Masking, models and reality (part 2)
What do SEIR models predict about interventions like mask mandates and how do those predictions compare to what actually happened?
This is the second in a short series of posts:
Part 2: What do SEIR models predict will happen if R₀ changes?
Part 4: Modelling the effects of a mask mandate with 30% compliance using SEIR
Part 5: Comparing mask mandate model predictions with the real world
If you are not familiar with SEIR models and you have the time, I recommend that you take a look at part 1…
What do SEIR models predict will happen if R₀ changes?
We’re going to start by using an SEIR model with the following parameter:
Basic reproduction number (R₀): 2.2
Length of incubation period: 5.2 days
Duration patient is infectious: 2.9 days
Initial infection level: 1 in 7,000,000
That gives us the following results:
The maximum infectious occurs on day 127 and the Full Width at Half Maximum (also known as FWHM) of the infectious wave is 32.6 days.
We can repeat the simulation using a different R₀, let’s try R₀=2.0:
This time the maximum infectious occurs on day 147. More importantly, the FWHM has increased to 37.4 days.
Let’s see what happens if we increase R₀ to say 2.5:
This time the FWHM has decreased to 27.7 days.
The point I want you to take away from this post is this:
A bigger R₀ value will predict a smaller the Full Width at Half Maximum and vice versa, a smaller R₀ value will predict a bigger FWHM
In fact if everything else stays the same, you could actually use the full width at half maximum as a way to measure R₀…
The problem is that often times other things change… which is what we will look at in the next part, namely what happens if we reduce the fraction of the population that is susceptible to infection at the start.