Modeling Population Growth with Constraints: A Scientific Approach

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SUMMARY

This discussion focuses on modeling population growth with specific constraints, particularly using differential equations. The initial model is based on the exponential growth equation dN/dT = kN, transitioning to a more complex model incorporating the probability of informing others and the number of people each individual informs. The user proposes a logistic growth model represented by dN/dt = RαkCm(1 - N/k)N, seeking clarification on the derivation and reasoning behind the model's structure, particularly the multiplication of terms rather than addition.

PREREQUISITES
  • Understanding of differential equations, specifically exponential and logistic growth models.
  • Familiarity with population dynamics and constraints in mathematical modeling.
  • Knowledge of parameters such as growth rate (r), carrying capacity (k), and informing probability (α).
  • Basic skills in mathematical derivation and manipulation of equations.
NEXT STEPS
  • Study the derivation of the logistic growth model and its applications in population dynamics.
  • Learn about the role of parameters in differential equations, focusing on growth rates and carrying capacity.
  • Explore the implications of informing probability and its impact on population spread in models.
  • Investigate real-world applications of population growth models in epidemiology and social dynamics.
USEFUL FOR

Students and researchers in mathematical biology, particularly those interested in population dynamics, epidemiology, and mathematical modeling techniques.

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Homework Statement


I'm trying to model how a population would grow wrt the following constraints:

1. Number of people within the population
2. How many people each person of the population informs
3. Probability of each person of the population informing a member outside the population.
4. There is an upper limit to the population growth, but this is not the most important constraint as of now.

It's almost like a virus growth model and I have some idea of where to begin, but it's been a while since I've solved these problems.

Homework Equations



Because of the first constraint, this looks like an exponential growth model to begin with. Thus,

\frac{dN}{dT}=kN

is the basic equation to be used.

The Attempt at a Solution



From the second constraint onwards, I'm having a problem forming the DE.

The growth is dependent on the probability of each person within the population telling others about it and the number of people each person will tell.

Thus, if \alpha (t) is the probability of a person within the population telling others
and m(t) are the number of people each person tells, then I think the model would look like this:

\frac{dN}{dT}=\alpha (t)m(t)N

Is this right? Again, it's been a while since I've done this stuff, so I'm not sure, but I think I'm on the right track.

For the final constraint, if I have to put an upper limit on the model, I think I can do a (k-N) growth model where k is the upper limit.
 
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I believe that the model I'm trying to derive is akin to the Logistic Growth Model. However, I cannot understand the derivation of the same. Could somebody guide me through the process?

From what I understand, as there is an upper limit on the population here (k), the rate of growth is directly proportional to (k-N). However, the model is also proportional to the current population, N.

I do not understand why we multiply the two and not add them. Thus, why is the growth proportional to N(k-N) ?

Furthermore, the logistic growth model gives the following equation:

\frac{dN}{dt}=\frac{r}{k} N(k-N)

Why do we divide throughout by k? Also, what is r?
 
Can someone help me out with this? This is what I have so far:

Since the rate of growth is directly proportional to the current population, N and there is a cap on the total possible population K.

As each person reaches out to m other persons, the probability of telling someone who is not a part of the current population is given by \alpha k C m(1-\frac{N}{K})

Thus, if the acceptance rate is R, then the rate of growth should be:

\frac{dN}{dt}=R\alpha kCm(1-\frac{N}{k})N

Is this right?
 

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