Computational Epidemiology for Healthcare-Associated Infections
Speaker: Sriram Pemmaraju, University of Iowa
Abstract: Healthcare-associated infections (HAIs), such as those caused by Clostridioides difficile (C. diff) and methicillin-resistant Staphylococcus aureus (MRSA) pathogens, affect ~2 million patients in US hospitals each year. Particularly concerning are multi-drug resistant organisms that can be amplified in hospitals, transmitted to other hospitals and long-term or skilled-care facilities, and eventually exported to the community at large.
The interdisciplinary Computational Epidemiology group at the University of Iowa has been using math modeling and simulations, optimization and algorithms, sensor network and camera deployment, statistical models and inference, graph mining and network science, and machine learning, to understand the spread of HAIs. An important theme throughout our work is the use of multiple spatial scales to understand HAI spread. This talk will describe some of our recent projects while illustrating this theme.
Bio: Sriram Pemmaraju is a Professor in the Department of Computer Science at the University of Iowa. His primary research interests are in algorithms, specifically distributed algorithms, but he is quite interested in applying algorithmic ideas to computational epidemiology problems, especially to understand the role of space and architecture in the spread of HAIs in healthcare facilities. Sriram's research on HAIs is funded by the NIH, NSF, and the CDC, and he is one of the Iowa PIs participating in the CDC MInD – Healthcare.