Modeling the Course of a Viral Illness

In summary, the task is to find an equation that models viral growth when certain viral particles enter the body and replicate at a rate of 160% every four hours, while the immune system eliminates them at a rate of 50000 viral particles per hour. The equation is derived using upper and lower Darboux sums and the integral of 1.6^(x/4). The current attempt at a solution is unclear and assistance is requested to correctly start the problem.
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jgens
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Homework Statement



When certain viral particles enter the body, they replicate to 160% every four hours and the immune system eliminates these particular viral particles at the rate of 50000 viral particles per hour. Find an equation modeling this viral growth.

Homework Equations



N/A

The Attempt at a Solution



Let [itex]n[/itex] be the function modeling this viral growth, then clearly

[tex]n_0(1.6^{t/4}) - 50000t \geq n(t) \geq (n_0 - 50000t)(1.6^{t/4})[/tex]

since the first expression assumes that every particle replicates first and then 50000t particles are eliminated and the last expression assumes that all 50000t particles are eliminated and then they replicate. In reality, the particles are always replicating and being eliminated simultaneously, so [itex]n(t)[/itex] must be between these two extremes. Using this same train of thought, we know that . . .

[tex](n_0 - 50000t)(1.6^{t/4}) \leq n(t)[/tex]

[tex][(n_0 - 50000t/2)(1.6^{t/8}) - 50000t/2](1.6^{t/8}) \leq n(t)[/tex]

[tex]\vdots[/tex]​

[tex]n_0(1.6^{t/4}) - 50000t \sum_{i=0}^{n-1} \frac{1.6^{\frac{(n-i)x}{n}}}{n} \leq n(t)[/tex]

and

[tex]n_0(1.6^{t/4}) - 50000t \geq n(t)[/tex]

[tex](n_0(1.6^{t/8}) - 50000t/2)(1.6^{t/8}) - 50000t/2 \geq n(t)[/tex]

[tex]\vdots[/tex]​

[tex]n_0(1.6^{t/4}) - 50000t \sum_{i=1}^{n} \frac{1.6^{\frac{(n-i)x}{n}}}{n} \geq n(t)[/tex]

Since [itex]1.6^{t/4}[/itex] is an integrable function and because the two sums above represent the upper and lower Darboux sums for [itex]1.6^{t/4}[/itex], it follows that

[tex]n(t) = n_0(1.6^{t/4}) - 50000t \int_0^t 1.6^{x/4} dx[/tex]

I'm sorry that my work isn't perfectly clear at the moment, but seeing as I'm fairly certain that it's wrong, I'm not sure that it matters much. I would appreciate help actually starting this problem correctly (which I'm fairly certain that I haven't done). Any help is appreciated. Thanks!
 
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If anyone needs me to clarify anything, please let me know and I'll get to work on it.
 

FAQ: Modeling the Course of a Viral Illness

How is the course of a viral illness modeled?

The course of a viral illness is typically modeled using mathematical models, such as the SIR (Susceptible-Infected-Recovered) model, which divides the population into three categories: susceptible, infected, and recovered individuals.

What factors are considered when modeling the course of a viral illness?

Factors such as the virulence and transmission rate of the virus, as well as population demographics and behaviors, are taken into account when modeling the course of a viral illness.

How accurate are the models used to predict the course of a viral illness?

The accuracy of the models depends on the quality of the data used to create them and the complexity of the model itself. While models can provide valuable insights, they are not always 100% accurate and should be interpreted with caution.

How can modeling the course of a viral illness help in controlling its spread?

By using models, we can identify potential hotspots for viral spread and predict the impact of different control measures, such as vaccination or social distancing, on the spread of the virus. This can help inform public health policies and interventions.

Are there any limitations to modeling the course of a viral illness?

Yes, there are limitations to modeling the course of a viral illness. Models are based on assumptions and simplifications, and they can only provide predictions based on the data and parameters used. They also do not take into account unpredictable factors, such as mutations in the virus or human behavior changes.

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