I wrote an article on LessWrong about how QS can help in the first against COVID-19: https://www.lesswrong.com/posts/6kj6cbcsMQFt4ntd9/using-the-quantified-self-paradigma-for-covid-19
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Note that “predict” in statistics doesn’t necessarily mean “predict in advance”. The linked Fitbit study for examples makes no claims that they were able to do latter.
Being able to get early warnings at the individual level would be useful; the challenge is that things like body temperature are affected by many factors, and by the time changes are large enough to rise above the noise, your internal alarms will have gone off already…
Testing everybodies temperature and mistakenly forbid people who have high temperatures for not COVID-19 related reasons from being outside is a lot less intrusive then fordidding everybody to be outside.
That seems to be partly what allows South Korea to deal with the crisis without the lock-downs we do in Europe at the moment.
The point where we are now, is that as a community we might provide a way that’s better then simply using the contactless thermometers in the way they are used in South Korea and China.
If you are going use body temperature to forbid people from being outside, a more Quantified-Self approach where you factor in a person’s normal temperature range would certainly let you set more aggressive thresholds.
But you’d need to be smart about this, so you don’t end up banning a third or so of the female population in reproductive age from going outside 
Just today @madprime & I launched a small data collection effort to see if there’s any interesting signal that could be used for those predictions: https://quantifiedflu.org/
If there’d be enough data collected one might even be able to see if there’s personalized thresholds 
why low rider?
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Here is a nice story on COVID-19 and how self-tracking a family member’s illness help in achieving a positive outcome: https://edition.cnn.com/2020/05/02/health/coronavirus-uk-elderly-patient-intl-gbr/index.html
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Our team at Duke has been running a study since March to determine how smart watch data 
can be used to predict COVID-19 infection and infection trajectory. We are excited to share two blog posts with the QS community. We are recruiting additional study participants and encourage the QS community to join and share with their networks. We are particularly looking for anyone who has had a COVID-19 or flu infection or suspected infection 
and owns a smart watch . Please reach out to us at covidentify@duke.edu with any questions, comments, or suggestions! Thank you!
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Welcome! Great to have you on the forum, where can we read the blog posts, they definitely have interest for people posting here.
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Thanks Gary! Glad to be here and a part of this community!
“Empowering the Quantified Self in a COVID World” : Event on October 1, 2020 sponsored by Startup Health: https://healthtransformer.co/startup-health-to-host-virtual-showcase-empowering-the-quantified-self-in-a-covid-world-ac6a25b5c4ea