Previously, in infectious disease modeling, you developed a simple SIR model. In the lectures that follow, we're going to set aside the mathematics and spend some time building intuition around what drives the behavior of an epidemic. As you'll see, this includes not just a number of infected individuals but also the rate at which these individuals are able to cause new infections. You will also learn about a core concept in infectious disease modeling, the basic reproduction number or what we call R-naught for short. Here are just an example of a real epidemic curve. It shows the total number of infected people over time, what we call the prevalence of infection. The curve is very similar to what you have already modeled using the SIR equations. That's because this is basically an SIR epidemic. It's a single epidemic of an infection that confers long lasting immunity. Given that we're looking at just a few months, the impact of newborn children entering the system has susceptibles is tiny and not enough to replace those who have gained immunity. So note some of the interesting features of this epidemic. To begin with, we have only one infected person while everyone else is susceptible. Then prevalence rapidly increases as this person infects other people and so forth. However, this doesn't go on forever, and at some point, the epidemic slows down. It reaches a peak and then goes into decline. Eventually, the prevalence decreases to zero. So why are each of these stages happening? How might they changed with a virus that has more or less infectious or one that has a shorter or longer infectious period? In the following e-tivity, we will think through these underlying mechanisms by splitting the epidemic curve into its two parts; its rise and fall. As I mentioned, don't worry about the mathematics for now. Our immediate aim is to gain a conceptual understanding of what is driving the epidemic. Later, once you have an intuitive understanding of what makes an epidemic tick, we will explore how this intuition translates into mathematical modeling. Then once you feel comfortable with these concepts and equations, we will then look at more complex, more realistic epidemic behaviors. But for now, let's come back to this simple SIR model and think about its rise and fall. We'll begin with the rise.