Domestic animals, wildlife, and the environment can serve as reservoirs and sources of antimicrobial-resistant (AMR) bacteria and antimicrobial resistance genes. These resistant bacteria and resistance genes can spread among animals, humans, and the environment through multiple pathways, including the food chain, direct animal contact, water, and other environmental routes. In some cases, transmission of AMR bacteria from animals or environmental sources can contribute to human infections and outbreaks. Therefore, understanding the molecular epidemiology and transmission dynamics of AMR-associated pathogenic bacteria across humans, animals, and the environment is essential for identifying reservoirs and transmission pathways and for informing the development of effective One Health strategies to prevent and control future outbreaks.

As summarized in the document attached to this application, the project aims to generate and analyze antimicrobial resistome profiles by using samples collected around east Tennessee (UT CVM, UT Hospital, UT dairy farm, and environment). I will be leading the project having Drs. Xiang Li and Michael Mahero as collaborators. Dr. Xiang Li has expertise in NanoPore sequencing and bioinformatic analysis. Dr. Mahero and I will be responsible for the sample collection and epidemiological analysis. Dr. Mahero has access to samples from CVM. I recently talked with two entomologists (Drs. Becky Trout-Fryxell and Charity Owing) who have fly samples that could be used as samples from the dairy and environment.

The collected samples will be subjected to Nanopore sequencing to identify AMR genes, characterize mutations and other genetic determinants associated with antimicrobial resistance, and determine the bacterial hosts carrying these genes. The resulting data will then be used for molecular epidemiological analyses to investigate the spread, persistence, and dynamics of AMR genes and their bacterial hosts in the region.

This study will generate a preliminary cross-site profile of antimicrobial resistance (AMR) genes across human, animal, and environmental interfaces in the Knoxville area. The pilot will leverage a One Health approach, focusing on transmission-relevant environments where resistant organisms and resistance determinants are likely to move among people, animals, wastewater, soil, and produce systems.

Whether a newly introduced pathogen fades out or expands depends partly on how protected the population is when the outbreak happens. That protection can arise from both biological immunity, acquired through infection or vaccination, and behavioral protection driven by awareness and risk perception. Both can wane over time, potentially creating periods when a population becomes increasingly vulnerable to outbreak expansion.
This seed builds on recent APPEX-related work examining how outbreak information, lived experience, individual concern, and protective behavior shape responses to infectious-disease threats (Silk et al. 2021, Pritchard et al., 2022, 2023; Roosa & Fefferman, 2022). These studies demonstrate that behavioral protection is dynamic and can change as individuals receive information and experience disease threats. This complements our earlier mathematical work showing that awareness, behavioral responses, intervention capacity, and stochasticity can substantially alter epidemic dynamics (Dubey et al., 2016; Laskowski et al., 2014). Building on these complementary foundations, we will investigate how waning biological immunity and changing behavioral protection jointly influence the probability that a pathogen introduction dies out or becomes established.
We propose to build on these results by coupling dynamic behavioral protection with waning biological immunity and asking how their joint decline changes the probability that a pathogen introduction becomes established. Using stochastic models, we will identify whether combinations of declining immunity and awareness create a window of vulnerability during which introductions that would previously have died out become more likely to expand. Empirical data, potentially including vaccination histories and measures of public awareness/behavioral data, can be used to evaluate and refine the framework/predictions.

A modeling framework and open-source code linking biological and behavioral protection; a peer-reviewed study identifying conditions under which their joint decline facilitates outbreak establishment; and a brief translating results into questions about the coordinated timing of immunization and risk communication.

Understanding when biological and behavioral protection decline together could help public health agencies anticipate periods of increased vulnerability and assess whether immunization and risk-communication strategies should be coordinated rather than planned independently.

Detecting when localized transmission is beginning to transition into sustained outbreak expansion remains difficult because these transitions often occur precisely when transmission conditions and model performance are changing.

Recent work by Azad, Candan and collaborators provides a foundation for addressing this problem. Their broader spatio-causal framework emphasizes integration of data, causal relationships, simulations, forecasting, and decision support across complex dynamic systems (Azad et al. 2024). More recently, Karami et al. showed that adaptive ensemble approaches can improve probabilistic epidemic forecasts by dynamically changing model weights as relative model performance changes across epidemic phases, while also finding that simpler ensemble approaches can remain competitive in some settings (Karami et al. 2026).

We propose to build on these results by moving from adaptive combination of existing forecasts toward deeper integration of mechanistic and machine-learning models, with particular emphasis on outbreak turning points. We will investigate approaches in which machine learning estimates time-varying mechanistic parameters, learns structured discrepancies between mechanistic predictions and observations, or augments ordinary and partial differential-equation models directly. Mechanistic, machine-learning, ensemble, and hybrid approaches will be compared specifically on their ability to detect transitions to outbreak expansion. Rather than optimizing only average forecast error, we will evaluate lead time, transition-detection sensitivity, false alarms, and uncertainty near inflection points (see attached conceptual approach figure). The central question is whether mechanistic structure combined with data-driven adaptation provides meaningful early-warning information beyond either approach alone.

An open and reproducible benchmarking framework for evaluating models at outbreak turning points; implementations of promising hybrid mechanistic–ML approaches; and a peer-reviewed study identifying when hybridization improves early detection of outbreak expansion.

Improved detection of outbreak expansion could help public health agencies determine when changing transmission patterns warrant increased surveillance, testing, healthcare preparedness, or risk communication, while quantifying the uncertainty and realistic lead time associated with those warnings.

The gut microbiome is known to play an important role in drug metabolism, yet its potential to chemically modify antibiotics and thereby alter their antimicrobial activity remains poorly understood. We propose to investigate whether gut microbial sulfotransferases can sulfate antibiotics and alter their potency, bioavailability, or susceptibility to bacterial resistance mechanisms.

Gut microbes encode aryl-sulfate sulfotransferases (ASSTs), that can transfer sulfo groups to structurally diverse phenolic molecules. Our recent work demonstrates broad substrate promiscuity of a gut microbial ASST toward host, dietary, and pharmaceutical derived compounds. Interestingly, earlier studies showed that some bacterial sulfotransferases can sulfate certain antibiotics, but the broader significance of this chemistry for antibiotic activity and antimicrobial resistance have not been investigated.

We hypothesize that gut microbial sulfotransferases can potentially modify susceptible antibiotics and alter their antimicrobial activity. We will identify antibiotic substrates of representative gut microbial sulfotransferases, characterize antibiotic sulfation using biochemical and mass-spectrometric approaches, and determine whether sulfation changes antibiotic activity using bacterial growth and susceptibility assays. In addition, we will investigate if sulfotransferase expressing gut bacteria alter antibiotic efficacy within microbial communities. To understand the broader metabolic impact of ASSTs, we will develop an AI-assisted structure-function approach to predict their substrate specificity and identify potential substrates.

This work could uncover a previously unknown mechanism through which the gut microbiome influences antibiotic exposure and activity, establishing a mechanistic connection between microbial drug metabolism and antimicrobial resistance.

Identification of antibiotics that are substrates for gut microbial sulfotransferases, determination of how sulfation affects antimicrobial activity, identification of sulfotransferase genes or microbial taxa with the potential to influence antibiotic efficacy, preliminary data supporting external grant applications, and a manuscript describing gut microbial sulfation as a potential mechanism influencing antibiotic activity and antimicrobial resistance.

Understanding whether gut microbial metabolism alters antibiotic efficacy could inform strategies for optimizing antimicrobial use and help explain interindividual variation in antibiotic response. In the longer term, microbial drug-metabolism markers could potentially contribute to more personalized selection or dosing of antimicrobial therapies.

Classical epidemiology models pandemic expansion through three foundational pillars: virus fitness, network topologies, and population susceptibility. Historically, these models have operated within the boundary conditions of Earth's biosphere and human society. However, the rapid expansion of the commercial space economy, encompassing Low Earth Orbit to cislunar habitats, space tourism, and orbital manufacturing, introduces unprecedented environmental vectors to viral evolution and transmission.

Space environments expose microbial populations to microgravity, altered fluid dynamics, fluctuating radiation levels, and operational stress. These variables alter mutation rates, viral replication mechanics, latent virus reactivation (e.g., herpesviruses), and drug-resistance profiles. Concurrently, human crews experience spaceflight-induced immune dysregulation. Despite these known phenomena, a critical research gap remains in modeling how space-passaged pathogens spread and behave upon re-entry into Earth’s biosphere. This shift translates foundational astrobiological concepts such as panspermia and virolithopanspermia (the interplanetary transport of viral agents protected within mineral, rock, or spacecraft material matrices) from theoretical origin hypotheses into immediate, anthropogenic biosafety considerations.

The question would be: “How are pandemic expansion dynamics altered when pathogen fitness, host susceptibility, and transmission networks operate across Earth-space environments?”

This research would aim to bridge space biology and terrestrial epidemiology to build predictive models, risk-assessment tools, and policy frameworks necessary to prevent space-linked disease outbreaks from escalating into global public health threats.

An integrated SEIR (Susceptible-Exposed-Infectious-Recovered)-Space model that adapts traditional terrestrial infection modeling to the unique physics, physiology, and logistics of human spaceflights.
Establishing a Space Viral Risk Benchmark- a comparative matrix ranking viral families by their sensitivity to microgravity, radiation, altered fluid dynamics, and spaceflight-associated host conditions
Whitepaper & Legislative Brief: A policy-oriented analysis for space regulatory bodies and public health agencies identifying gaps in current Earth–space biosafety governance and developing evidence-based recommendations for international standards governing pathogen monitoring, containment, reporting, and re-entry associated with human spaceflights.

This research has potential to develop an evidence-based Earth–Space Biosafety Governance Framework addressing five emerging policy domains:
Planetary protection and human-mediated back-contamination,
Biological risk assessment within commercial-space licensing,
Spaceports as potential future international points of entry,
Emergency reentry and isolation protocols, and
Pathogen surveillance, data sharing, and outbreak reporting.

Indoor environments, occupants, and microbiomes constitute a system of ecosystems with extensive interactions that impact one another. Understanding the interactions between these systems is essential to develop strategies for effective management of the indoor environment and its inhabitants to enhance public health and well-being.

Microorganisms introduced into buildings may disappear rapidly, persist locally, or disperse across connected spaces. However, the ecological and environmental processes governing these different outcomes remain poorly understood. Microbial communities are shaped by deterministic selection, dispersal-related processes, and stochastic events, while indoor factors such as ventilation, temperature, humidity, spatial connectivity, and human occupancy may further influence their persistence and distribution.

This working group proposes to integrate existing long-term building microbiome monitoring datasets with ecological community-assembly modeling, including the Normalized Stochasticity Ratio (NST) and iCAMP, to characterize the relative roles of deterministic and stochastic processes and identify ecological signatures associated with microbial persistence and spatial distribution.

We will further explore machine-learning approaches that integrate microbial community profiles, ecological-process estimates, spatial information, and indoor environmental variables to identify conditions associated with microbial persistence and dispersal across indoor spaces. A central goal will be to determine when the inclusion of ecological processes improves prediction

beyond environmental modeling alone and whether identified relationships are transferable across buildings, occupancy patterns, and microbial systems.

By linking microbial ecology, building science, and predictive modeling, this project seeks to develop a mechanistic and predictive framework for understanding how localized microbial populations may become persistent and spatially distri

Expected outcomes include a reproducible computational framework integrating microbial community data, ecological assembly models, environmental metadata, and predictive modeling; identification of ecological and environmental factors associated with microbial persistence and spatial distribution; and evaluation of whether ecological-process modeling improves prediction across indoor environments.

The working group will also aim to develop transferable hypotheses for how indoor conditions influence microbial dispersal, produce at least one peer-reviewed publication, and provide recommendations for future indoor microbial and epidemiological surveillance studies.

The project could help identify indoor environmental conditions that warrant enhanced microbial or pathogen surveillance and provide evidence for evaluating ventilation and building-management strategies during infectious-disease outbreaks.

An outbreak usually begins with sparse, delayed, and incomplete observations from a small number of locations. By the time conventional time-series indicators clearly show sustained growth, opportunities for early intervention may already be disappearing. Could changes in the structure and activity of the underlying transmission network provide earlier warning that an outbreak is shifting from localized transmission toward regional or wider expansion?

We propose studying outbreaks as dynamic, directed, multiscale graphs. Nodes could represent geographic areas, populations, healthcare systems, or host groups. Time-varying edges could represent mobility, contact, transportation, inferred transmission, or genetic relatedness. Signals observed on the nodes could include cases, hospitalizations, wastewater measurements, syndromic surveillance, and genomic indicators.

The working group would identify structural-temporal warning signals—such as changes in connectivity, directionality, community boundaries, or bridge populations—that precede expansion. A central question would be whether these signals transfer across pathogens, locations, spatial resolutions, and surveillance systems. Retrospective studies would simulate real-time emergence by restricting the information available at each point in an outbreak.

Graph-based approaches would be evaluated against time-series-only methods, static-network approaches, established epidemiological models, and ensemble forecasts. The group would explicitly study missingness, reporting delays, changes in surveillance, uncertainty, and negative transfer: situations in which a pattern learned from one outbreak produces misleading conclusions in another.

We seek interpretable principles for when network structure adds actionable information, when evidence from previous outbreaks can be reused, what additional observations would reduce uncertainty, and when a model should abstain from issuing an alert.

A multidisciplinary definition and taxonomy of network-based pandemic-expansion warning signals.

An open benchmark spanning multiple pathogens, locations, outbreak phases, and surveillance modalities.

An evaluation protocol that prevents future information from leaking into retrospective analyses.

Statistical and graph-learning methods for early warning, transferability assessment, uncertainty quantification, and detection of negative transfer.

An open-source diagnostic toolkit for outbreak analysts.

Academic publications and novel hypotheses about how network changes precede geographic expansion.

A policy and technical brief for public-health forecasting and surveillance teams.

A foundation for a larger interdisciplinary grant proposal.

The project could help public-health agencies determine when changes in mobility, transmission, or surveillance networks warrant heightened monitoring or intervention. It could also provide evidence-based criteria for deciding whether an early-warning model developed for one outbreak is safe to reuse in another setting.

Coinfection is widespread across disease ecology systems, yet empirical studies report highly variable and often inconsistent relationships among coinfecting parasites. Beyond genuine biological variation among systems, this inconsistency may also reflect substantial heterogeneity in how coinfection is observed and analyzed. Existing datasets vary widely in sampling design and resolution, ranging from presence–absence records to quantitative measures of infection intensity, while analytical approaches often rely on relatively standard statistical assumptions that may not reflect the underlying biological processes. As a result, potentially important coinfection relationships may be overlooked or difficult to interpret.

Mechanistic theory offers an opportunity to distinguish biological variation from methodological limitations by identifying plausible relationships that may otherwise be missed in empirical analyses. Our recent work provides a proof of concept, showing that theory can guide the detection of nonlinear coinfection relationships that become obscured under reduced data resolution or conventional analytical assumptions. Building from this foundation, we propose to systematically examine how coinfection data are structured and analyzed across disease ecology systems, determine how these differences affect what biological relationships can be detected, and develop a flexible, theory-informed framework for extracting greater biological insight from existing data.

Ultimately, we aim to translate these insights into a computational framework in which mechanistic knowledge prioritizes biologically plausible hypotheses while empirical data determine which relationships are supported, making data-driven and machine-learning analyses more efficient and interpretable.

A systematic synthesis of coinfection data structures and analytical approaches across disease ecology systems; a theory-informed framework for evaluating what biological relationships can be detected from different types of existing data; and a prototype computational approach for integrating mechanistic knowledge into data-driven analyses. These products will provide practical guidance for extracting greater biological insight from existing coinfection datasets and identify priorities for future empirical and theoretical studies.

By improving our ability to detect and interpret coinfection interactions across disease ecology systems, this framework could provide important insights for future disease control and management. Understanding when coinfecting pathogens facilitate or inhibit one another, and how these relationships change across ecological contexts, could inform whether and how targeting one pathogen may affect another. Translating these insights into specific management actions will require further collaboration with disease managers and other stakeholders.