Porisma

Worked solution

Fit a logistic model using both printed integration methods

[Diseases modelling]

A community has 1000 people. The infected population x at t days satisfies dx/dt=kx(1000−x)dx/dt=kx(1000-x), with k>0k>0. Initially one person is infected; after five days 12% are infected. Find when half the population has contracted the disease and state an assumption of the model.

[2008/HCI/Promo/Q8]

CJC · Lecture · Example 10

Separate using partial fractions

For 0<x<10000<x<1000, 1/[x(1000−x)]=11000[1/x+1/(1000−x)]1/[x(1000-x)]=\tfrac1{1000}[1/x+1/(1000-x)]. Integration gives ln⁡∣x∣−ln⁡∣1000−x∣=1000kt+C0\ln|x|-\ln|1000-x|=1000kt+C_0, where the combined constant can also be written as 1000C, as in the source.

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Source method 1: use the contextual range

Since 0<x<10000<x<1000, remove the modulus signs. “Initially” means t=0: x=1 gives C0=−ln⁡999C_0=-\ln999. At t=5, x=120, so 5000k=ln⁡120−ln⁡880+ln⁡999=ln⁡(2997/22)5000k=\ln120-\ln880+\ln999=\ln(2997/22). Setting x=500 gives 1000kt=ln⁡9991000kt=\ln999, hence t=5ln⁡999/ln⁡(2997/22)≈7.03t=5\ln999/\ln(2997/22)\approx7.03 days.

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Source method 2: exponentiate the absolute-value ratio

ln⁡∣x/(1000−x)∣=1000kt+C0\ln|x/(1000-x)|=1000kt+C_0 gives x/(1000−x)=Ae1000ktx/(1000-x)=Ae^{1000kt} after absorbing sign and constant magnitude into A. At t=0, A=1/999. At t=5, e5000k=120(999)/880=2997/22e^{5000k}=120(999)/880=2997/22, so k=ln⁡(2997/22)/5000k=\ln(2997/22)/5000. At x=500 the ratio is 1, yielding the same 7.03 days. One source substitution line drops the last 2 in the ratio denominator and shows 2997/2; the earlier fitted k and later calculation both use 2997/22. The correct fitted value is retained here.

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State the source assumption and model scope

The population remains constant: nobody dies, leaves or enters the community. This is the printed closed-community assumption. The model also represents infected population continuously and has no recovery term; its solution from x=1 stays between 0 and 1000. The mathematical equilibria x=0 and x=1000 were excluded during separation but do not match the initial condition.

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Common mistakes

The coefficient multiplies the number infected and the number not infected; keep both factors.

Read the hint