Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics

A person is labelled as having COVID-19 infection either from a positive PCR-based diagnostic test, or by a health professional’s assessment of the clinical picture in a process described by some as symptom screening. There is considerable fragility in the resulting data as both of these methods are susceptible to human biases in judgment and decision-making. In this article we show the value of a casual representation that maps out the relations between observed and inferred evidence of contamination, in order to expose what is lacking and what is needed to reduce the uncertainty in classifying an individual as infected with COVID-19.