- Distilling Mechanistic Models From Multi-Omics Data , (2023)
- Early coronavirus disease 2019 (COVID-19) pandemic effects on individual-level risk for healthcare-associated infections in hospitalized patients , INFECTION CONTROL AND HOSPITAL EPIDEMIOLOGY (2023)
- Early evaluation of the Food and Drug Administration (FDA) guidance on antimicrobial use in food animals on antimicrobial resistance trends reported by the National Antimicrobial Resistance Monitoring System (2012-2019) , ONE HEALTH (2023)
- Impacts of the COVID-19 pandemic on antimicrobial use in companion animals in an academic veterinary hospital in North Carolina , ZOONOSES AND PUBLIC HEALTH (2023)
- A review of epidemiological models of Clostridioides difficile transmission and control (2009e2021) , ANAEROBE (2022)
- Epidemiology of Plasmid Lineages Mediating the Spread of Extended-Spectrum Beta-Lactamases among Clinical Escherichia coli , MSYSTEMS (2022)
- Extensions of mean-field approximations for environmentally-transmitted pathogen networks , MATHEMATICAL BIOSCIENCES AND ENGINEERING (2022)
- Geographic disparities and determinants of COVID-19 incidence risk in the greater St. Louis Area, Missouri (United States) , PLOS ONE (2022)
- Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA) , BMC PUBLIC HEALTH (2022)
- Identifying patient-level risk factors associated with non-beta-lactam resistance outcomes in invasive MRSA infections in the United States using chain graphs , JAC-ANTIMICROBIAL RESISTANCE (2022)
The emergence and persistence of antimicrobial resistant pathogens are a serious threat to human and animal populations worldwide. Although antimicrobial use is known to select for antimicrobial resistance (AMR), resistance often persists in the absence of antimicrobial exposure. An improved understanding of the epidemiology of AMR in natural pathogen populations is needed. However, traditional epidemiological methods are not well-suited for understanding AMR risk factors because they do not account for the ecological and evolutionary processes that confound the expected association between antimicrobial exposure and AMR. Improved epidemiological methods that incorporate ecological and evolutionary principles are necessary for providing new insights on AMR, informing more effective AMR prevention strategies, and ultimately promoting the future efficacy of antimicrobial therapies. The long-term goal of this research is to develop a quantitative framework integrating ecological and evolutionary principles with epidemiological methods to advance existing knowledge of AMR risk among human and animal populations. This proposal aims to use commensal Campylobacter coli populations in commercial swine herds as a model system to investigate the ecology, evolution, and epidemiology of AMR. Prior studies found that C. coli from pigs reared under both conventional and antibiotic-free farms were resistant to fluoroquinolone and macrolide antibiotics, which is clinically significant as these drugs are used to treat human campylobacteriosis. The proposed research will build upon this previous work and accomplish the following aims: 1) identify and quantify host exposures and microbial genotypes associated with phenotypic resistance to fluoroquinolone and macrolide drugs among commensal C. coli in swine herds using probabilistic graphical models (i.e. chain graphs), and 2) quantify selection and bacterial fitness costs of genotypes associated with fluoroquinolone- and macrolide-resistance among natural C. coli populations in presence and absence of antimicrobial use, using phylodynamic methods. The expected outcome of this research is the development of a quantitative framework that can be applied to any host-pathogen system in which AMR is a threat to elucidate drivers of AMR selection and persistence. This proposal and mentorship team will provide training critical for the applicantÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s development as an independent clinician-scientist studying infectious disease epidemiology at the human-animal interface. The interdisciplinary research training plan will provide the applicant opportunities to develop skills in molecular epidemiology, computational biology and infectious disease epidemiology. The comparative clinical training plan will enable the applicant to gain experience in infectious disease management among both human and animal populations under the guidance of infectious disease physicians and veterinary epidemiologists. The training environment at North Carolina State University is ideal for the applicants cross-disciplinary training goals, as the applicant also has access to resources and opportunities at Duke University and University of North Carolina-Chapel Hill.
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.
Judicious antimicrobial use in veterinary medicine is important because improper antimicrobial use can contribute to the evolution of antimicrobial resistance in bacterial pathogens, which makes subsequent use of these drugs less effective in both human and veterinary medicine. There is very little on-the-ground information about veterinary cliniciansÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ antimicrobial use (AMU) practices in companion animal practice in the US veterinary medicine. To improve our understanding of antimicrobial use in dogs and cats, we propose to create a nationwide digital surveillance system to collect critical AMU data using existing electronic practice information management systems (PIMS) in collaboration with veterinary industry partners. The system will automatically harvest AMU and patient data from digital PIMS. The proposed system will harvest data collected in routine veterinary examinations from existing PIMS systems and therefore will not require any additional effort from practitioners to participate in the program. Natural language processing, a machine learning method used to classify unstructured text, will be used to review electronic medical records to determine patientsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ diagnosis. We aim to prototype the system in our native digital PIMS at North Carolina State UniversityÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s College of Veterinary Medicine Teaching hospital. We will then enroll additional private veterinary practices, including general practice, specialty hospitals, and emergency clinics, as sentinels and collect the same detailed PIMS data from a more representative set of clinics. Working closely with the sentinel clinics will provide a deep understanding of how our system operates in private clinics, and in the final stage we aim to expand the fully automated system to PIMS nationwide. The combination of sentinel clinics with the nationwide survey of clinics will create a powerful broad and deep surveillance system for antimicrobial use in veterinary clinics. A broad suite of AMU parameters will be estimated from this data, and the results reported to the FDA in an annual report. Additionally, we will share the data with other researchers through an web-based portal and GitHub repositories. This system will provide the critical data and analysis to understand veterinary AMU in the US.
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 metricsÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Âsuch as population-level fitness of resistant bacteria and the effect of resistant traits on fitnessÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Âare 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 modelsÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ applications for evaluating interventions such as antimicrobial stewardship and rapid testing. Our models and tools will be made available to the broader community.
Host behavior and pathogen-specific life history generate complex patterns of pathogen exposure that drive transmission heterogeneity. For enteric pathogens, the sources of transmission heterogeneity, including exposure, are not comprehensively characterized, nor well represented in mathematical models. The overreaching hypothesis of this project is that variation in pathogen exposure, as determined by the contact structure between host and its associated environments, and pathogen dynamics outside the host lead to important heterogeneities in infection traits and transmission that can be exploited for control purposes. The project will integrate data generated in a natural infection model system (Escherichia coli cattle) through experimental challenge studies, field transmission studies, and animal movement monitoring systems with mathematical models. The objectives are to (1) Characterize sources of heterogeneity for enteric disease transmission in cattle production systems: Data will be collected on the variation in pathogen dynamics in the host-associated environments, the contact structure among hosts, between host and specific environments as well as its environmental covariates (e.g. temperature, humidity), and the dose dependency of infection traits. Network analysis, time series analysis, and spatial statistics will be used to analyze the generated data (2) Develop a modeling framework to integrate and investigate the sources of heterogeneity for enteric pathogens for disease transmission. Individual-based models (IBM) will be developed to investigate the effect of the contact structure and variation of the exposure on population transmission patterns, and to identify dominant transmission pathways. Models will be parameterized and validated using data collected in longitudinal field studies, and the appropriateness of common assumptions and transmission functions of compartmental transmission models will be evaluated. (3) Assessment of the implications of exposure heterogeneity in enteric disease control. The developed models will be used to investigate and identify intervention strategies that could lead to reduction in transmission.
Antimicrobial resistance (AMR) is an important threat to human and animal health worldwide. The overuse and misuse of antimicrobials in human and veterinary medicine are major contributors to the recent rise in resistant infections; however, complete removal of antimicrobial use oftentimes does not revert resistance in bacterial populations (Andersson and Hughes 2011). In addition to antimicrobial use, ecological and evolutionary processes determine the persistence of AMR. Due to recent advances in sequencing technologies, the genomic data required to understand the eco-evolutionary dynamics of AMR are becoming increasingly available (Didelot et al. 2012). However, the quantitative methods needed to effectively integrate large volumes genomic data with epidemiological metadata require further development. This project proposes to build upon research I have previously conducted to develop an eco- evolutionary framework for evaluating the epidemiology, selection, and persistence of AMR among bacteria isolated from animal populations. We will apply the framework to investigate the persistence of resistance against antibiotics classified as critically important for veterinary and human medicine in Campylobacter coli from swine farms.
Many pathogens are able to infect hosts via the environment without direct contact between hosts. There is a critical need to better characterize environmental transmission in mathematical and computational models used for disease control. Our long term research goal is to develop a quantitative framework to advance disease transmission theory and control of pathogens with environmental pathways and reservoirs. Our central hypothesis is that the suitability of different model formulations for environmental transmission depends on the pathogen life history, the characterization of the environmental reservoir, and the processes associated with pathogen exposure. Using as a case study the transmission of Clostridium difficile in health-care settings, the following specific aims will be carried out: 1) Develop and analyze models that mechanistically address environmental transmission. Explicit functional forms that represent exposure pathways will be developed using queueing theory, and will be linked to the pathogen dynamics outside the host, and implications in disease dynamics will be investigated. 2) Develop and analyze spatially explicit models to address the role of spatial heterogeneity in environmental transmission. Agent-based models (ABM) will be developed to include spatial features of environmental transmission. Their aggregated behavior will be pproximated and compared with partial differential equations models. 3) Assess the implications of environmental transmission in disease control and surveillance using optimal control theory. Optimal control will be applied to ABMs to identify the preferred environmental strategies. The project will deliver a comprehensive understanding of the scaling up of environmental transmission, and the suitability of different model representations of environmental reservoirs, transmission, and control strategies that reduce environmental exposure. From a mathematical perspective, the project will address significant mathematical challenges associated to the analysis of ABMs, which are used across all biological disciplines, from molecular biology to ecology, namely how to reduce spatially explicit ABMs to mean-field dynamics, and how to transfer optimal control strategies from the mean-field models to the ABMs.
Dr. Lanzas will carry out the transmission modeling within the aim 3 (To determine how the amount of C. difficile shedding and environmental contamination impact risk of C. difficile transmission among patients admitted to HCT and leukemia units). She will set up the model and the code necessary to fit the transmission model to the collected data and will carry out simulations to investigate interventions once the model is fitted. She will be assisted by a postdoctoral researcher.