Controlling hospital acquired infections: an in-silico framework

Hospital-acquired infections (HAIs) of Clostridioides difficile and multidrug-resistant organisms such as carbapenem-resistant Enterobacteriaceae pose a significant burden to modern healthcare systems. Our goal is to develop an in-silico modeling framework for prediction of incidence rates and patient risk to healthcare-associated pathogens and evaluate interventions to control their spread. We will develop an agent-based modeling framework for pathogen transmission using high-dimensional clinical and network data and will harness it using machine learning to predict patients at risk of colonization and infection. We will also examine the socio-economic factors involved in HAI transmission through infection control measures across hospitals. The financial support by GIDI will bring together a multi-disciplinary team of researchers that includes Drs. Anil Vullikanti and Achla Marathe and infectious disease clinicians Drs. Costi Sifri and Gregory Madden to develop training sets and detailed simulations that will be the cornerstone of this project. The team hopes to explore multiple extramural funding opportunities to NSF and NIH to continue this work.