Well, I Got COVID-19

Well, it was bound to happen eventually - I got COVID-19. It hasn’t been a fun ride, but likely would have been worse without my two Pfizer vaccinations and Moderna booster. Symptoms started the day before Thanksgiving (11/24). Initially it was a really bad cough, chills, and a fever of 102F. Generally I’m very fortunate in my health, and this is easily the most sick I’ve been in several years. Unfortunately two self-administered rapid antigen tests and another at urgent care reported negatives (presumably false-negatives), so I was given Tamiflu and sent on my way. On a hunch related to some very bland tasting tikka masala, I took another test on the 27th which came back positive. Since then I’ve been testing myself daily, and today I finally tested negative.

In addition to the expected symptoms, the jump in my resting heartrate was pronounced. As the graph below shows, on the first day (11/24) my resting heartrate jumped from it’s normal value of 51 bpm to 65 bpm, and it’s been slowly coming back to normal ever since. I don’t know if this is because of the impact of COVID-19 on the heart, or simply because my sleep was poor (and deep sleep is normally where the lowest HR occurs). I suspect the former, as lately I’ve been sleeping well but my heartrate hasn’t quite returned to normal.

Because of this experience I was curious about the false-positive and false-negative rates for rapid antigen tests. I’ve had a bit of trouble finding this data so I thought I’d compile what I learned…

Study #1, False Positives

False-Positive Results in Rapid Antigen Tests for SARS-CoV-2

Summarized findings:

Study #2, False Negatives

Accuracy of Rapid Antigen vs Reverse Transcriptase–Polymerase Chain Reaction Testing for SARS-CoV-2 Infection in College Athletes During Prevalence of the Omicron Variant

Summarized findings:


To makes sense of this, we need to be Bayesians.

First, what are the chances that if I test positive I’m actually well?

P(well|positive_test) = P(positive_test|well)*P(well)/P(positive_test)

Sticking in some numbers from study #1:

Probabiity Fraction Decimal
P(positive_test|well) 462/(462+902086) 0.00051
P(well) (902086+462)/903408 0.999
P(positive_test) 1322/903408 0.0015
P(well|positive_test) 462/(462+902086)*(902086+462)/903408/(1322/903408) 35%

Second, what are the chances that if I test negative that I’m actually sick?

P(sick|negative_test) = P(negative_test|sick)*P(sick)/P(negative_test)

Sticking in some numbers from study #2:

Probabiity Fraction Decimal
P(negative_test|sick) 27/(46+27) 0.37
P(sick) (46+27)/723 0.10
P(negative_test) 677/723 0.94
P(sick|negative_test) 27/(46+27)*(46+27)/723/(677/723) 4.0%

Well, I was sick and tested negative, so either:

In closing…

As of today my symptoms are largely gone, only a small lingering cough which mostly comes out when I’m running or walking. Before COVID I had been increasing my running mileage, and it’s a bit of a setback to my training to have taken so long off, and still not be 100%. Regardless, I’m happy to be done with it, and grateful for all the medical technologies which made my life easier.

Written by David Friedman on 04 December 2022