Healthcare-associated infections (HAIs) are a major source of mortality and morbidity and affect about two million patients each year. Within hospitals, pathogens like Clostridioides dif?cile and methicillin-resistant Staphylococ- cus aureus (MRSA) are routinely transmitted to and among hospitalized patients: also of particular concern are multidrug resistant organisms (MDROs), because HAIs caused by these pathogens are increasingly dif?cult to treat. These infections can be ampli?ed in hospitals, transmitted to other hospitals, long-term or skilled-care facil- ities, and then, eventually, to the community at large. Developing effective interventions to prevent the spread of HAIs remains an important public health goal, and demands some means by which the effectiveness of proposed interventions (or combinations thereof) can be ef?ciently and inexpensively compared. In ?elds where experi- ments are not possible, mathematical models and simulations can yield insight into how a system responds to the intervention under study. The overarching theme of this project is to overcome existing barriers for modeling the spread of HAIs. We hypothesize that high-?delity models derived from complex, ?ne-grained data can be used to understand the acquisition and transmission of HAIs within and across healthcare facilities. Simulations based on our models can be used to compare alternative interventions and provide effective and practical guidance for how to reduce the transmission of MDROs and other pathogens capable of causing HAIs.
Healthcare-associated infections (HAIs) affect about two million patients in American hospitals each year. Of particular concern are multidrug resistant organisms (MDROs) that can be ampli?ed in hospitals, transmitted to other hospitals, long-term or skilled-care facilities, and then, eventually, exported to the community at large. The goal of this project is to build high-?delity models of HAI transmission within and across healthcare facilities from large, complex, ?ne-grained data sources. Simulations based on these models can be used to better understand the transmission of HAI pathogens, compare alternative infection control interventions, and provide effective and practical guidance on best practices to reduce the transmission of MDROs and other HAI pathogens.
