8 Matching Annotations
1. Nov 2017
2. www.maa.org www.maa.org
1. Where did the minus sign come from?

Pretty sure it's to do with increase / decrease of respective populations... but will need to double check

2. What assumption about the model does this reflect?

reflects the assumption of a constant population.

3. Why is the factor of  I(t)  present?

I(t) is present in this equation as it is the rate the infected population comes into contact with the susceptible fraction s(t) of the population over time??

4. [With a large susceptible population and a relatively small infected population, we can ignore tricky counting situations such as a single susceptible encountering more than one infected in a given day.]

add to assumptions explanation?>

5. In particular, suppose that each infected individual has a fixed number  b  of contacts per day that are sufficient to spread the disease.

infection rate is another way of stating the number of infected that come into contact with susceptible members of the population.

6. Under the assumptions we have made, how do you think  s(t)  should vary with time? How should  r(t)  vary with time? How should  i(t)  vary with time?

Might need to look up this in the research paper(s). My hunch is that for each relevant increment of time (eg. day), "I" should increase and "S" should decrease at a directly proportional rate. This is due to the Susceptible portion of the population becoming ill, thusly infected.

7. Explain why, at each time  t,  s(t) + i(t) + r(t) = 1.

Assuming that this is due to the fact that combined, S, I and R classes are each representing a portion (or fraction) of the whole population (aka. are a portion of the whole).

#### URL

3. docs.statwing.com docs.statwing.com
1. Heteroscedasticity

Heteroscedasticity is a hard word to pronounce, but it doesn't need to be a difficult concept to understand. Put simply, heteroscedasticity (also spelled heteroskedasticity) refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it.