Modeling infectious network dynamics for surveillance, control, and prevention enhancement (Mindscape)

For UCSF Faculty and Staff: Please login via MyAccess to request access to update your Project.

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