How to Calculate the Risk of a Pandemic
With new COVID strains popping up and a future that holds new and evolving viruses, Martin Pergler’s post on how epidemiologists measure the severity of a pandemic may be useful.
We all look at our national and local COVID infection counts. But to really measure the pandemic’s pulse, epidemiologists monitor a number called Rt and do their best to wrest it below 1. What is Rt, how does it work, why do we care, and what are its limitations?
What is Rt? (and a baseball analogy)
Fundamentally, Rt is the average number of people a single COVID-infected person will directly infect in turn, before that individual stops being infectious. It is called the effective reproduction ratio. It assumes a closed population, and the t refers to the fact that it varies in time.
If Rt is below 1, the infection will die out, since it is finding new victims more slowly than current victims recover (or not.) If Rt is greater than 1, the infection will spread. If Rt stays any constant number greater than 1 for some time, that spread will be exponential, the more “explosive” the greater that number is.
A special case is R0, also called the basic reproduction ratio, which is the number of people infected by one carrier at time 0, the start of a pandemic, before anyone is doing anything special and before anyone has immunity. It reflects both the infection’s average “at bats” (to use a baseball analogy)–how many people an average carrier has the opportunity to infect–as well as its natural “on base percentage”–how transmissible it is, both given normal human behavioural patterns and resiliency to infection.
>> R0 for COVID seems to have been about 2.5 or higher (see below for difficulties in estimation). This is higher than typical seasonal influenzas, but much lower than traditional highly infectious diseases like chicken pox or measles.
One generally expects Rt to decrease from R0 over time, for three reasons.
People change their behaviour, whether of their own free will or forced by public health directives. This decreases a combination of the “at bats” and “on base percentage”.
We get better at identifying those infected, those susceptible to infection, and at prevention or treatment. We don’t allow the pandemic as many at bats, and we pitch at it in a way to induce it to harmlessly fly out when we do.
Finally, if there is any sort of resistance to reinfection (immunity), fewer of the contacts that would earlier have led to an infection are with people still susceptive. The baseball analogy is a bit strained here: maybe we say we keep on flooding the field with more and more outfielders until there is no gap to hit into any longer.
Key points include:
- The math, in simplified form
- Different scenarios relevant to COVID response
- Unsimplifying and applying to COVID
Read the full article, The What and the Why of Rt, on LinkedIn.
