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 proposal is to develop models that more explicitly address resistance traits and modeling tools that support the identification of transmission sources and pathways for HAI. We will use the models to further identify HAI sources and evaluate and optimize interventions. In particular, we will address the following thematic areas: antimicrobial resistance (A), surveillance (A), genomics (B), and simulation of epidemiological studies (B). We have assembled an interdisciplinary group of researchers with expertise in infectious disease modeling, HAI hospital epidemiology and clinics, applied mathematics, and genomics located at North Carolina State University, Washington University (WU) and University of Tennessee. We plan to build on our previous and current collaborations among this team to: develop modeling approaches for addressing HAI transmission; extend phylodynamics methods; and model antimicrobial resistance dynamics. The CDC-Epi Center at WU and Barnes-Jewish Hospital in St. Louis, Missouri, will be the main source of data. Additionally, we will use nation-level publicly available data sources. We will carry out the following aims: 1) Develop improved approaches for inferring routes of acquisition of HAI and optimizing HAI surveillance and control: We will develop ward- and hospital- level network models that take into account the main routes of HAI acquisition and patient connectivity. We will apply optimization methods to identify environmental sampling protocols and cost-effective control strategies. 2) Phylodynamics to estimate fitness of antimicrobial resistance pathogens: We will apply and refine multi-type birth-death models to explore the fitness effects of a large number of antimicrobial-resistant traits on pathogen phylogenies, and speed the methods to quantify fitness for large numbers of strains, and 3) Multi-scale models for multidrug-resistant organisms: extended-spectrum beta-lactamase (ESBL)- producing Enterobacteriaceae as case study: We will develop both agent- and equation-based models that account for multi-scale dynamics of resistance transmission. This will greatly expand the models? applications for evaluating interventions such as antimicrobial stewardship and rapid testing. Our models and tools will be made available to the broader community.
Healthcare associated infections (HAI) are a significant source of preventable morbidity and mortality that disproportionately affect the elderly, immunocompromised, and critically ill. We plan to improve the prevention and control of HAI by advancing quantitative methods that address HAI transmission including multiscale models to quantify emergence and transmission pathways for resistant pathogens and phylodynamics methods that use sequence data to quantify the effect of resistant traits on bacteria fitness.
