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Public Summary
We aim to use mathematical modeling and machine learning approaches to build decision-making technologies to improve the risk assessment, prevention, and control of healthcare-associated infections (HAI) and antimicrobial resistant infections (ARI).
Our proposed technologies will account for spatial and temporal dynamics, provide continuous, real-time feedback to clinicians, and are robust to changes in risk factors and disease prevalence over time. We specifically focus on Staphylococcus areus and Clostridioides difficile infections.
Department/Financial Control Point:
Francis I. Proctor Foundation