Routinely-collected data generated in surveillance and diagnostic laboratories and medical records contain information valuable for understanding the transmission of antimicrobial resistant pathogens. This information can be harnessed to inform transmission models and to design and evaluate mitigation strategies. However, current modeling approaches often focus on addressing one resistance on a pathogen at a time which leads to key features of resistant pathogen dynamics such as multidrug resistance and pathogen interactions to not be commonly addressed. In order to capitalize on the data being generated via surveillance and diagnostic activity, we propose to carry out the following research activities: 1) we will develop graphical models to integrate multiple data streams (phenotypic, genotypic resistances and metadata) to support analysis and visualization of complex resistant patterns and joint distribution of resistances, 2) we will apply and evaluate the analytical pipeline to data collected in national-level surveillance systems, 3) we will develop and evaluate agent-based models for pathogen transmission in health-care settings that incorporate multidrug resistance features and pathogen interactions and apply graphical modeling approaches to analyze and validate the models, and 4) we will disseminate the tools by creating open source packages and through website implementation. With the developed tools we will provide a path to quantify changes on complex resistance patterns over time or across sources, identify drugs that can lead to further selection of first choice drugs, identify cluster of risk factor for resistance and evaluate vaccination, antibiotic stewardship and heterogeneity interventions in the presence of pathogen and resistance interactions. The challenges of dealing with multiple streams of data and complex models are not unique to infectious disease research. The developed workflows will be applicable to a broad spectrum of biomedical research questions, particularly those that involve the collection of mixed data and simultaneous genotypic and phenotypic data. Similarly, agent-based models are increasingly used across all biomedical disciplines, from molecular biology to epidemiology, and therefore advances in their analyses can have a broader positive impact in multiple biomedical disciplines.
Antimicrobial resistance and Clostridioides difficile infections are important public health threats caused by the use of antibiotics. Interactions among resistances and among pathogens influence pathogen burden and effectiveness of the interventions against resistant pathogens. We will develop quantitative methods to harness information on resistance interactions from surveillance data and to evaluate resistance interactions using computational models to design and evaluate interventions.
