How long will COVID-19 last in Ireland?

Short answer: most likely a very long time

Andrew Parnell , Amin Shoari Nejad , Ken Duffy , HoChan Cheon , Bentelhoda Binaei


Do it yourself

Without further ado, this is the app. Put in what you think are your best guess values on the left hand side and see how long it will last by looking at the calendar on the right:

The app gives you the likely date of the 10% chance, the 50% chance (i.e. best guess) and the 90% chance that the virus will be extinct, i.e. Ireland will have no more infected people.

Perhaps the most important thing to try is to fiddle with the R-number at the top left. If you are a member of the Zero COVID crew you can see how long it would take to get to extinction by moving the R number all the way down to 0.1 (simulating an almost total lockdown). Or if you want to follow something like the Swedish strategy you could put it up to closer to 3 to see how quickly it would burn through the population. Or you could put in what you think are best guesses as to all the figures and see what the model comes up with for how long this will last.

How does the app work? (simple explanation)

The app takes four input values down the left hand side:

Once we have these values they are run through an epidemic model of the type used by the National Public Health Emergency Team (NPHET) which simulates many thousands of future scenarios measuring the time to virus extinction. There is randomness in this model; in some scenarios we get lucky and the epidemic dies out quite quickly, in others we don’t and it goes on for quite some time. We summarise the thousands of future extinction times and this gives us an estimate of the date at which 10%, 50% and 90% of the scenarios had the virus extinct.

At this point you might have the question; but we’ve all read in the papers that if the R number is bigger than one then the virus is spreading and if it’s less than 1 then it’s dying out? Well unfortunately this isn’t quite true. It’s possible to have a scenario where the R number is bigger than one and the virus dies out quickly, and the opposite! The reason is that the R number only describes the ‘average’ person. At an individual level each person infects a random different number of people, equivalent to rolling a dice and infecting the number shown on the upward face. If we get lucky then everyone rolls a 1 and they infect very few people. If we get unlucky many people roll a 6 and infect lots of people.

Having said all of the above, there are caveats to our model, and understanding them can help judge whether or not the approach is useful for you. Quite a few of the assumptions are a bit abstract, and are listed in full at the end of the article. Perhaps the main disconnect from reality though is that it assumes that the R value stays the same (at whatever you set it to) until the virus goes extinct. As we are now fully aware, tuning our daily behaviour to stop the virus getting out of control (or killing the economy) is one of the key discussions being had at government level. Were you to set a high R value you might see the virus burn through the population quicker, but it’s unlikely the government would let that happen without being put under some political pressure.

How does the app work? (longer explanation)

The full app is a bit richer than that described above and in this section I’ll give some of the more technical details. Feel free to stop here and go back to playing with the app if you are not interested in the maths.

We use the stochastic discrete-time approximation to the common Susceptible, Exposed, Infected, Removed (SEIR) infection model. This is a multi-compartment epidemic model and is commonly used by governments and organisations around the world to forecast epidemic behaviour. There are versions of the model that we would consider overly simplistic; such as those without stochastic components which seem to yield way-too-certain estimates of cases and deaths. Other more advanced versions have changing R number behaviour which allows lockdown effects to be modelled, but that’s the subject of a post for a different day.

The basic stochastic models have exponential waiting times, meaning that the amount of time someone spends in each of the 4 states (S, E, I or R) follows an exponential distribution. We have extended this to allow for gamma distributions which provides a slightly richer and more realistic estimate of the waiting time. The reason this entire app works as a prediction tool is because the exponential distribution is memoryless, and so this model is appropriate to estimate the future behaviour of the epidemic given only current estimates of the parameters.

To run our stochastic simulator we have to set some values of the parameters of the SEIR model. We have tried to follow guidance from the documents provided by the IEMAG as given here. The values we have chosen include:

If you think that we have got any of these values wrong, then please contact us.

Even with this model, that’s not the end of the story. Since repeatedly running the model thousands of times to get estimated extinction quantiles is very computationally intensive, we build an emulator of the output quantiles. At the first stage we used a Latin hypercube sample to cover a large range of R_0, E, I, and R values from the input space. We then repeatedly build Gaussian Process models to determine where the uncertainty in the quantiles is largest. After a large number of these simulations we then used the scikit-learn Python package to build a set of extreme boosted trees on the multivariate outputs to produce the final predictions of the quantiles.

A list of caveats

To end this post, here are some of the final caveats to the model, which we might try and improve at a later date:

If you can think of other caveats or issues with our app please contact us.


For attribution, please cite this work as

Parnell, et al. (2020, Sept. 13). The Hamilton Institute's COVID-19 Data Dive: How long will COVID-19 last in Ireland?. Retrieved from

BibTeX citation

  author = {Parnell, Andrew and Nejad, Amin Shoari and Duffy, Ken and Cheon, HoChan and Binaei, Bentelhoda},
  title = {The Hamilton Institute's COVID-19 Data Dive: How long will COVID-19 last in Ireland?},
  url = {},
  year = {2020}