How can we model viral infections?
One way is with a logistic function. This is a function $f(t)$ which is a solution to
a differential equation of the form something like
\[ f'(t) = cf(t)(Lf(t))\]
Typically, equations like this have the form
\[ f(t) = \frac{L}{1+de^{ct}} \]

Another potential model for these sorts of phenomena is the question of how big the largest component of a random graph is.
What happens with random graphs?
We get functions appearing like
\[ g(t)=e^{e^{ct}} \]


How do these functions compare? Let's plot them on the same axes: $f(t)$ in blue, $g(t)$ in red.

We see that we have a later start, with a steeper trajectory for $g(t)$ than we do with $f(t)$.

How about modeling a population consisting of two separate groups who don't (basically) infect each other.
For example, Hubei + rest of china.

