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Institute of Applied Data Science



Coronavirus: country comparisons are pointless unless we account for these biases in testing

3 April 2020

What a causal model would look like
What a causal model would look like
Researchers from the Institute of Applied Data Science at Queen Mary have developed an initial prototype “causal model” whose structure is shown in the Coronavirus figure below. The links between the named variables in a model like this show how they are dependent on each other. These links, along with other unknown variables, are captured as probabilities. As data are entered for specific, known variables, all of the unknown variable probabilities are updated using a method called Bayesian inference.

The model shows that the COVID-19 death rate is as much a function of sampling methods, testing and reporting, as it is determined by the underlying rate of infection in a vulnerable population.

This article was written by the following at Queen Mary University of London - Norman Fenton, Professor of Risk and Information Management; Magda Osman, Reader in Experimental Psychology; Martin Neil, Professor in Computer Science and Statistics & Scott McLachlan, Postdoctoral Researcher in Computer Science.