What is the distribution function for a population with a disease?

In summary, the death density function, f(t), approximates the fraction of the sick individuals who die in the time interval [t, t+Δt] as follows:
  • #1
altegron
14
2

Homework Statement



Consider a population of individuals with a disease. Suppose that [tex]t[/tex] is the number of years since the onset of the disease. The death density function, [tex]f(t) = cte^{-kt}[/tex], approximates the fraction of the sick individuals who die in the time interval [t, t+Δt] as follows:
Fraction who die: [tex]f(t)\Delta t = cte^{-kt} \Delta t[/tex]
where [tex]c[/tex] and [tex]k[/tex] are positive constants whose values depend on the particular disease.

(a) Find the value of c in terms of k.

(b) Express the cumulative death distribution function in the form below. Your answer will be in terms of k.

[tex]
C(t)=\left\{\begin{array}{cc}A(t),& t < 0\\
B(t), & t \geq 0\end{array}\right
[/tex]

Homework Equations



[tex]P(t) = \int_{-\inf}^t p(x) dx[/tex]

The Attempt at a Solution



To solve part a, I know that [tex]\lim_{t\rightarrow\infty} P(t) = 1[/tex]. So c and k must have values so that it equals one. So I integrate [tex]f(t)[/tex] with the relevant equations to get:

[tex]
\frac{-(kt+1) \cdot e^{-kt} \cdot c}{k^2}
[/tex]

from negative infinity to t.

The problem is that this diverges to negative infinity and doesn't give me a meaningful answer. So what do I do?


Also, how do I get the the bar with a superscript and subscript for 'from a to b' in tex?
 
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  • #2
Hi altegron! :smile:
altegron said:
Consider a population of individuals with a disease. Suppose that [tex]t[/tex] is the number of years since the onset of the disease.

[tex]P(t) = \int_{-\infty}^t p(x) dx[/tex]

No, nobody died before the onset of the disease! :wink:

(and that bar would be [tex]|_a^b\ \big|_a^b\ \Big|_a^b\ \bigg|_a^b\ \Bigg|_a^b[/tex])
 
  • #3
tiny-tim said:
Hi altegron! :smile:


No, nobody died before the onset of the disease! :wink:

(and that bar would be [tex]|_a^b\ \big|_a^b\ \Big|_a^b\ \bigg|_a^b\ \Bigg|_a^b[/tex])

Thanks for the response,

That makes sense -- I guess that means I should integrate from 0 to infinity then?

[tex]
\lim_{x\rightarrow\infty} \int_0^x cte^{-kt} dt = 1
[/tex]

Which is easier written as

[tex]
\int_0^{\infty} cte^{-kt} dt = 1
[/tex]

Which is:

[tex]
\frac{-(kt+1) \cdot e^{-kt} \cdot c}{k^2} \bigg|_0^{\infty} = 1
[/tex]

The infinity term goes to zero and the zero term (negated for subtraction) is:

[tex]
\frac{(k \cdot (0)+1) \cdot e^{-k \cdot (0)} \cdot c}{k^2} = 1
[/tex]

[tex]
\frac{(0+1) \cdot e^{0} \cdot c}{k^2} = 1
[/tex]

[tex]
\frac{c}{k^2} = 1
[/tex]

So, finally:

[tex]
c = k^2
[/tex]

That seems plausible but I don't know how to check it.

Then for part b, I know [tex]A(t) = 0[/tex] because the disease hasn't started yet.

For [tex]B(t)[/tex] we use the integral we've already computed, from zero to t years. We know the value at zero is 1 (actually, -1, but it is being subtracted), so we have:

[tex]
B(t) = 1 - \frac{(kt+1) \cdot e^{-kt} \cdot c}{k^2}
[/tex]

And that looks correct on my calculator when I graph it with k=1.
 
  • #4
Looks good! :biggrin:

(btw, that | is really only used for "value at", with only a subscript …

for integrals, use [] … LaTeX will automatically size them to fit if you type "\left[" and "\right]" :wink:)
 

Related to What is the distribution function for a population with a disease?

What is a distribution function problem?

A distribution function problem is a mathematical problem that involves finding a function that describes the probability distribution of a set of data.

What is the purpose of solving a distribution function problem?

The purpose of solving a distribution function problem is to better understand the data and make predictions about future outcomes based on the probability distribution.

How do you solve a distribution function problem?

Solving a distribution function problem involves using various statistical methods and techniques, such as calculating mean, median, and standard deviation, to determine the best fitting distribution function for the data.

What are the different types of distribution functions?

There are various types of distribution functions, including the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. The type of distribution function used depends on the type of data being analyzed.

What are some real-world applications of distribution function problems?

Distribution function problems are commonly used in fields such as finance, economics, and engineering to analyze and predict outcomes based on data. For example, in finance, distribution functions are used to model stock prices and predict future market trends.

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