It's All In The Variables
I'm writing this on April 2, 2020 and given how fast the novel coronavirus (the virus that causes COVID-19) is spreading, what is true today will definitely not be true tomorrow. There are plenty of reliable sources to find out how to react to the virus in your area (in the US, I recommend the CDC which has easily accessible information for non-scientists). So I won't attempt here what those more knowledgeable people are doing, but I will add something for people who like learning how things are measured.
Everything is measurable. Even a virus.
COVID-19 is being regularly compared to SARS (Severe Acute Respiratory Syndrome), H1H1 (commonly called the swine flu), as well as the seasonal flu for which many people get vaccinated every year. In those comparisons, I've seen arguments on social media about which of these is "worse" based on how quickly they spread, the mortality rate, or the presence (or absence) of medications. <sarcasm>Heaven knows I don't object to getting my medical news from memes </sarcasm>, but I wanted to know more about how a pandemic is measured and that led me to read about R0* (pronounced "R naught").
Now remember that any measurement is always a measure of what has already happened. Customer satisfaction is a measure of last month's customer interactions, sales is a measure of last quarter's sales. These rear-view-mirror measurements are usually intended to inform what future we see out of the front windshield and R0 is no different. The problem in this case is that as this pandemic grows and more data is available, the numbers change. This means that we can't draw many conclusions based on the R0 value itself, but perhaps can understand it better based on the underlying variables.
In epidemiology, R0 expresses the average number of individuals in a susceptible population who will be infected by a contagious individual***.
Now anyone who knows more about medicine or epidemiology than I do (there are a lot of them) may start picking apart my use of R0 and that's fair. But my point here isn't to talk about the relative benefits and drawbacks of the measure itself (there is a better discussion of that here). Instead my point is to use this example to talk about how critical it is to know the variables that make up any measure.
The variables in this case are
COVID-19 R0 is estimated at 1.9** based on early analysis done on cases in Wuhan, China between December 2019 and January 2020, although it's likely that we won't know the final R0 value until after the pandemic is under control.
*Because I'm a perfectionist and this website text editor doesn't make it easy to express "R naught" correctly throughout this text, I'll show you here that it should look like R0 when typed.