Calibrating COVID (part 2)

Using n-dimensional space to determine what COVID is and is not

In Calibrating COVID (part 1) I used a machine learning approach (Multilayer Perceptron) to give me a handle on the reliability of COVID-19 designation for 11,156 in-hospital deaths occurring between 1 Feb ‘20 and 7 Dec ‘20 in a sizeable NHS Trust somewhere in England. We observed a reasonable correspondence between PCR test result and severely symptomatic COVID death (74.5% agreement) and poor correspondence between PCR test result and asymptomatic COVID death (22.15% agreement), though all hinged on what was meant by ‘symptomatic’. For this initial analysis I played a straight bat by relying on acute respiratory diagnoses to flag what is deemed to be a respiratory illness. But is COVID a respiratory illness?

As 2020 unfolded I sat scratching my head as clinical teams and physicians around the globe reported all manner of symptom. Diffuse alveolar damage I could understand; hair loss not so much. This led us down the path of cytokine storms, possibly toward the bradykinin hypothesis. …