Sep 22, 2021
Hamsa Bastani, Kimon Drakopoulos, Vishal Gupta, Jon Vlachogiannis, Christos Hadjicristodoulou, Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis & Sotirios Tsiodras
In the summer of 2020, with limited COVID-19 testing capability, Greece used a reinforcement learning system ('Eva') to identify travellers most at risk of bringing COVID into the country.
1️⃣ Passengers completed a form beforehand, providing info on their demographics, origin country and date of entry.
2️⃣ They used
LASSO regression is a form of machine learning.
When you have lots of information about travellers, LASSO is a useful method for prioritising which of these factors are important.
For example, imagine you were trying to predict which supercar has the top speed — and you had lots of information about the cars: their horse power, their weight, their colour, whether they had a radio or not...
Some of these factors are clearly important, whereas others (like the colour) are probably irrelevant. A normal regression model might put too much weight on irrelevant factors based on your specific dataset. For example, all the Ferraris and Lamborginis might be red — and your model might start using this to predict the fastest car. LASSO regression helps to avoid this.
3️⃣ They used
Why do we need to use empirical Bayes method?
COVID-19 prevalence data (in travellers) brings up an interesting data problem because
1) COVID-19 has relatively low prevalence and
2) Each country has a very different number of tourists coming to Greece.
For example, the US might have 10,000 tourists coming to Greece — 100 of whom test positive with COVID-19 (1% prevalence). But perhaps only 2 tourists from the Vatican City come to Greece, and 1 tests positive for COVID-19 (50% prevalence). Based on this data — can we say that the Vatican City is a COVID-19 hotbed — and that we should ban all travellers from there? Possibly not.
Empirical Bayes methods help make more sensible decisions when we're dealing with imperfect data like this.
4️⃣Once Eva had identified people to test, they tested multiple people at once using
This is a really interesting form of testing, first used in the 1940s on soldiers with syphilis.
Greece had limited testing capability. So it would collect samples from 5 people, mix them all together and use one test. If the test was positive, they would go back and re-test everyone individually. Quite smart!
Eva uses reinforcement learning. This is a type of machine learning in which the model is 'rewarded' for correctly identifying COVID-19 cases and 'penalised' for missing them. It's almost as if the researchers designed a game for Eva to play.
But there's a problem. Eva might become too good at playing the game. It might start to assign all COVID-19 tests to a specific 'type' of traveller who currently have high COVID-19 prevalence (exploitation).
Since step 2️⃣ is dependent upon recent test data, this could mean Eva was unable to learn from types that were previously low prevalence (but may not longer be!). To avoid being blinded, Eva continues to randomly test (explore) some low prevalence types (it's a bit like doing 'random searches' at the airport)
Eva was efficient and effective. Eva identified 1.85x as many COVID-19 cases as random testing would have.
Many countries tested passengers based on data from their home countries such as cases, deaths and positivity rates. Eva identified about 1.25–1.45x as many cases as this approach.
Eva was found to be most beneficial when testing resources were scarce.
Data minimisation is using the minimum data necessary for a task.
The authors consulted with lawyers, policymakers and epidemiologists in order to decide what information was necessary to collect — and what would be too invasive.
They used data points like age, gender and country of origin, but omitted factors thought to be too identifiable (such as occupation).
It's a tradeoff. More information would have probably led to better performance but risked being too invasive.
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