the growth of biological populations, but can also be applied to other areas such as marketing and economics.

Why is logistic model integration important?

Integration of a logistic model is important because it allows us to better understand and predict the behavior of complex systems that are affected by limiting factors. It also helps us determine the optimal conditions for growth and identify potential problems or limitations that may arise.

What are the steps involved in integrating a logistic model?

The steps involved in integrating a logistic model may vary depending on the specific application, but generally include defining the problem, gathering data, selecting appropriate parameters, fitting the model to the data, and evaluating the results. It may also involve sensitivity analysis and model validation to ensure the accuracy and reliability of the integrated model.

What are some challenges in integrating a logistic model?

Some challenges in integrating a logistic model include selecting appropriate parameters, dealing with missing or inaccurate data, and ensuring the model is a good representation of the real-world system. Additionally, the complexity of the model and the potential for nonlinear behavior can make it difficult to interpret and use the results effectively.

How can logistic model integration be applied in real-world scenarios?

Logistic model integration has a wide range of applications in various fields, including biology, ecology, economics, marketing, and public health. It can be used to predict population growth or decline, evaluate the effectiveness of management strategies, and inform decision-making in areas such as resource allocation, disease control, and market forecasting.

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