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abstract: |
Epidemiology studies have revealed that variability in disease incidence across populations can be attributed to differences in risk factors, which are in turn attributable to differences in the prevalence of diseases in different environments. In other words, one observes a particular factor-disease link that holds for one population, but does not hold for another. In this paper, we formalize this concept and describe how it can be modeled in a mixture model framework. We propose a Bayesian model that enables us to efficiently learn the underlying factor-disease structure from data, by simultaneously learning the model structure and the effect of each risk factor. After learning the model, we can predict the probability of a disease given its own risk factors and environmental covariates. We illustrate the model’s performance by studying two case studies, in influenza and diabetic retinopathy.\
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