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Configuration of the apps changed overnight #112
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Hi @pdehaye, thank you for your questions and your interest in the project. We strive to configure Immuni’s risk model to adhere as closely as possible to the requirements set forth by Italy’s Ministry of Health, in particular to the definition of “contatto stretto” (close contact): “a person that had a direct contact (face to face) with a COVID-19-positive person at a distance less than 2 meters and for at least 15 minutes.” Several experiments were conducted to calibrate the parameters of the algorithm. As more experiments are performed and we gather more information about the problem, it cannot be ruled out that the parameters will change again, just as they did on June 5. |
There is a lot to unpack in the statement above, especially in conjunction with the data shared by the DP-3T team (and others). Note that the Swiss team configures the app very differently: see the discussion here), or directly download their data from here. While they do have a bad attenuation threshold at 73dB, their choice of higher threshold to be passed to the API is 55dB, not 73dB. This is a very substantial difference. Statistics can definitely be misleading, so there is a distinct possibility that by considering the same data you would be led to vastly different configuration than the Swiss team, if you are optimizing for different functions (yours seems to be focused on just being better than pure luck). Alternatively you could somehow have obtained very different data, which naturally would lead you to different conclusions. Hence my question to you: does your data look anything like the Swiss' ? |
This apparently made some little news in Italy. |
The "distance measurement" is still present in the official documentation. https://github.com/immuni-app/immuni-documentation#product @immunistaff @immuniteam correct that and issue an official statement of said modification. |
How can you mathematically minimize false positve and false negative at the same time by acting on the threshold? |
@Seio I assume that the attenuation measurements are distributed according to two Gaussian (or normal) probability density functions; I further assume that the given intervals cover three standard deviations (so called 3σ).
In this simulation the threshold that minimizes the false positives and negatives is 71dBm. The above is theory, in practice you can have a set of "far" attenuation measurements and a set of "near" attenuation measurements and you can apply a classifier like for example a Naive Bayes one (see https://scikit-learn.org/stable/modules/naive_bayes.html) in order to get a threshold. |
@alessandro-gentilini that minimizes the sum of false positives and false negatives, not each simultaneously. |
Dear all, |
@immuniopensource , could you kindly clarify? |
As detailed here, the configuration of the apps changed overnight.
In short: there no longer seems to be any attempt at leveraging Bluetooth as a proxy for distance, only duration counts, and pretty much all the beacons heard.
Has there been any public communication or explanation on the topic?
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