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Found 4 papers, 1 papers with code
Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning
Based on sources of stability in the model, we posit that for human-sourced data and health prediction tasks we can combine environment and population information in a novel population-aware hierarchical Bayesian domain adaptation framework that harnesses multiple invariant components through population attributes when needed.
Deep Landscape Features for Improving Vector-borne Disease Prediction
We then incorporate landscape features from satellite image data from Pakistan, labelled using the CNN, in a well-known Susceptible-Infectious-Recovered epidemic model, alongside dengue case data from 2012-2016 in Pakistan.
Population-aware Hierarchical Bayesian Domain Adaptation
Population attributes are essential in health for understanding who the data represents and precision medicine efforts.
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats.
