About

Cristina Lanzas is associated with the College of Veterinary Medicine at NC State University, where she is involved in research and academic activities. Her work focuses on areas such as population health and pathobiology, with specific research areas including infectious disease, epidemiology, and global health. She is part of research centers, consortia, and institutes that conduct diagnostic and research labs, including biosafety level 3 (BSL3) biocontainment facilities, clinical sciences labs, and molecular biomedical sciences laboratories. Her contributions support the college's mission to advance veterinary medicine through research, education, and clinical practice, emphasizing the importance of understanding disease mechanisms, improving diagnostics, and promoting animal and human health.

Research topics

• Computer Science
• Artificial Intelligence
• Medicine
• Telecommunications
• Microbiology
• Intensive care medicine
• Mathematics
• Biology
• Mathematical optimization
• Mathematical analysis
• Pathology
• Applied mathematics
• Environmental health
• Virology
• Geography
• Medical emergency
• Genetics
• Statistical physics
• Meteorology
• Physics
• Geometry
• Simulation
• Nursing
• Internal medicine

Selected publications

• Quantifying the contributions of asymptomatic and symptomatic colonized patients to <i>Clostridioides difficile</i> acquisition in oncological units

  medRxiv · 2026-05-12

  articleSenior author

  Abstract Objective Leukemic and hematopoietic cell transplant patients have one of the highest incidences of C. difficile infection (CDI). While CDI patients are considered the primary source of transmission, asymptomatic colonized patients (AC) can progress to CDI or contribute to in-unit transmission. We aim to quantify the roles of CDI and AC patients in C. difficile importation and transmission within oncological units. Design Prospective cohort study Setting Two leukemia and HCT transplant units in a large tertiary care hospital in the US Methods We developed a stochastic, individual-based network model to simulate C. difficile acquisition and transmission. Data from cultures and nucleic acid amplification testing (NAAT) obtained at admission and weekly, and toxin enzyme immunoassay (EIA) tests used for CDI diagnosis were used to calibrate the model. Healthcare worker room assignments informed the network structure. Key parameters were estimated via particle filtering. Results The model reproduced observed weekly test counts and transmission pairs. AC patients were the primary source of new colonizations: 51% were due to importation (of those, 88% were admitted as AC), and 49% were due to transmission (AC was the source in 92% of transmissions). Sensitivity analysis showed that these findings were most influenced by the colonization rate and rates of environmental contamination and cleaning. Conclusions These findings reinforce the role of AC, particularly via admission importation, in sustaining C. difficile transmission in high-risk hospital settings. Infection control focused on CDI effectively reduced onward transmission, as indicated by CDI’s low contribution to new colonizations.

  PublisherDOI

• P-1022. Modeling the Impact of Sampling Intensity on Observing C. difficile Transmission in Healthcare Settings

  Open Forum Infectious Diseases · 2026-01-01

  articleOpen accessSenior author

  Abstract Background Whole genome sequencing (WGS) is increasingly used to investigate healthcare-associated infections, yet prior studies have only been able to link a small percentage of C. difficile cases. It remains unclear whether this is due to low sampling intensity or because transmission events are occurring outside of monitored healthcare settings. Methods We developed a stochastic transmission model integrated with an observation model to simulate C. difficile spread and case detection under varying sampling intensities. The model was originally fitted to data from a six-month prospective cohort study with weekly active surveillance sampling. Retrospective analyses of the data were combined with prospective simulation-based analyses using synthetic testing datasets at three sampling intensities (standard, intermediate, and intensive). Transmission trees were constructed to examine transmission events’ temporal, spatial, and network-scale dynamics. Results At the standard sampling level (weekly testing), an estimated 21.9% of cases and only 6.8% of transmission pairs were observed. Increasing sampling frequency to daily testing improved observability to 65.0% of cases and 55.2% of transmission pairs. Transmission events occurred across diverse time scales, with 95.5% of modeled transmission trees containing at least one pair separated by four or more weeks, highlighting the pathogen's persistence in healthcare environments. Cluster analyses revealed that over half of transmission trees contained at least one cluster linking 15 or more new colonizations. Conclusion Our findings demonstrate that substantial transmission occurs but that weekly sampling is insufficient to capture most C. difficile transmission events. Enhanced sampling intensity substantially improves the observability of cases and transmission pairs, underscoring the need for more comprehensive surveillance protocols. Additionally, modeling approaches can augment genomic epidemiology by illuminating unobserved transmission pathways, allowing for improved tree reconstruction. Disclosures All Authors: No reported disclosures

  PublisherDOI

• One Health antimicrobial resistance modelling: from science to policy

  Science in One Health · 2026-01-01 · 1 citations

  articleOpen access

  Modern human and veterinary medical interventions to combat infectious diseases depend on the continued efficacy of antimicrobial drugs. Antimicrobial resistance (AMR) is the quintessential One Health challenge threatening human and animal health and welfare and has environmental effects on ecological communities in soil and water. Policy guidance on AMR needs to anticipate the likely outcomes of different interventions and courses of action. For that, transdisciplinary collaboration to understand the development, spread, and impacts of AMR is crucial. We report the outcomes of an international workshop that explored the challenges and opportunities for modelling AMR across One Health settings. They include the disparity of data quality and availability, the broader knowledge gaps in key areas such as the relationship between antimicrobial use (AMU) and AMR, and the difficulty of defining AMR as a single outcome given its heterogeneity. Differences between microbial species, resistance genes, environments (i.e., terrestrial vs. aquatic) and practical settings (e.g., human clinical vs. veterinary, or individual vs. population) complicate the generalizability of model applications. However, synoptic AMR metrics are necessary to cut through the complexity for policymaking. We discuss the status of AMR modelling with respect to a hierarchy of modelling evidence for decision-making. Finally, we consider learnings from modelling other wicked environmental challenges to develop a pragmatic approach to inform policy.

  PublisherDOI

• Risk factors for fluoroquinolone- and macrolide-resistance among swine <i>Campylobacter coli</i> using multi-layered chain graphs

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-16

  preprintOpen accessSenior authorCorresponding

  Abstract Campylobacter spp . resistant to fluoroquinolones and macrolides are serious public health threats. Studies aiming to identify risk factors for drug-resistant Campylobacter have narrowly focused on antimicrobial use at the farm level. Using chain graphs, we quantified risk factors for fluoroquinolones- and macrolide-resistance in Campylobacter coli isolated from two distinctive swine production systems, conventional and antibiotic-free (ABF). The chain graphs were learned using genotypic and phenotypic resistance data from 1082 isolates and host exposures obtained through surveys for 18 cohorts of pigs. The gyrA T86I point mutation alone explained at least 58 % of the variance in ciprofloxacin minimum inhibitory concentration (MIC) for ABF and 79 % in conventional farms. For macrolides, genotype and host exposures explained similar variance in azithromycin and erythromycin MIC. Among host exposures, heavy metal exposures were identified as risk factors in both conventional and ABF. Chain graph models can generate insights into the complex epidemiology of antimicrobial resistance by characterizing context-specific risk factors and facilitating causal discovery. Author summary Antimicrobial resistance is influenced by multiple factors, including exposures to selecting agents, such as antibiotics, antiseptics, or heavy metals, and factors affecting the transmission of resistant pathogens, such as biosecurity and hygiene measures. Understanding what specific factors are associated with resistance in a given context is challenging. We developed an approach based on probabilistic graphical models to investigate context-specific antimicrobial resistance risk factors. We applied the approach to Campylobacter coli isolated from pigs in antibiotic-free and conventional farms. We demonstrated how for fluoroquinolones, risk factors were similar across both types of farms, but risk factors for macrolides were different across settings.

  PublisherDOI

• Effects of regional diversity on antimicrobial prescribing in dogs and cats in North Carolina from 2019 to 2020

  JAC-Antimicrobial Resistance · 2025-04-29 · 1 citations

  articleOpen accessSenior author

  Background: Data on antimicrobial use (AMU) in companion animals is lacking in the United States, along with information regarding drivers of such prescribing. Objectives: To describe trends in AMU for dogs and cats in North Carolina (NC) over geography, urbanicity, time, and patient sex from 1 January 2019 to 31 December 2020 and evaluate the influence of summarized measures of social vulnerability and the COVID-19 pandemic on prescribing practices. Methods: In cooperation with IDEXX Laboratories, Inc. (IDEXX), we collected prescribing data from dogs and cats treated at 389 practices during 2019 and 2020. Practices were stratified by geographic region (mountain, piedmont, coastal plain) and urbanization (rural, urban). Social vulnerability was measured using the CDC published Social Vulnerability Index (SVI) data and was summarized for each of the six areas. Poisson family models were used to estimate prescribing rates and rate ratios for independent variables, normalized by the total number of monthly patients. Results: Combination beta-lactam agents, fluoroquinolones, nitroimidazole, and cephalosporins were the most prescribed drug classes. Region and urbanicity only significantly affected prescribing rate for first-generation cephalosporins in dogs, and prescribing rates did not significantly change during the COVID-19 pandemic. Patient sex was the most consistently significant independent variable for prescribing rates. Conclusions: The current study found that prescribing rates of the most common antimicrobials in dogs and cats were fairly uniform with some increased prescribing in rural and vulnerable areas of the state.

  PublisherOA PDFDOI

• Genomic Dynamics of the Emergent <i>Candida auris</i> : Exploring Climate-dependent Trends

  Open Forum Infectious Diseases · 2025-07-25 · 2 citations

  articleOpen accessSenior author

  Abstract Candidozyma auris (formerly known as Candida auris) (C auris) is a multidrug-resistant fungal pathogen that has emerged as a significant threat to global health. Shifts in climatic conditions may be driving its adaptation and pathogenicity. Its increased ability to tolerate higher temperatures has been suggested as the first adaptation led by anthropogenic climate change in a pathogenic organism. In this study, we analyzed 801 whole-genome sequences isolated in clinical settings from the New York-New Jersey region from 2016 to 2024. Using Bayesian hierarchical logistic regression models, we identified previously described antifungal resistance genes, their associated point mutations, heat tolerance genes, and their link with key climatic variables using mixed-effects logistic regression models. Our analysis revealed that the heat tolerance genes HSP90 and HSP104 were present in &amp;gt;98% of isolates. Among the antifungal resistance-related genes, several showed significant associations with climatic variables, particularly with precipitation and temperature. Elevated precipitation was consistently linked to increased prevalence in antifungal resistance genes and their associated point mutations, suggesting that elevated moisture levels may promote favorable conditions for fungal growth and biofilm formation. Additionally, the interaction between climatic variables showed a stronger association with the presence of resistance genes, evidencing the multifactorial nature of climate change in shaping pathogen adaptations. These findings emphasize the influence of climatic variables on the resistome of C auris, which is crucial for predicting the spread and resistance patterns of C auris as climate change continues.

  PublisherOA PDFDOI

• Influence of Sequencing Technology on Pangenome-level Analysis and Detection of Antimicrobial Resistance Genes in ESKAPE Pathogens

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-11 · 1 citations

  preprintOpen accessSenior author

  ). Utilizing a dataset of 1,385 whole genome sequences and applying commonly used bioinformatic methods in bacterial genomics, we assessed the differences in genomic completeness, pangenome structure, and AMR gene and point mutation identification. Illumina presented higher genome completeness, while ONT identified a broader pangenome. Hybrid assembly outperformed both Illumina and ONT at identifying key AMR genetic determinants, presented results closer to Illumina's completeness, and revealed ONT-like pangenomic content. Notably, Illumina consistently detected more AMR-related point mutations than its counterparts. This highlights the importance of method selection based on research goals. Differences were also observed for specific gene classes and bacterial species, underscoring the need for a nuanced understanding of technology limitations. Overall, this study reveals the strengths and limitations of each approach, advocating for the use of Illumina for common AMR analysis; ONT for studying complex genomes and novel species, and hybrid assembly for a more comprehensive characterization, leveraging the benefits of both technologies. Impact Statement: This study provides a comprehensive comparison of short-read (Illumina) and long-read (Oxford Nanopore Technologies, ONT) sequencing technologies in the context of antimicrobial resistance (AMR) detection in ESKAPE pathogens. By analyzing a large dataset of 1,385 whole genome sequences, the research offers valuable insights into the strengths and limitations of each approach, as well as the benefits of the novel approach of hybrid assembly. These findings have broad utility across microbiology, genomics, and infectious disease research. In particular, they apply to the work of researchers and clinicians dealing with AMR surveillance, investigation into outbreaks, and bacterial genome analysis. Given the nuance with which technological differences in genomic completeness, pangenome structure, and AMR determinant detection have been explored in this study, it is a good basis for informed method selection for future research. While the output represents an incremental advance, its significance lies in its practical implications. It thus enables researchers to take more reasonable decisions in designing genomic studies of bacterial pathogens by showing the complementarity of various sequencing approaches and their specific strengths. This could lead to more accurate and comprehensive detection of AMR, which would contribute ultimately to improved antibiotic stewardship and public health strategies. Data Summary: The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Repositories: All the sequences used for this study are publicly accessible from GenBank, and their individual IDs are disclosed in Supplementary Table 1.

  PublisherOA PDFDOI

• Influence of Sequencing Technology on Pangenome-Level Analysis and Detection of Antimicrobial Resistance Genes in ESKAPE Pathogens

  Open Forum Infectious Diseases · 2025-03-26 · 3 citations

  articleOpen accessSenior author

  Abstract As sequencing costs decrease, short-read and long-read technologies are indispensable tools for uncovering the genetic drivers behind bacterial pathogen resistance. This study explores the differences between the use of short-read (Illumina) and long-read (Oxford Nanopore Technologies [ONT]) sequencing in detecting antimicrobial resistance (AMR) genes in ESKAPE pathogens (ie, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae). Utilizing a dataset of 1385 whole genome sequences and applying commonly used bioinformatic methods in bacterial genomics, we assessed the differences in genomic completeness, pangenome structure, and AMR gene and point mutation identification. Illumina presented higher genome completeness, while ONT identified a broader pangenome. Hybrid assembly outperformed both Illumina and ONT at identifying key AMR genetic determinants, presented results closer to Illumina's completeness, and revealed ONT-like pangenomic content. Notably, Illumina consistently detected more AMR-related point mutations than its counterparts. This highlights the importance of method selection based on research goals, particularly when using publicly available data ranging a wide timespan. Differences were also observed for specific gene classes and bacterial species, underscoring the need for a nuanced understanding of technology limitations. Overall, this study reveals the strengths and limitations of each approach, advocating for the use of Illumina for common AMR analysis, ONT for studying complex genomes and novel species, and hybrid assembly for a more comprehensive characterization, leveraging the benefits of both technologies.

  PublisherOA PDFDOI

• Risk factors for fluoroquinolone- and macrolide-resistance among swine Campylobacter coli using multi-layered chain graphs

  PLoS Computational Biology · 2025-08-13 · 1 citations

  articleOpen accessSenior authorCorresponding

  Campylobacter spp. resistant to fluoroquinolones and macrolides are serious public health threats. Studies aiming to identify risk factors for drug-resistant Campylobacter have narrowly focused on antimicrobial use at the farm level. Using chain graphs, we quantified risk factors for fluoroquinolones- and macrolide-resistance in Campylobacter coli isolated from two distinctive swine production systems, conventional and antibiotic-free (ABF). The chain graphs were learned using genotypic and antimicrobial susceptibility data from 1082 isolates and host exposures obtained through surveys for 14 cohorts of pigs. The gyrA T86I point mutation alone explained at least 58% of the variance in ciprofloxacin minimum inhibitory concentration (MIC) for ABF and 79% in conventional farms. For macrolides, genotype and host exposures explained similar variance in azithromycin and erythromycin MIC. Among host exposures, heavy metal exposures were identified as risk factors in both conventional and ABF. Chain graph models can generate insights into the complex epidemiology of antimicrobial resistance by characterizing context-specific risk factors and facilitating causal discovery.

  PublisherDOI

• Quantifying the genomic determinants of fitness in <i>E. coli</i> ST131 using phylodynamics

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-06-10 · 3 citations

  preprintOpen access

  Abstract Antimicrobial resistant pathogens such as Escherichia coli sequence type 131 (ST131) pose a serious threat to public health globally. In the United States, ST131 acquired multiple antimicrobial resistance (AMR) genes and rapidly grew to its current high prevalence in healthcare settings. Notably, this coincided with the introduction and widespread use of antibiotics such as fluoroquinolones, suggesting AMR as the major driver of ST131’s expansion. Yet, within ST131, there remains considerable diversity between strains in resistance profiles and their repertoires of virulence factors, stress factors, plasmids, and other accessory elements. Understanding which genomic features contribute to ST131’s competitive advantage and their relative effects on population-level fitness therefore poses a considerable challenge. Here we use phylodynamic birth-death models to estimate the relative fitness of different ST131 lineages from bacterial phylogenies. By extending these phylodynamic methods to allow multiple genomic features to shape bacterial fitness, we further quantify the relative contribution of individual AMR genes to ST131’s fitness. Our analysis indicates that while many genomic elements, including various AMR genes, virulence factors, and plasmids, have all contributed substantially to ST131’s rapid growth, major increases in ST131’s fitness are largely attributable to mutations in gyrase A that confer resistance to fluoroquinolones. Author summary ST131 is a pandemic lineage of E. coli that has spread globally and is now responsible for a large percentage of blood and urinary tract infections that cannot be treated with many common antibiotics. While antibiotic resistance has undoubtedly given ST131 a competitive edge, the relative importance of resistance compared with other factors shaping a pathogen’s growth or transmission potential (i.e. fitness) is often difficult to measure in natural settings. Here, we present a method that allows us to look at the entire spectrum of factors determining a pathogen’s fitness and estimate the individual contribution of each component to pathogen’s overall fitness. Our results suggest that resistance to fluoroquinolones, a widely used class of antibiotics, provides ST131 with a disproportionately large fitness advantage relative to many other factors with more moderate fitness effects. Understanding what determines the fitness of ST131 therefore provides insights that can be used to curb the spread of resistance and monitor for emerging lineages with high pandemic potential due to shared fitness enhancing attributes.

  PublisherOA PDFDOI

Recent grants

• Exposure heterogeneity & environmental transmission dynamics of Escherichia coli

  NIH · $1.7M · 2015–2020

• Analytical pipelines for data and model integration: finding informed pathways for antimicrobial resistance control

  NIH · $2.1M · 2020–2025

• Modeling and control of environmentally transmitted pathogens

  NIH · $624k · 2014–2019

Frequent coauthors

• Erik R. Dubberke

  Washington University in St. Louis

  63 shared

• Yrjo T. Gröhn

  44 shared

• Kimberly A. Reske

  42 shared

• Suzanne Lenhart

  University of Tennessee at Knoxville

  40 shared

• Trevor S. Farthing

  North Carolina State University

  39 shared

• Jennie H. Kwon

  Washington University in St. Louis

  36 shared

• Carol Chenoweth

  University of Michigan–Ann Arbor

  34 shared

• Jessina Mcgregor

  University of Pittsburgh

  34 shared

Education

• Ph.D., Animal Science

  North Carolina State University

  2008

• M.S., Animal Science

  University of California, Davis

  2003

• B.S., Animal Science

  University of California, Davis

  2001

Awards & honors

• University Faculty Scholars