And if ML wasn't redundant, you have no training set containing the relevant bio-markers and a binary variable indicating COVID-19 presence.
But I was thinking a little more macroscopically. We don't have any device that can see antibodies or anything as obvious in real time. Instead, we have to collect samples and test them separately in controlled environments while we look for specific markers we know to be signs of infection.
What ML does well though is see patterns in data we don't. Rather than find specific markers, I was thinking we could feed a series of different values into a model to see if a pattern emerged. We can't observe the virus directly, but we can measure the percentage makeup of different chemicals in someone's breath and see if there's a pattern in those we know to be infected vs those we know aren't -- the training set would be the pile of tests already conducted. Then we can apply that model to new data and test the results.
You don't know who is infected and who isn't. That was my second point about not having a training set.
Yeah - separate labs spread all over the world, using different protocols collecting different data points on different populations and answering to different companies and/or governments each of them with their own laws and regulations. You can't just "get all the data" in this case.