
- The COVID-19 testings tend to focus on already hospitalized people, not accounting for the people with mild symptoms and the asymptomatics leading to selection bias.  
- The differences in death rates across countries may not be attributed to clinical, demographic, and environmental factors.  
- Together with random sampling/testing, the Bayesian Network model allows causal explanations from data for better predictions.  


Summary of "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing"

Norman E. Fenton, Martin Neil, Magda Osman & Scott McLachlan
Journal of Risk Research, DOI: 10.1080/13669877.2020.1756381
Received: April 7, 2020
Accepted April 10, 2020
Published online: April 24, 2020
https://www.tandfonline.com/doi/full/10.1080/13669877.2020.1756381


University of Michigan - Ann Arbor

- COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing

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