Epidemics

Background: Respiratory syncytial virus (RSV) remains a leading cause of hospitalization in infancy, with severe outcomes influenced by both contact patterns and passive immunity. Non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic suppressed RSV circulation and reduced opportunities for maternal immune boosting, potentially altering protection among newborns. We evaluated whether incorporating time-varying maternal immunity improves the ability of an age-structured transmission model to predict post-pandemic RSV hospitalization patterns in infants. Methods: We analyzed population-based RSV hospitalizations among Ontario (Canada) infants (<1 year) from July 2, 2017 to June 25, 2024, using linked administrative databases. A deterministic compartmental model across seven age classes was calibrated against pre-pandemic data using Latin Hypercube Sampling. We compared a model incorporating time-varying contact rates alone against a specification that additionally included time-varying maternal immunity. Results: Both specifications accurately reproduced pre-pandemic seasonality and macro-level post-pandemic resurgence features. The constant maternal immunity model showed slightly better accuracy in capturing the 2021/22 peak compared to the time-varying maternal immunity specification. However, both qualitatively captured the continued near-absence of RSV and the observed peak was captured within the 95% credible intervals. While both models precisely captured the timing and overwhelming surge of admissions that occurred in 2022/23, they failed to capture the premature peak timing and magnitude in 2023/24. Conclusions: Incorporating time-varying maternal immunity did not improve model accuracy post-pandemic. While maternal protection is essential for evaluating infant immunizations, population-level contact shifts primarily shaped post-pandemic RSV seasonality, indicating that models must account for these mechanisms of RSV transmission dynamics.

Models that provide risk maps for zoonoses often lack (i) a spatiotemporal autocorrelation component, yet crucial in understanding the spread of infectious diseases, (ii) accounting for heterogeneity in case reporting, and (iii) a causal framework for explanatory variables. Here, we addressed these limitations with a model system, West Nile virus, a vector-borne pathogen transmitted in a bird reservoir, and affecting humans and horses. We built a spatiotemporal occupancy model and fitted it to notified (human and horse) case data. Based on a directed acyclic graph, we estimated the causal effects of conjectural weather variables (i.e. changing in the short-term) vs. structural variables (i.e. changing in the long-term) on WNV circulation in the bird reservoir, besides assessing variables associated with case reporting. By computing population attributable fractions, we found the contribution of conjectural weather variables to WNV outbreaks in Europe to be globally higher than the structure of the bird community.

Age is a primary determinant of symptom severity and transmission patterns for many infectious diseases, motivating the use of age-stratified models parameterized by contact matrices. In the United States, the absence of direct contact surveys has required estimating synthetic contact matrices from demographic data on household size, school attendance, and workforce participation. However, this likely underestimates contacts among children under age 5, who often attend group childcare missing from censuses. The goal of this study was to use nationally-representative data on childcare arrangements (the Early Childhood Program Participation Survey) to reconstruct daily contacts occurring in childcare settings, and augment existing all-age contact matrices. For infants under 1 year of age, we estimated 0.2 daily contacts with other infants, increasing to 0.7 daily contacts with same-age peers for 1- or 2-year-olds, 1.3 for 3-year-olds, and 3.5 for 4-year-olds. Including childcare settings increases estimated contacts among young children by up to six fold. Using simulations of measles outbreaks in inadequately vaccinated populations, we show that prior contact matrices significantly underestimated the outbreak frequency, size, and impact on preschool age groups. Our findings highlight the need for targeted data collection on childcare contacts to improve model-based evaluation of interventions particularly for young children.

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.

Bacterial sexually transmitted infections (STIs) pose a major global public health challenge, with Neisseria gonorrhoeae being of particular concern due to its persistently high prevalence and increasing antimicrobial resistance. The emergence of multidrug-resistant strains has narrowed treatment options, highlighting the importance of prevention. In this context, knowing whether there is superspreading (transmission heterogeneity) within a population becomes crucial for accurate public health measures. However, classic methods to quantify superspreading rely on dense contact tracing, and this is not always feasible. As an alternative, we can use Bayesian phylodynamic modelling to infer transmission dynamics, including superspreading. Yet modelling transmission dynamics using bacterial data remains problematic, although it is widely used for viral data. Here, we apply a multi-type birth-death model parametrised to quantify superspreading in N. gonorrhoeae outbreaks, estimating the fraction and relative impact of superspreaders and reproductive numbers for superspreaders and non-superspreaders. We also use a hierarchical modelling strategy with partial pooling to increase the power for detecting superspreading in each cluster. Model performance was successfully evaluated across a range of superspreading scenarios using both transmission-informed phylogenies and sequence data with phylogenetic uncertainty. Application to empirical genomic data revealed a substantial role of superspreading in N. gonorrhoeae transmission during the COVID-19 pandemic in Australia. These results highlight the impact of superspreading in N. gonorrhoeae transmission and the importance of detecting it to efficiently stop the dissemination of the disease

Leptospirosis is a water-related zoonotic disease with complex transmission pathways, including direct transmission from infected animals and indirect transmission through contaminated soil and water. Identifying key areas to implement targeted infection prevention and control strategies is challenging, as a range of risk factors across different scales can drive human infection. We aimed to develop an epidemiological modelling approach to predict key transmission pathways driving leptospirosis infection, including risks ranging from household level factors to the movement of pathogens across watersheds. We combined a causal Bayesian network with a novel hydrological pathogen transport model to predict leptospirosis across Fiji and found key infection hotspots adjacent to rivers and within degraded watersheds; a dynamic overlooked by previous epidemiological models. We used a wide range of data for model parameterisation (e.g., expert elicitation, epidemiological surveys, and environmental data) and found that predictive validity improved when expert input on risk factors was used to guide model parameterisation, improving R2 for predicted versus observed seroprevalence from 0.71 to 0.91. Our One Health modelling approach can be used to support the design and evaluation of environment-based disease prevention strategies at national scales.

Background: Real-time outbreak analyses are often requested before surveillance systems have stabilised or epidemics have generated enough information for the desired inference. Existing approaches address surveillance quality, forecasting, estimands and identifiability separately, but do not provide a common rule for deciding which analytical product is supportable at a particular data vintage. We developed an estimand-first framework for analytical readiness. Methods: The framework distinguishes surveillance maturity (S), epidemic-process informativeness (E) and estimand-specific analytical readiness, defined as whether the available data vintage, observation process, method and decision-matched validation support a specified inference for a specified decision. We stress-tested four implications using longitudinal data from the 2018-2020 Ebola response in eastern Democratic Republic of the Congo (DRC), archived geographic forecasts, independent forecasting data from Western Area, Sierra Leone, and a targeted mortality-identifiability experiment. Results: During a documented DRC surveillance disruption and recovery, seven-day persistence forecasts had all-health-zone absolute errors of 1, 3, 18 and 4 cases across pre-shock, acute-shock, early-recovery and recovery origins; the largest error occurred during early recovery. Four-week reported-case trend multipliers changed from 0.67 and 0.73 to 1.12 and 1.29, while the final fit was strongly overdispersed (Pearson dispersion 7.65), demonstrating asynchronous readiness across estimands. Archived geographic forecasts improved a Top-3 allocation decision over cumulative burden at only one origin despite consistently lower Brier scores for one specification. In Western Area, persistence forecast mean absolute error increased from 39.1 cases at one week to 82.3 at four weeks, and a history-to-horizon ratio did not define a universal threshold. An observed reported case-fatality ratio of 0.40 was compatible with constructed latent fatality values from 0.10 to 0.80; increasing the reported denominator narrowed sampling uncertainty without reducing structural uncertainty. Conclusions: Analytical readiness is task- and vintage-specific rather than a property of a dataset. More data, model convergence or narrow intervals cannot substitute for estimand definition, observation-process awareness, decision-matched validation and explicit identification analysis. Keywords: outbreak analytics; surveillance maturity; analytical readiness; estimand; identifiability; forecasting; Ebola; reporting process; decision-matched validation

Since the COVID-19 pandemic, forecasting hubs and non-traditional respiratory disease surveillance streams have become increasingly common. However, many forecasting approaches assume that relationships between surveillance predictors and disease outcomes remain stable over time and that incorporating additional historical data will improve forecast performance. To evaluate these assumptions in a real-world setting, we developed and evaluated forecasts of SARS-CoV-2 and influenza hospitalizations in Utah using syndromic surveillance, test positivity, and wastewater data. Rather than identifying a single, best-performing model, we examined whether relationships between surveillance predictors and hospitalization outcomes remained stable across seasons and whether longer historical training periods consistently improved forecast accuracy. Relationships between surveillance predictors and hospitalizations varied substantially by pathogen and season. Analyses using pooled data across multiple years suggested strong positive correlations between predictors and outcomes, but these aggregated patterns often obscured weak or negative correlations observed during SARS-CoV-2 variant waves and influenza seasons. Forecast performance similarly varied over time. Models that performed well during some seasons, transmission phases, or under certain training strategies frequently performed worse than benchmark models in others. Training on additional historical data generally reduced forecast accuracy, though this varied by disease and transmission phase. Forecasting groups should prioritize continual evaluation of surveillance predictors, adaptive strategies, and diverse ensembles, rather than relying on a single model, data stream, or historical training framework each year.

Background: Influenza and respiratory syncytial virus (RSV) both cause annual peaks in hospitalisation and with considerable overlap. Epidemiological studies suggest an inhibitory relationship, which is supported by laboratory studies. Several biological mechanisms including resource competition and immune activation have been identified, but limited evidence exists to distinguish their effects at the population level. Methods: We developed an influenza-RSV compartmental model to estimate and compare potential interactions using data on virus-associated hospitalisations in Valencia, Spain from 2010 through 2020. We considered five interaction mechanisms: altered susceptibility, transmissibility and severity, a refractory period post-infection, and a residual positivity period. Using Bayesian inference, we fit the model to weekly hospitalisations with influenza, RSV or both, and to national surveillance data for influenza-like illness. Results: We found evidence for inhibition between influenza and RSV, with several interaction mechanisms being supported. In the best-fitting model, active RSV infection reduced the probability of hospitalisation with influenza to 20% of the baseline (with the data supporting values from 0%-60%). Compared to a model without interaction, this mechanism reduced the number of hospital cases with influenza-RSV co-detection by 63%. Conclusions: Our results support the existence of inhibitory mechanisms, with small but substantial effects on disease epidemiology. These results highlight the importance of understanding these interactions under the changing epidemiology of respiratory viruses and vaccinations.

Legionnaires disease is a severe respiratory illness caused by Legionella bacteria, with most cases occurring sporadically and environmental sources often unidentified. Effective outbreak detection requires understanding the spatiotemporal dynamics of sporadic cases and their environmental drivers. We developed a mechanistically informed spatiotemporal model integrating fine-scale spatial heterogeneity, multi-week meteorological influences, and extended temporal lags. The framework combines a negative binomial generalised additive model (GAM), a Besag-York-Mollie (BYM2) spatial component, and distributed lag nonlinear models (DLNMs) to capture nonlinear, delayed effects of temperature, dewpoint depression, precipitation, and cloud cover. These outputs generate a national daily index of weather-driven vulnerability, which is combined with hierarchical clustering to identify potential outbreaks. Across 2000-2019, our model improved outbreak detection in 15 of 20 years compared with the baseline UKHSA approach; in the remaining years performance was either equivalent (two years) or only slightly worse (three years, 0.98% reduction). Mean relative improvements were 6.0%, with a maximum of 14.4% in 2013. Improvements were consistent across months, and coarser 0.25-degree grid evaluations likely underestimate the models advantage at finer spatial scales. The analysis also clarified dual-stage Legionnaires disease dynamics, distinguishing environmental bacterial growth from the shorter infection window, and demonstrated the necessity of extended lags for accurate risk prediction. This framework provides a robust platform for targeted surveillance and predictive modelling, supporting evidence-based interventions and enhancing preparedness for sporadic Legionnaires disease under observed climatic conditions.

National-level estimates of the time-varying reproduction number (Rt) for the 2026 Bundibugyo virus disease (BDBV) outbreak in the Democratic Republic of the Congo (DRC) have converged on a value close to the epidemic threshold since early August 2026, consistent with independent joint Bayesian renewal-model estimates. A single national Rt, however, can obscure divergent sub-national epidemic trajectories, particularly across a five-province outbreak in which provinces range from a declining original epicentre to recently-seeded fronts. We estimated Rt at national, provincial, and, where case volume allowed, health-zone level, using both a standard sliding-window (Cori) estimator and a hierarchical Bayesian renewal model with partial pooling across spatial units, fitted by Hamiltonian Monte Carlo (No-U-Turn Sampler). Provincial estimates diverged materially from the national trend: as of the week of 6 August 2026, Ituri, the outbreak's original epicentre, had a hierarchical median Rt of 0.91 (95% credible interval [CrI] 0.68 - 1.24), while Nord-Kivu (1.23, [0.87 - 1.72]) and Haut-Uele (1.79, [1.24 - 2.67]) remained above threshold. Health-zone disaggregation, feasible only in Ituri and Nord-Kivu given case volume, showed this provincial picture itself masked further heterogeneity: in Ituri, the zone where the outbreak began (Mongbwalu) had clearly declined (Rt 0.36, [0.17 - 0.74]) while the two largest zones by cumulative case count (Bunia, Rwampara) remained at or above threshold; in Nord-Kivu, elevated transmission was concentrated in a single zone (Katwa, Rt 1.39, [0.85 - 1.99]) while a comparably-sized zone (Butembo) had already declined (0.64, [0.23 - 1.38]). An initial disagreement between the sliding-window and hierarchical provincial estimates was traced to a data-reconstruction artefact (forward-filling, rather than interpolating, multi-day gaps in health-zone reporting) rather than a genuine methods disagreement, and resolved once corrected. The hierarchical model's dispersion structure, calibration, and sensitivity to the generation-interval assumption were each checked explicitly; a shared (non-province-specific) dispersion parameter was retained on the basis of negligible predictive difference (PSIS-LOO), the model achieved 95.0% pooled 95% posterior-predictive interval coverage, and the province ranking was unchanged across a generation-interval sensitivity grid (Spearman {rho} = 1.0). Aggregation masks meaningful heterogeneity in transmission intensity at every spatial resolution examined; response prioritisation based on a single national or even provincial Rt risks directing attention away from the specific zones where transmission remains supercritical.

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.

Antimicrobial-resistant organisms (AMROs) impose a major burden on healthcare systems, yet routine surveillance cannot readily distinguish colonization imported at admission from transmission acquired within hospitals. This gap is especially consequential because both processes may vary substantially across wards, while asymptomatic carriage, incomplete testing, imperfect diagnostic sensitivity, and patient movement obscure the underlying transmission dynamics. To address this challenge, we developed a blockwise agent-based iterated filter (BAIF) for inference in a patient-level transmission model on a dynamic ward co-location network. The model tracks susceptible and colonized patients as they move across wards, represents unobserved colonization histories, and incorporates the recorded testing schedule and imperfect diagnostic sensitivity. BAIF uses blockwise likelihood evaluation and resampling to estimate ward-block-specific transmission rates and importation probabilities in this high-dimensional latent system. Synthetic experiments showed that BAIF recovered these parameters from partially observed outbreaks. We then applied the framework to hospitalization and microbiological surveillance data collected from 2012 to 2016 at an urban quaternary care hospital in New York City for four AMROs. Transmission and importation were highly heterogeneous across ward blocks. Elevated transmission was repeatedly concentrated in the same ward groups, whereas blocks with the highest importation varied by pathogen. By distinguishing importation-dominated from transmission-dominated ward blocks, the framework can inform more targeted surveillance and infection-control strategies. More broadly, BAIF provides an effective inference framework for high-dimensional, partially observed agent-based models on dynamic contact networks.

Background The 2026 Bundibugyo virus disease outbreak in the Democratic Republic of the Congo became the largest recorded epidemic caused by Bundibugyo virus and showed unusually rapid early growth. Initial projections warned that it could become one of the largest Ebola-family outbreaks on record, but these were based on assumed mortality totals and intervention scenarios rather than repeated comparison with subsequently observed surveillance data. We assessed how accurately the outbreak could have been forecast in real time from routinely published national situation reports, whether a simple ensemble improved on its component models, and how forecasts should inform operational capacity planning. Methods We conducted a retrospective pseudo-prospective rolling-origin study using daily cumulative confirmed cases and deaths reported from 14 May to 20 July 2026 (68 calendar days; 61 numeric reports). Each date with a newly reported national total became an eligible forecast origin once seven numeric observations were available, yielding 55 origins. At each origin, all later data were withheld. Two prespecified models were refitted using only information then available: a recent seven-increment baseline and a Bayesian negative-binomial surveillance-maturity model fitted by Markov Chain Monte Carlo. A Gompertz model was fitted identically as a benchmark. Forecasts were produced for 7, 14, and 21 days and evaluated only when an observation existed on the exact target date. Bayesian forecasts were prospectively recalibrated using only previously realised errors from forecast-maturity-qualified origins. We also evaluated a baseline-Bayesian ensemble, with horizon- and outcome-specific weights selected prospectively by an expanding-origin procedure using an asymmetric operational loss function. Findings A statistically and externally corroborated surveillance-maturity discontinuity occurred on 28 May 2026 (robust z score 13{middle dot}0). The Gompertz model had the poorest 80% predictive-interval coverage at every horizon and for both outcomes (25{middle dot}9-34{middle dot}1% for cases; 0{middle dot}0-23{middle dot}5% for deaths) and was excluded. Recalibration corrected consistent Bayesian underprediction and was applied at 24 of 55 origins for 21-day forecasts. It substantially improved case coverage (7-day, 60{middle dot}0% to 80{middle dot}0%; 14-day, 41{middle dot}0% to 76{middle dot}9%) and improved death forecasting in both accuracy and calibration (7-day median absolute percentage error, 10{middle dot}3% to 7{middle dot}7%; 80% coverage, 33{middle dot}3% to 95{middle dot}6%). Ensemble weights differed by outcome: case forecasts were baseline-heavy (w{approx}0{middle dot}8-0{middle dot}9), whereas death forecasts were near parity (w{approx}0{middle dot}5-0{middle dot}6). For deaths, the ensemble improved mean weighted interval score over both component models at every horizon (7-day: baseline 24{middle dot}8, Bayesian 20{middle dot}5, ensemble 17{middle dot}9). Interpretation Routine situation-report data supported useful short-term forecasting, but no single model was best on every criterion. Three forms of maturity shaped forecast reliability: epidemic, surveillance, and forecast maturity. Forecast maturity was the most robust and model-independent finding and supports 21 days, rather than 28 days, as the longest routine operational horizon. The study provides a reproducible and adjustable framework for combining simple and complex models and communicating uncertainty to operational decision-makers.

Anticipating which health zone will report the next confirmed case is operationally distinct from forecasting national case counts and matters for prepositioning response capacity; most spatial spread models rely on mobile-phone mobility data unavailable in the Democratic Republic of the Congo (DRC). We modelled the discrete-time hazard of a first reported confirmed case across 106 health zones in four provinces affected by the 2026 Bundibugyo virus disease outbreak (47 affected, 59 at risk, 26 July 2026), comparing four connectivity specifications,none, road-distance, a gravity score, and an incidence-weighted force-of-infection (FOI) term, fitted within an identical Bayesian hierarchical hazard architecture. Evaluation used a rolling-origin design, cluster bootstrap resampling, leave-one-origin-out and non-overlapping-origin checks, and a kernel-parameter sensitivity grid, with top-10 hit rate the pre-specified primary metric, matched to the operational question of which few zones warrant attention; AUC-PR, top-5 hit rate, and median rank percentile were secondary. FOI had the highest top-10 hit rate (42.6%), approaching conventional significance against road-distance and no-connectivity comparators. On AUC-PR, a model with no connectivity term performed as well as or better than any connectivity specification (0.437 vs. 0.409 for FOI), a discrepancy we report rather than omit. Rankings were stable across the sensitivity grid (Spearman; 0.90-0.99) and across robustness checks. An incidence-weighted connectivity term modestly and specifically improves identification of the highest-risk zones, concentrated in top-k ranking rather than uniform across metrics. The evaluation is pseudo-prospective, since historical data-vintage snapshots could not rule out retrospective revision, pending verification via a pre-registered top-20 ranking. Keywords: Bundibugyo virus disease; Ebola; spatial epidemiology; hazard model; Bayesian statistics; Democratic Republic of the Congo; disease surveillance

Cyclospora cayetanensis causes significant seasonal foodborne illness, characterized by recent large-scale nationwide outbreaks. We are currently experiencing the largest recorded outbreak in USA history during the summer of 2026. The summer 2026 outbreak emphasizes the urgent need for enhanced surveillance of infectious diseases. In this study, we use phylogenetic network analysis via the StrainHub framework to reconstruct the historical spread of C. cayetanensis in the USA using only mitochondrial sequence data and place of isolation metadata collected from 1997 to 2022. By quantifying parasite importation to localities through the metric indegree centrality, we identify Texas as a primary sink for C. cayetanensis introduction. Our finding aligns with historical epidemiologic investigations such as the 2013 multistate outbreak. While our model demonstrates the efficacy of reconstructing transmission pathways from molecular data alone, it also reveals critical surveillance gaps, particularly the masking of geographic origins in public datasets and the current absence of molecular data for the 2026 event. Moving forward, integrating real-time molecular surveillance with robust network analysis is essential to shift from reactive to proactive intervention strategies. We conclude that improved data sharing and reporting across food, clinical, and farm sectors are vital for disrupting transmission cycles of parasites. This work will protect public health and ensure a fresh clean supply of key foods for the nutrition of Americans.

Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC's FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024-25 and 2025-26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.

Human-to-human transmission of the Andes virus (ANDV), which causes the deadly Hantavirus Pulmonary Syndrome, has been documented in several outbreaks, but remains insufficiently characterized to ensure preparedness against future emergence. Here, we integrate epidemiological data from historical outbreaks and from the 2026 MV Hondius outbreak with a stochastic network-based transmission model to quantify ANDV epidemic potential and control requirements. We estimate a reproduction number of $R_0=2.30$ (95\% CI: 1.33--3.27), supporting sustained human transmission. Uncertainty and sensitivity analysis show that variation in transmissibility and incubation period mainly affects outbreak timing and size, while superspreading events can substantially alter the epidemic evolution. Model analysis and simulations further reveal how contact-network structure strongly modulates outbreak size, with close-contact clustering facilitating containment. Due to the virus characteristics, rapid identification and isolation can effectively suppress outbreaks, even more effectively than in the case of COVID-19, whereas pre-symptomatic transmission or increased mixing could substantially increase risk. Our results provide a quantitative framework for ANDV preparedness and highlight the most critical transmission features for future surveillance.

Mass gathering events (MGEs) play a critical role for infectious disease dynamics on a population level as they provide opportunities for superspreading; however, underlying mechanisms remain insufficiently understood. We analyzed nationwide GPS-based, individual-level location data from mobile phone users in Germany between April and August 2024 with 16m spatial precision. Potentially infectious contacts were inferred from close co-location and linked to contact settings using OpenStreetMap data. Various MGEs, including EURO 2024 matches, major concerts, festivals, and fairs were compared using a common contact metric. Non-football events generated substantially more contacts than football events. While overall national contact numbers remained stable, MGEs produced so-called "small-world" contacts which gather people from distant locations into close proximity and could strongly enhance infectious disease dynamics. Crucially, most high-risk contacts occurred within two hours before the event, not at the event itself, and concentrated in public transport, leisure, and event-adjacent areas. Our work provides the first systematic and comparative evaluation of contact exposure across various types of MGEs and contact settings. Event-type-specific dynamics, particularly indirect and mobility-driven contacts, critically shape infection risk. These insights can inform accurate transmission modeling, targeted intervention and event-management strategies.

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.