Epidemics

Simulation models are widely used to support counterfactual inference in settings where direct experimental validation is difficult or infeasible. However, for phenomenological models calibrated to observational time series, goodness-of-fit does not necessarily establish that the structural assumptions underlying such inferences are uniquely supported by the data. Here, we examine this issue using a published COVID-19 transmission model for Japan that incorporates empirical inputs including human mobility, variant prevalence, and vaccination and has been used to estimate the effects of alternative vaccination scenarios. Using the publicly archived code together with a reconstructed substitute for a restricted-access input file required by the released implementation, we first reproduced the published model behaviour. We then performed two complementary computational stress tests. First, the vaccination-related input was suppressed and the remaining parameters were re-estimated against the original epidemic trajectory. Second, as a complementary test of trajectory-level flexibility, the model was refitted to synthetic target series with qualitatively different temporal structures while retaining the empirical mobility and variant inputs. The model retained substantial fitting capacity in both tests: suppression of the vaccination-related input caused only a limited deterioration in fit to the original trajectory, while qualitatively different synthetic trajectories could also be reproduced through parameter adjustment. Variation in fitted parameters across these conditions was also consistent with compensatory parameter behaviour. These results do not imply that vaccination had no effect or that the original model is invalid. Rather, they provide empirical evidence that, in this case, goodness-of-fit to the observed epidemic trajectory alone does not uniquely support the structural interpretation required for vaccination-related counterfactual inference. The study illustrates the value of reproducible computational stress testing as a complement to, rather than a substitute for, formal identifiability or model-comparison analyses.

Mechanistic models of infectious disease transmission provide a framework for understanding and predicting disease dynamics, but statistical inference is often computationally prohibitive. This challenge is especially pronounced for models with high-dimensional structures and many unknown parameters, where trajectory matching may require thousands of evaluations of an expensive likelihood function. We developed a surrogate-assisted optimization framework that uses Bayesian optimization to reduce the number of evaluations of the underlying mechanistic model required for parameter inference. We evaluated the approach using simulated fitting problems based on age-structured susceptible-exposed-infected-recovered models with 4, 8, and 16 age classes and a 210-compartment age-structured rotavirus transmission model. Applying surrogate-assisted optimization substantially reduced the number of likelihood evaluations required to identify good parameter estimates, with larger improvements as model dimensionality increased. In the rotavirus model, the surrogate Bayesian optimization approach reached the benchmark after approximately 0.7 hours, compared with approximately 9.4 hours using traditional Nelder-Mead optimization. These results demonstrate that surrogate-assisted optimization can substantially reduce the computational burden of trajectory matching for complex mechanistic transmission models, particularly for higher-dimensional inference problems.

We propose a stochastic epidemic model to describe the spread of avian influenza in a network of poultry farms. We consider three main paths of infection transmission between poultry farms: from nearby farms, mainly through airborne diffusion; from farms belonging to the same company, through shared veterinarians, forage providers and similar; from undetected small farms or wildlife. From the parameter estimation, based on a modified Expectation-Maximization algorithm, we infer that approximately 63% of the farms were infected from nearby ones, with an infection force declining with distance; of these, more than one third belonged to the same company of the estimated infector. About 20% were infected from premises belonging to the same company but farther away than the distance threshold of 2 km; the remaining ones from wildlife or unidentified sources. These estimates have been validated by a comparison with genetic data, available for a subset of the farms: the genetic distance between two farms identified, with high probability, as an infector-infectee pair is much lower than between random pairs. These results may help in the implementation of tailored prevention and control measures for future outbreaks.

Background. Classical macroparasite models commonly represent worm burden heterogeneity through prescribed Poisson or negative-binomial (NB) distributions, while transmission dynamics are formulated in terms of aggregate quantities such as mean worm burden and prevalence. We revisit these assumptions using a stratified worm burden (SWB) framework that explicitly tracks the distribution of parasite burdens across heterogeneous host classes and allows demographic turnover, transmission, and interventions to be analyzed within a unified model. Methods. We derive equilibrium distributions of the SWB system under homogeneous and heterogeneous infection processes and compare them with the classical Poisson and NB distributions used in macroparasite epidemiology and control policymaking. The framework is then extended to include host demography, coupled human-snail transmission dynamics, and periodic mass drug administration (MDA). Corresponding SWB and NB-type MacDonald models are calibrated to comparable endemic prevalence and mean worm burden, allowing direct comparison of potential transmission breakpoints and intervention outcomes. Results. In the absence of demographic turnover, the SWB framework naturally generates Poisson and NB equilibrium distributions as limiting cases of homogeneous and heterogeneous infection processes. Incorporating demographic recruitment and mortality alters these benchmark distributions, producing endemic burden structures that differ systematically from their classical counterparts. Analysis of coupled human-snail systems reveals that transmission breakpoints and bistable equilibria occur only within a relatively restricted region of SWB parameter space. In contrast, comparable NB-MacDonald formulations exhibit breakpoint behavior over substantially broader parameter ranges with the net effect of overestimating the likelihood of interruption of transmission. Although calibrated models can reproduce near identical endemic prevalence and mean worm burden, they often generate markedly different responses to MDA, including differences in rebound dynamics, persistence, and elimination thresholds. Conclusions. The SWB framework links classical statistical descriptions of worm aggregation to mechanistic burden-resolved population dynamics, clarifying how endemic distributions emerge and how they are modified by demography and control interventions. Our results suggest that transmission breakpoints are not a universal feature of helminth systems but depend strongly on model structure, and that predictions of elimination and post-treatment recovery may be considerably more model-dependent than implied by prevalence-based calibration alone. The SWB approach therefore provides a flexible alternative framework for evaluating parasite transmission dynamics and predicting the outcomes of control strategies.

Introduction: In temperate regions, influenza and respiratory syncytial virus (RSV) typically exhibit seasonal epidemics during the winter months, often concurrently with SARS-CoV-2 epidemics. Monitoring past and real-time trends in infection dynamics is important for informing public health situational awareness, including identifying and characterising unusual epidemic activity. However, data used to monitor these trends can exhibit substantial noise including strong day-of-the-week variation in reporting. In addition, case data for influenza can obscure distinct trends in influenza subtypes if not combined with virological data. Aim: By quantifying trends in respiratory virus activity using statistical methods, the Australia-Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) aims to provide government partners with comprehensive epidemic intelligence. Methods: Analyses were performed on daily case time series data for SARS-CoV-2, RSV and influenza across Australia's eight jurisdictions. Past and real-time trends were inferred for each pathogen and each influenza subtype using Bayesian p-spline models. Results: The 2025 Winter Situational Assessment Program provided weekly reports throughout the winter season and during the out-of-season influenza A H3N2 epidemic, driven by the emergence of the novel influenza subclade ('subclade K'). Despite limited subtyping data, our analysis provided an early signal of increasing influenza A H3N2 during a period in which overall influenza cases were decreasing; increases in influenza A H3N2 activity were first detected in July, and by mid-August, increasing H3N2 activity was estimated across all jurisdictions. Discussion: Our analysis highlights the benefits of incorporating statistical methods into real-time epidemic situational assessment and in consolidating reporting across jurisdictions.

Infectious disease forecasting supports public health preparedness and response by informing resource allocation, intervention planning, and situational awareness. Infectious disease forecasting capabilities in the United States have grown over the past decade yet whether forecast quality has improved over this time remains unclear. Current evaluation metrics describe relative performance for a common set of forecast targets but cannot evaluate performance across different outcomes, seasons, and pathogens. We addressed this challenge by developing a novel skill score metric to assess forecasts based on performance relative to two benchmark models: a hindcast as an idealized upper bound of performance and a baseline model as a minimal performance standard. To study the impact of the baseline choice, we considered three types of baseline models with varying levels of complexity. Using this skill score framework, we retrospectively evaluated short-term influenza (influenza-like illness and hospitalization) and COVID-19 (case, death, and hospitalization) forecasts, focusing on how the performance of team and multi-model ensemble forecasts changed over time. The ensemble outperformed 99% of forecasts from a simplistic, naive baseline, and 84-91% of forecasts from more complex baseline models that incorporate recent data. We found consistent improvements in ensemble and team forecast skill over time for both influenza forecast targets, with 4-week horizon forecasts in the most recent year having higher skill scores than 1-week horizon forecasts in the first year of each challenge. Results were mixed for COVID-19 forecasts. This framework provides a comprehensive picture of progress in infectious disease forecasting over the past decade enabled by improved standardization, data, models, and collaboration.

Nitrogen dioxide (NO2) is a widespread air pollutant associated with the risk and prevalence of various respiratory infectious diseases. Epidemiological studies have reported associations between NO2 exposure and COVID-19 incidence, and biological evidence indicates that NO2 exposure increases vulnerability to infection. Motivated by these findings and by the scarcity of mechanistic epidemic models that incorporate air pollutant data, here we introduce an NO2- and mobility-informed metapopulation model and study the first COVID-19 wave in Oklahoma. County-level NO2 concentrations enter the infection rates through a first-order parameterization, alongside high-resolution mobility patterns capturing heterogeneous inter-county connectivity. Using numerical simulations, we demonstrate that higher environmental sensitivity produces earlier and more synchronized outbreaks across counties, while lower sensitivity yields delayed, asynchronous trajectories. When fitted to county-level COVID-19 incidence data, our model outperformed the classic SIR model (fitted independently in each county) in over 75% of counties in Oklahoma across all standard error metrics. The improvements were even more pronounced in forecasting tasks across epidemic stages, with county-level win rates against the SIR model of 59.74% (surge), 87.01% (peak), 80.52% (post-peak), and 88.31% (decline) by nRMSE. To isolate the contribution of NO2, we compared our model against a mobility-only model with NO2 removed and homogeneous infection rates across counties. The NO2 term improved county-level forecasts during the surge (94.81% of counties by nRMSE), post-peak (88.31%), and peak (62.34%) stages, though not during the early and decline stages, suggesting that NO2 information can generate better forecasts than mobility alone during periods of high incidence. Our results demonstrate that environmental exposure can enter the transmission structure of a mechanistic epidemic model directly and that doing so yields measurable forecasting gains.

The 2026 Andes virus (ANDV) outbreak aboard the MV Hondius cruise ship resulted in 11 confirmed cases and 3 deaths and raised questions regarding the origin of infection, with much media focus resting on a landfill in Tierra del Fuego. We evaluated competing exposure hypotheses by assembling open data on reservoir host occurrences, rodent serosurveys, and historical ANDV outbreaks along with the index case's self-documented, publicly-posted citizen-science digital record of their movements. Historical outbreaks associated with human-to-human transmission were concentrated within a restricted region spanning southern Chile and adjacent western Argentina, coinciding with the distribution of the principal reservoir host, Oligoryzomys longicaudatus, and areas identified as elevated risk by multiple ecological and genetic models. The index case was documented within this transmission focus shortly before the putative exposure period. Together, our open data approach was able to rapidly locate initial exposure within the established ANDV transmission focus and develop scenarios to prioritise further epidemiological investigation.

Infected individuals drive infectious disease transmission, yet empirical data on social interactions during acute infection remain limited. We conducted a prospective longitudinal study of 1,000 medically attended acute gastroenteritis and acute respiratory infection cases and 709 household members in the northwestern United States from 2024 to 2025 to quantify illness-associated changes in social contacts and their implications for transmission modeling. Cases substantially reduced social contacts during peak illness; weighted mean number of contacts increased 2.0-fold (95% confidence interval, 1.8-2.2) over the two-week follow-up. In transmission modeling, the simulated outbreak trajectory and estimated effectiveness of interventions differed substantially between models that did and did not account for temporal reductions in social contacts during acute infection. These findings show that behavioral responses during acute infection could substantially influence transmission dynamics and intervention evaluation, and provide an empirical framework for incorporating illness-associated behavioral change into infectious disease models.

Highly pathogenic avian influenza (HPAI) is among the most significant disease threats to poultry worldwide. Modelling challenges, where independent teams address a common epidemiological scenario, can support the development of response capabilities and strengthen communication between modellers and risk managers. Despite the established benefits of these exercises, they remain uncommon in animal health, and none have previously addressed avian influenza. Here Jolly Island, an artificial landscape of 9160 poultry farms with five production types, was developed and seeded with stochastic HPAI epidemics that ran their course over a three-to-four month period. From January through April 2026, 45 registered teams across five continents investigated a selected synthetic epidemic, producing probabilistic forecasts, inferring transmission drivers, and evaluating counterfactual control policies. Here the objectives, design choices, and data generation of the HPAI Modelling Challenge are described in full, covering transmission processes, surveillance system operations, and control strategy implementation, along with the selected epidemic trajectory, the narrative situation reports supplied to the teams at each phase, and the complete phase-specific datasets released to them. As the opening paper of this special issue, here is provided the foundation upon which all subsequent contributions, where teams document their approaches and analyses, are built.

Understanding when and how pathogens spread from person to person is fundamental to characterizing transmission, interpreting surveillance data, and designing effective control measures. Yet key epidemiological delays -- including the latent period, incubation period, generation interval, and serial interval -- are difficult to estimate because key events such as infection, infectiousness, symptom onset, and transmission are rarely observed directly. Current approaches typically estimate these delays using flexible statistical distributions rather than explicitly linking them through the underlying processes that generate them, making it difficult to interpret the underlying biology and to ensure consistency across related delay estimates. Here, we develop a stochastic event-time framework built around a dynamic within-host pathogen-growth model that treats epidemiological delays as coupled outcomes of a shared biological process. By representing these key events as linked stochastic events, the framework produces internally consistent delay distributions while inferring parameters with mechanistic interpretations conditional on the assumed within-host model that govern pathogen growth, clearance, infectiousness, and symptom onset. The framework also accommodates censored and partially observed infection and symptom-onset times through a unified observation model. Across simulation studies, the framework accurately recovered generation-interval characteristics across sample sizes and disease time scales. In applications to SARS-CoV-2 and mpox transmission-pair data, the framework showed improved predictive fit relative to conventional lognormal delay models in several analyses, although the magnitude of improvement varied across datasets and observation models; it estimated shorter generation intervals for Omicron than Delta and revealed alternative within-host parameters compatible with the mpox transmission data. This mechanistic framework provides a biologically interpretable alternative to independently fitted delay distributions, yields internally consistent estimates of epidemiological delays, and establishes a foundation for integrating biological and epidemiological data to better understand, predict, and control pathogen transmission.

Host immunity acquired through prior infections is a key factor shaping the spread of emerging variants. However, accounting for complete infection histories in mathematical models is challenging because the number of possible histories grows exponentially with the number of variants. Here, we present a flexible framework for modeling the co-circulation of multiple variants emerging at arbitrary times while incorporating cross-immunity and variant-specific characteristics. We introduce three tractable approximations: truncation, which retains only infection histories up to a given number of strains; one variant at a time, which is exact when variants circulation does not overlap; and two variants at a time, which is exact when at most two strains co-circulate. We systematically evaluate these approximations across a range of cross-immunity levels and inter-variant emergence delays. Truncation approximations perform best under short emergence delays and strong cross-immunity, whereas the other approaches outperform them at longer delays. When applied to SARS-CoV-2 variant dynamics, the second-order truncation and the two variants at a time approximation accurately capture incidence patterns, whereas the other approaches perform well only for specific subsets of variants. These results identify the parameter regimes in which simplifying assumptions remain valid, provide guidance for model selection, and highlight the key role of infection history in shaping epidemic trajectories.

We develop an age- and immunity-structured COVID-19 model with setting-specific contacts, calibrated to Ontario incidence and hospital admissions before Omicron. We simulate counterfactual vaccination strategies and contact restrictions in schools, workplaces, and other community settings, evaluating projected severe-case demand against an illustrative healthcare-capacity benchmark. Vaccination combined with workplace and community contact restrictions keeps simulated demand within capacity. In contrast, relaxing restrictions in either setting produces large surges and repeated capacity threats, even when school restrictions remain. School-only changes produce smaller effects on severe disease. These findings highlight the value of vaccination and setting-specific contact restrictions that prioritize workplace and community settings for pathogens with adult-concentrated severe outcomes.

Brazil experienced its worst dengue epidemic in 2024, with an increase in severe outcomes among elderly individuals, highlighting the need for vaccination strategies informed by population immunity. We reconstructed state- and age-specific immune profiles by fitting an age-structured catalytic model to dengue surveillance data from 2000-2025. This inferred immune profile was incorporated into an SIR-type transmission model to project disease burden and to evaluate the public health impact of vaccine allocation strategies. We observed substantial spatial heterogeneity in accumulated immunity, which was closely associated with projected burden and optimal allocation. Strategies prioritizing states with lower historical incidence and larger susceptible populations yielded greater benefits. Under limited vaccine supply, targeting children resulted in the greatest reductions in DALYs, whereas targeting elderly populations achieved smaller DALY reductions but larger reductions in mortality. We further showed that accelerated vaccine supply markedly increased impact, while allocation choices remained critical when availability was constrained.

Understanding the relationship between weather conditions and infectious disease transmission has important public health implications; nevertheless, the functional form of this relationship often remains poorly understood. Humidity has previously been suggested to have a U-shaped effect on the transmission of respiratory pathogens, with optimal transmission occurring at both low and high levels. Here, to assess the impact of this functional form and of its potential misspecification, we first simulate transmission rates based on the U-shaped relationship and then test whether an exponential function accurately estimates these rates across a range of climates. We further use a transmission model to simulate case count data for a range of hypothetical pathogens, comparing outbreak dynamics under the U-shaped forcing function to those under the best-fit exponential approximation. In our simulations, the model fit for the exponential function is climate-dependent, performing best in very dry and very humid environments. In locations where specific humidity values centre around the vertex of the U-shaped function, the exponential model underestimates the amplitude and frequency of incidence peaks. These findings demonstrate the potential consequences of assuming an incorrect functional form for weather-infectious disease relationships and highlight the need for more careful consideration of this functional form in modelling practice.

The contribution of environmental reservoirs to antimicrobial resistance (AMR) acquisition in humans remains poorly quantified, despite extensive evidence that resistant bacteria and resistance genes circulate through wastewater, surface waters, food and other environmental matrices. The central difficulty is no longer to demonstrate that these reservoirs are connected, but to quantify the direction and relative contribution of the pathways linking environmental AMR to human acquisition. Here, we synthesize the main processes that need to be represented at the interface between the environment and humans and propose a mechanistic framework for Escherichia coli producing extended spectrum beta lactamases (ESBL Ec), linking human, water and food compartments. The framework explicitly represents contamination of the environment by humans and multiple routes of human exposure to environmental reservoirs, including drinking water, recreational water exposure and food contamination mediated by irrigation. We apply the model to two contrasting illustrative settings, France and Madagascar, and use scenarios involving antibiotic use, environmental contamination and climate to explore how changes in specific drivers propagate across compartments. The simulations show responses that depend strongly on the compartment, while sensitivity analyses identify wastewater treatment efficiency and parameters describing transmission, exposure and environmental selection at specific interfaces as influential components of the system. The framework is used to explore how different transmission pathways and environmental drivers can shape AMR dynamics across interconnected human and environmental compartments. By comparing contrasting scenarios and identifying the parameters that most strongly influence model behaviour, we highlight both potential leverage points and critical gaps in current empirical knowledge. More broadly, this work illustrates how decomposing One Health AMR into tractable reservoir interfaces can help structure future data collection and progressively improve our ability to quantify transmission between reservoirs.

The 2025 measles outbreak in South Carolina, the largest in the United States in over two decades, produced 997 confirmed cases, predominantly among school-aged children in an under-vaccinated community in Spartanburg County. To support the South Carolina Department of Public Health (DPH) measles response, we developed a stochastic agent-based model to forecast outbreak trajectory and evaluate intervention strategies in real time, integrating transmission across three spatial scales and grounded in geocoded households, school enrollment records, and census-derived demographic data. The model predicted a median of 1,020 total infections (90% prediction interval: 840-1,273). Counterfactual simulations without active contact tracing and quarantine projected a median of 2,432 infections (90% prediction interval: 1,867-3,185), approximately 138% more than the model-predicted baseline (1,020 cases). Model fidelity required a highly localized between-school contact structure; broader contact assumptions substantially overpredicted both case counts and geographic spread. Simulated regional introductions confirmed that outbreaks arose only near connected clusters of undervaccination, while well-vaccinated areas remained contained. Model versions deployed prospectively at weeks 9, 17, and 34 of the response informed resource allocation decisions and provided operational evidence for the effectiveness of contact tracing and quarantine strategies. Critically, the model reproduced geographic spread, number of affected schools, and breakthrough infection counts not used in fitting, validating its structural fidelity and establishing a replicable framework for jurisdictions maintaining school-enrollment and vaccination records.

The 2026 multinational Andes virus (ANDV) outbreak linked to cruise-ship travel from Argentina triggered extensive contact tracing and quarantine. Whether intervention on this scale was necessary or sufficient depends on the risk of onward transmission, which can be difficult to estimate early in an outbreak. Using transmission-pair data from the 2018--2019 outbreak in Epuyen, Argentina, and a mechanistic model linking viral growth, symptom onset, and transmission, we estimated that 42.4% (95% CrI: 18.2-69.7\%) of transmission occurs before symptom onset and that the median incubation period is 18.5 days (95% CrI: 16.5-19.5). We simulated outbreaks under an Epuyen-calibrated estimate (R = 2.12, k = 0.316) and a pooled estimate from 15 prior ANDV investigations (R = 0.24, k = 0.39), with intervention beginning after two generations, as in the cruise-associated event. Under the higher-transmission scenario, missing 10% of exposed individuals or downstream contacts resulted in a 29.9% probability of continued spread and a 20.6% probability that at least 5,000 ultimately uninfected people would be quarantined. Under lower transmission, continued spread stayed below 8%. However, the risk of onward transmission could not be estimated precisely from the first several detected cases. These findings therefore support rapid, comprehensive contact tracing and quarantine after large international exposure events despite substantial costs.

Background Exposure-history data collected during epidemics or pandemics within digital contact-tracing applications hold immense potential for rapid, scalable epidemiological inference. The sensitivity of these data makes privacy-preserving designs essential to secure public trust and app uptake. However, during the COVID-19 pandemic, such approaches were not yet available at the scale or maturity required for deployment. We present a Bayesian decentralised framework to infer transmission dynamics from exposure-history data while protecting user privacy. Methods Our approach is based on iterative communication between user devices and a central server, computing log-likelihood gradients on device from exposure-history data and centrally updating parameter values using only aggregated gradients. Applying this framework to synthetic exposure-history data from a hypothetical respiratory pathogen epidemic simulated on dynamic contact networks, we assessed its ability to estimate the underlying transmission parameters, perform infector attribution, and reconstruct serial-interval (SI) distributions. We systematically explored how inference was affected by incomplete exposure-history data arising from partial app adoption, case under-ascertainment, and incomplete exposure logging, and by transmission scenarios. Findings Our decentralised framework accurately recovered transmission parameters, closely matching estimates derived from centralised data with no systematic bias in posterior means. Parameter recovery remained robust when hazards from unobserved exposures were known as a daily average across the population. Although incomplete exposure-history data and specific transmission scenarios introduced compensatory parameter biases, the inferred host infectiousness profile retained sufficient accuracy, and the weekly SI distributions reconstructed from infector attributions closely resembled the ground-truth distributions. Interpretation Our findings highlight that, beyond its primary role as a control measure, digital contact tracing could help characterise transmission dynamics almost in real time and inform public health interventions while preserving user privacy. Funding Oxford Martin School Programme on Digital Pandemic Preparedness; National Institute for Health and Care Research (NIHR) Health Protection Research Unit in Emerging and Zoonotic Infections.

Patient movement within and between hospitals creates a complex hospital network that facilitates the spread of antimicrobial-resistant bacteria and contributes to healthcare-associated infections. Yet most national-scale studies infer spread from aggregated hospital-to-hospital patient sharing, leaving patient-to-patient contact pathways and the role of routine patient mobility in shaping colonization risk poorly characterized. Using Frances 2023-2024 national hospital-discharge data, we reconstructed a temporal patient-based network of 10.4 million patients across 1,408 acute-care hospitals, comprising 326.6 million overnight ward co-location interactions. We simulated colonization over 365 days on the observed day-by-day temporal network using a stochastic susceptible-colonized-susceptible model, with a mean colonization duration of 180 days. We used national carbapenemase-producing Enterobacterales (CPE) surveillance data to calibrate differences in transmission risk across clinical settings. Across 2,000 simulations initiated on randomly selected dates in 2023, we calculated patient-level colonization probabilities and summarized risk across patients residential areas, wards, and hospitals. We further assessed hospital-level consistency between simulated risk and recorded CPE occurrence. Simulated risk was highly heterogeneous across patients and care settings. When mapped by place of residence, hospital-associated colonization risk showed strong spatial clustering in both urban and rural areas. Multivariable regression analyses identified patient- and hospital-level characteristics associated with heterogeneity in simulated colonization risk. Sensitivity analyses varying initial seeding and colonization duration showed that relative risk patterns remained highly stable. This nationwide framework shows how patient mobility and ward co-location patterns can reveal patterns of hospital-associated colonization risk and could support pathogen-specific surveillance and intervention evaluation. Significance StatementAsymptomatic carriage allows antimicrobial-resistant bacteria to spread through healthcare systems while colonized patients may remain unrecognized in the absence of active screening. We reconstructed a nationwide, time-resolved network of ward co-location among hospitalized patients in France. We accounted for differences in transmission risk across clinical settings using national surveillance data on carbapenemase-producing Enterobacterales. Using a transmission model, we characterized colonization-risk patterns and identified patient- and hospital-level features associated with variation in risk. This framework offers health authorities a way to examine where patient movement and hospital care may contribute to concentrations of colonization risk, although pathogen- and context-specific adaptation and evaluation remain necessary to guide surveillance or interventions. Author contributionsS.B., L.C., L.T., A.K., T.D., V.C., and P.C. designed research; S.B. and P.C. performed research; S.B. and P.C. analyzed data; S.B. and P.C. wrote the paper; all authors reviewed and edited the final manuscript.