Susceptibilis: the land of complete susceptibility 

Imagine that in the middle of the Pacific Ocean there is an island called ‘Susceptibilis’. It has a population of 150 people. Measles isn’t a problem on this island and no one is vaccinated against it. Suddenly, seemingly out of the blue, a case of measles pops up in the west of the island. A returned traveller develops a high fever, a runny nose and watery eyes. Several days later his body erupts in a nasty rash. Measles has hit.

This is what I imagine the coast of Susceptibilis might look like, an idyllic place.
(Photo by Jailam Rashad on Unsplash)

 

Epidemiologist hats on! Let’s have a quick look at what’s happened. 

An index case is found

Our returned traveller is, in this case, our index case. The index case is the very first case that the authorities become aware of. For Susceptibilis, this index case may also be our primary case because measles was never seen in the population before this. However, for some disease outbreaks, we actually may never figure out who the very first person to catch the disease was. We now have to think about where the traveller has been, who he has been in contact with. Essentially we need to do some contact tracing to find and isolate any other cases in the community. It’s something that’s easier said than done. It really helps to know how the disease behaves and how it’s transmitted.

What we wish contact tracing looked like: clear, easy, simple.
Unfortunately you don’t always get what you wish for.
(Photo by Markus Spiske on Unsplash)

 

The Basic Reproduction Number

Something we do sometimes know about diseases is the basic reproduction number. R0 (pronounced ‘R naught’), gives us an idea of just how contagious an infectious disease can be when there is no immunity for it in a population. R0 can also be used to help us figure out how many people need to be vaccinated to create herd immunity.

Measles is one of the most contagious diseases that we know of and has a very high R0 value, generally estimated to be somewhere between 12-18. This means that one infected person can go on to infect between 12 to 18 people if nothing is done and the disease is given free reign. In Susceptibilis, with no one being immune to the disease, there’s a good chance that measles will spread very quickly unless something is done. This is why vaccinations are really important. Preventing measles is all we have against it. There isn’t a specific medicine yet to treat it and you can only really treat symptoms.

R0 not as simple as it seems

In real life, estimating R0 isn’t as easy as shown in a textbook or classroom. Infectious disease modelling has to take into account a number of factors. In any population, there are births and deaths. People migrate and different diseases come and go. Seasons change and so do people’s behaviours. For some diseases, people are contagious before they even know they have the disease. So models can only provide good estimates and although there is some degree of uncertainty, they are really important for making informed decisions. 

The story I’ve presented here is an extremely simple snippet. There is much more to a disease outbreak than just knowing the basic reproduction number and who the index case is. If you’d like to delve deeper, here are some places you could start:


5 Responses to “Susceptibilis: the land of complete susceptibility ”

  1. Scarlett Parker says:

    I’m not an epidemiologist myself but you explain everything so clearly in this blog that I didn’t have to be! Thanks for the scattering the links throughout as well, was interesting reading about herd immunity and the current COVID-19 climate.

  2. Ekmini Ramanayaka Pathirannehelage says:

    Thank you so much for your comment Chia-Lun! Means a lot, especially from a fellow Epidemiology student!

  3. Chia-Lun says:

    I really like this blog, it made Epidemiology simply 🙂 And very useful for understanding the current pandemic as well!

  4. Ekmini Ramanayaka Pathirannehelage says:

    Hi Christina,
    How an infectious disease spreads generally depends on a few things, the simplest of which are: how many people are susceptible, how many people are infected and how many people are recovered in your population. It’s not always this simple though. Populations change (births, deaths, migration, etc.). If you are infected, you may have lifelong immunity, or immunity may be lost over time. Some vaccines can also wane (one of the reasons why we sometimes need booster shots). Sometimes you’ll need to know hospitalisation numbers and, for diseases like Ebola, bodies remain infectious even after death so you need to factor in death.
    You’d also have to consider how your population is mixing, at school, in the work place, in the household. And interventions (both medical and non-medical, such as social distancing and masking wearing) all influence the data.
    There’s a lot more I’d like to write but I’ll stop here! You can maybe see why some of the restrictions we’re seeing in Victoria were put in place and why, although there’s been a lot of work done on pandemic influenza, there’s still a lot we’re figuring out about COVID-19 as we go.
    There are different models used in epidemiology and I’ll leave you with an TEDx talk that one of my lecturers did on mathematical models if you’re keen to know more: https://www.youtube.com/watch?v=eHlu6Vi_wxo
    Thanks for the comment (and sorry for the extensive, and hopefully accurate, answer!)

  5. Thank you Ekmini for a well thought out and simple explanation of how an infectious disease spreads and how we track it. A timely post!
    What sort of data goes into the models to create predictions about how the virus will spread?
    Thanks, Christina.