Lately, there has been a lot of talk about the Corona Virus disease (Covid-19) and Italy is allegedly a hotspot. As my partner lives in Italy and is constantly bombarded by warnings about potential infections and other alarming news like that, I figured it would be appropriate to do some back-of-the-envelop calculations about this situation and put things in perspective a bit. After all, Bologna (the city where she lives) is not officially a "red zone" like Milan and a few other cities in the country.
For this analysis, I used Bayes' Theorem (see formula below) along with some figures I managed to dig up, regarding the virus in the greater Bologna area. The numbers may not be 100% accurate but they are the best I could find, while the assumption made was more than generous.
Namely, I used the latest numbers regarding the spread of the disease as the priors, while regarding the likelihoods (conditional probabilities regarding the test made) I had to use two figures, one from the Journal of Radiology to figure out the false positives rate (5 out of 167 or about 3%, in a particular study) and one for the true positive rate (aka precision), the aforementioned assumption, namely 99%. In reality, this number is bound to be lower but for the sake of argument, let's say that it's correct 99% of the time. Note that certain tests regarding the Covid-19 using CT scans can be as low as 80%, while the test kits available in some countries have even lower precision. For the priors, I used the data reported in the newspaper, namely around 40 for the greater Bologna area. The latter has a population of about 400 000 people (including the suburbs). So, given all that, what are the chances you actually have the virus if you do a test for it the result comes back positive?
Well, by doing the math on Bayes’ theorem, it can take the form:
P(infection | positive) = P(positive | infection) * P(infection) / [P(positive | infection) * P(infection) + P(positive | healthy) * P(healthy)]
As being infected and being healthy are mutually exclusive, we can say that P(healthy) = 1 – P(infection). Doing some more math on this we end up with this slightly more elegant formula:
P(infection | positive) = 1 / [1 + λ (1 / P(infection) – 1)] where λ = P(positive | healthy) / P(positive | infection).
Plugging in all the numbers we end up with: P(infection | positive) = 1 / (1 + 303) = 0.3% (!)
In other words, even if you do a proper test for Covid-19, and the test is positive (i.e. the doctor tells you “you’re infected”) the chances of this being true are about 1 in 300. This is roughly equivalent to rolling a triple 1 using 3 dice (i.e. you roll three dice and the outcome is 1-1-1). Of course, if you don’t test positive, the chances of you having the virus are much lower.
Note that the above analysis is for the city of Bologna and that for other cities you'll need to update the formula with the numbers that apply there. However, even if the scope of this analysis is limited to the greater Bologna area, it goes on to show that this whole situation that plagues Italy is more fear-mongering than anything else. Nevertheless, it is advisable to be mindful of your health as during times of changing weather (and climate), your immune system may need some help to ensure it keeps your body healthy, so anything you do to help it is a plus. Things like exercise, a good diet, exposure to the sun, keeping stress at bay, and maintaining good body hygiene are essential regardless of what pathogens may or may not threaten your well-being. Stay healthy!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.