Multi-scale modeling and phylodynamics for healthcare associated infections

Healthcare-associated infections (HAI) are a significant source of preventable morbidity and mortality. Transmission models for HAI are a cornerstone method to both understand pathogen spread and evaluate control interventions. Models have been particularly helpful in addressing transmission-blocking interventions, for elucidating the connectivity among facilities, and their implications for controlling HAI. Mechanisms underlying antimicrobial resistance, such as co-selection, have received less attention in transmission models. In addition, key metrics—such as population-level fitness of resistant bacteria and the effect of resistant traits on fitness—are often unknown. This limits our understanding of the complex relationship between antimicrobial drug use and resistance, as well as the effectiveness of interventions aimed at changing drug selection pressure. The objective of this project is to develop models that more explicitly address resistance traits and modeling tools that support the identification of transmission sources and pathways for HAI.

AR and HAI aims:

• Develop improved approaches for inferring routes of acquisition of HAIs and optimizing HAI surveillance and control
• Develop and apply methods to explore the fitness effects of antibiotic-resistant traits on pathogen phylogenies and speed the methods to quantify fitness for large numbers of strains
• Develop both agent- and equation-based models that account for multi-scale dynamics of resistance transmission to evaluate interventions to mitigate antibiotic resistance

COVID-19 aims:

• Quantify the impact of COVID-19 infections on healthcare burden and resources including the co-incidence of bacterial and fungal infections
• Project hospitalizations and healthcare use in a region by developing and implementing zip code level, risk-structured compartmental models of COVID-19 transmission
• Project the effects of varying patient management and flows in healthcare delivery by developing and implementing microsimulation models of healthcare resources and patient flows and agent-based models of HAIs to evaluate interactions between COVID-19 and HAIs

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