Relation
How Causal model (Bayesian Network) can better explain CFR
- The model (Figure 1) demonstrates that the COVID-19 death rate is not only affected by the observed variable (confirmed cases), but also influenced by other factors such as sampling methods, testing accuracies, and the definition of COVID-19 deaths.
- In the causal model, dependencies are expressed in arrows.
- (e.g.) 'population proportion with COVID-19 who die' is affected by both 'population demographics and environmental factors' and 'Quality of healthcare.'
- (e.g.) Similarly, 'Number of reported COVID-19 positive reported as dead' is dependent on (affected by) 'Population proportion with COVID-19 who die', 'Death Reporting Policy,' 'Number tested positive with symptoms who do not have COVID-19', 'Number tested positive with symptoms who have COVID-19,' and the 'Number tested without symptoms who test positive.'
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Updated 2020-04-28
Tags
SARS-CoV-2 (COVID-19)
Biomedical Sciences