Using the Quantified Self paradigma for COVID-19

Petri Hollmén traveled to Tyrol on the 5th of March. He had a bottle of hand sanitizer with him, used it a lot and washed his hands like never before.

Sunday, the 8th he returned home to hear a day afterwards that Tyrol was declared a COVID-19 epidemic area. He decided to work from home given the higher risk of having been in an epidemic area. On Thursday the 12th he woke up feeling normal but his Oura ring measured that his readiness was down to 54 from being normally at 80-90 which was mostly due to having a 1°C elevated temperature at his finger at night.

Even though he felt normal, he went to the doctor and given that he was from an epidemic area, they decided to test him. He tested positive and went to self-quarantine for 14 days. He measured his temperature several times during the following day and it always came back with 36.5°C. The Oura ring provided evidence that led to his diagnosis that wouldn’t have been available otherwise.

While he didn’t have true fever as defined by the official gold standard he did have a kind of clinical relevant fever. It’s my impression that our medical community is too focused on their gold standards that are based on old and outdated technology like mercurial thermometers.

Even when new measurements like nightly finger temperature don’t match with the gold standard there are still cases where the information allows for better clinical decision making.

Today, we have cheap sensors and machine learning that provide us with a different context of making medical decisions then going to the doctors office.

Testing by doctors is very important in the fight against COVID-19 but people need to know when it’s time to go to the doctor. Hollmén needed his Oura to know that it was time to get tested professionally.

We need to get good at catching cases of COVID-19 as fast as possible when they happen in the wild if we want to avoid that millions die without us choking our economy by long-term quarantines.

Analysis of Fitbit users found that their resting heart rate and total amount of sleep can be used to predict the official state numbers for influenza-like illness.

It’s very likely that lower heart rate variance and a higher minimum of the nightly heartrate happens in at least some of the COVID-19 cases. Unfortunately, the WHO is stuck in the last century and the official symptoms charts tell us nothing about how common either of those metrics are in COVID-19 patients. Lack of access to those metrics in the official statistics means it’s harder for people who have an Oura Ring, an Apple watch or another device that can measure nightly heartrate to make good decisions about when to go to the doctor or self-quarantine.

Given that Apple sold around 50 million Apple watches between 2018 and 2019, a sizable portion of people could make better decisions if we would have more information about how COVID-19 affects heart rate.

Even more people have access to a smart phone with a decent camera. Having a sore throat is a typical symptom for many virus infections like COVID-19 and a good machine learning algorithm could produce valuable data from those images.

A priori it’s unclear about how much we can learn from such pictures. If a throat of a patient is red due to inflammation a doctor who looks at it, can’t distinguish whether it’s due to snoring or a virus infection.

If a machine learning algorithm could have access to a steady stream of daily imagine of a person’s throat the algorithm could understand a person’s baseline and use that insight to factor out the effects of snoring.

When the gold standard of diagnosing the throat is to look at one image at a particular point in time at the doctor’s office there’s potentially a big improvement to be gained by looking at a series over multiple days. We don’t know how useful such a diagnostic tool is before building it.

Ideally, users of a new app would take an image of their throat every morning after getting up and every evening before going to sleep. They would also measure their temperature with a normal thermometer at both points and enter information about subjective symptoms. If a person gets a proper COVID-19 test, they should also be able to enter the data.

At first we would train the machine learning algorithm to use the images to predict temperature. With enough users our algorithm can learn how the throat of a person having flu differs from their baseline whether or not they are snoring.

As we have more users and some of our users get COVID-19 lab tests our machine learning algorithm can learn to predict the test results directly. It’s the nature of advanced technology that we don’t know how powerful a tool is before it’s developed. Most clinical trials for new drugs find that they don’t live up to their promise.

We need more dakka for COVID-19. Creating an app that does the above function doesn’t cost much and the cost of the project should be worth the potential benefits of catching COVID-19 cases faster and thus preventing people from unknowingly infecting their friends.