, and it turns out that simple versions of this model work pretty well empirically for many diseases. The basic idea of the model is that one has three compartments representing three types of people, people who are susceptible to the disease but are uninfected, people who are infected, and people who have recovered. People move from the susceptible to the infected category based on the number of people infected, with some parameter representing how likely an infected person is to infect new people. People move from the infected to the recovered population at some fixed probability. Strictly speaking "recovered" for our purposes also includes people who died and thus aren't spreading the disease but that distinction doesn't matter much for this sort of model. The basic model assumes that once one is in the recovered category one is either dead or immune.

This is the basic model, and one can do a lot of things to play with the model. For example, one can imagine one has a vaccine for some people; this moves some people directly from the susceptible box into the recovered box. This drastically reduces the size of one's hump. Another thing one can do is have more than three boxes; one can imagine each region (say a city, school or nation) with its own set of boxes but then a chance for people infected in a region to infect people in a nearby region. One can also imagine diseases which have long periods of spread and infection, so the presence of births in the population become relevant. A good exercise is to think of some other thing you'd want to add to this sort of model.

This is the basic model, and one can do a lot of things to play with the model. For example, one can imagine one has a vaccine for some people; this moves some people directly from the susceptible box into the recovered box. This drastically reduces the size of one's hump. Another thing one can do is have more than three boxes; one can imagine each region (say a city, school or nation) with its own set of boxes but then a chance for people infected in a region to infect people in a nearby region. One can also imagine diseases which have long periods of spread and infection, so the presence of births in the population become relevant. A good exercise is to think of some other thing you'd want to add to this sort of model.

First, not everyone who gets the virus becomes immune after they recover; we're seeing not just relapses but evidence of reinfection. One essentially has some people who would move from infected to recovered but instead are moving back to susceptible. If one plays around with SIR models one will see that having such movement can easily make epidemics much worse. We're not completely certain that such reinfection is occurring but it looks likely. One difficulty is trying to distinguish reinfection from relapse. There's some evidence for low-level infections in people who are otherwise considered to have recovered. Trying to figure this out is going to be something doctors are thinking about very carefully. This is a general news article discussing some of the issues involved

Second, the contagion rate of this virus appears to be higher than that for influenza. While there's a lot of uncertainty about the reproduction rate, R_0, which roughly speaking, represents the number of average new infections from infected individual, those numbers range from about 2.2 to about 4.5 and they seem to be likely on the higher end. In contrasts, many estimates for influenza put this number for it at at most 2; for the 1918 flu the number was probably between 2 and 3. Increasing R_0 has for what should be obvious reasons, a pretty severe impact on the severity of epidemics. The exact translation of R_0 into the SIR has some subtleties, and estimates for R_0 do change for diseases. They frequently start off larger than they would be otherwise until procedures targeting a disease are in place. There's some substantial criticism of R_0 as a metric in general , but as a rough guide it is useful here. There's also been some statement by the WHO (such as here ) saying that at least by some metrics, COVID-19 is less efficient at transmission than the flu. I''m not sure what metrics they are using there, but if that's accurate that's a major reason to be less worried.