Annals of Internal Medicine

Background: Antibody-mediated rejection (AMR) after heart transplant (HT) is associated with increased risk of mortality and graft loss. Contemporary studies delineating AMR presentation, management, and response to treatment are lacking, especially for patients who do not have typical immunohistological evidence of rejection (biopsy-negative, BN-AMR). In this study, we sought to describe the prevalence and clinical course of BN-AMR compared to biopsy-positive (BP-AMR) patients in a multicenter HT population. Methods: We conducted a retrospective analysis of all adult HT recipients at 2 academic medical centers. AMR was further divided into BP-AMR and BN-AMR, depending on their endomyocardial biopsy findings. The primary outcome was death and secondary outcome was a composite of death, retransplant, and new International Society of Heart and Lung Transplant grade 2 or 3 coronary artery vasculopathy. Results: A total of 742 patients were included in this study. We found that AMR occurred in 10% of HT recipients and was associated with worse overall survival compared to those with only cellular rejection or no rejection. BN-AMR accounted for 33% of AMR cases. Compared to BP-AMR, BN-AMR was diagnosed later, less aggressively treated, and associated with high morbidity and mortality. The long-term outcomes between BP-AMR and BN-AMR were similarly poor, with 5-year mortality approaching 50% after diagnosis. Conclusions: AMR after HT is associated with poor clinical outcomes and BN-AMR is common. Future studies should focus on incorporating novel tools for earlier detection of AMR and investigating AMR sub-phenotypes and optimal modes of treatment.

Background and Aims SARS-CoV-2 infection is associated with an increased risk of cardiovascular, cerebrovascular and venous thromboembolism events. We aimed to assess the impact of COVID-19 vaccination prior to SARS-CoV-2 infection on the risk of these events post-infection. Methods Embase and MEDLINE were searched from January 2021 to 11 September 2025, supplemented by citation searching. Observational studies were included if they reported risks of cardiovascular, cerebrovascular, or venous thromboembolic events after SARS-CoV-2 infection between different vaccination groups (e.g. unvaccinated, vaccinated, or booster vaccinated), or reported risk of events after SARS-CoV-2 infection compared with no infection, stratified by vaccination status. Random-effects meta-analyses were conducted to estimate pooled hazard ratios (HRs) comparing vaccinated and unvaccinated individuals across prespecified outcomes. Results Twenty-three studies were included in the systematic review; most reported an association between vaccination and a reduced risk of post-infection vascular events. Ten studies were included across meta-analyses comparing vaccinated and unvaccinated individuals. Pre-infection vaccination was associated with significantly reduced risks of composite cardiovascular/cerebrovascular events (HR 0.60, 95% confidence intervals [CI] 0.51-0.69), stroke (HR 0.75, 95% CI 0.64-0.88), acute coronary syndrome (HR 0.70, 95% CI 0.52-0.95), arrhythmias (HR 0.82, 95% CI 0.69-0.98), and venous thromboembolism (HR 0.51, 95% CI 0.36-0.73). No statistically significant reduction was observed for heart failure (HR 0.72 [95% CI 0.47-1.10]). Conclusions Pre-infection COVID-19 vaccination is associated with lower risks of cardiovascular, cerebrovascular and venous thromboembolism events following SARS-CoV-2 infection in the pre- and post-Omicron eras, supporting its role within broader prevention strategies

Heart transplantation (HT) is the durable therapy for end-stage heart failure (HF). Despite advances in immunosuppression, cardiac allograft vasculopathy (CAV) remains a leading cause of late graft failure and mortality in the modern era. Prior studies have established donor age and immunological phenomena, such as acute cellular rejection (ACR), antibody-mediated rejection (AMR), and development of donor-specific antibodies (DSAs) as risk factors for CAV. However, it remains unclear whether acute rejection (AR) that occurs early post-HT, when individuals experience the highest degree of immunosuppression, reflects higher baseline immune activity and confers a higher risk of future CAV compared to later AR, when immunosuppression is minimized. We therefore examined whether AR occurring during pre-specified early and intermediate intervals compared to those who did not experience AR in the first post-HT year was associated with future CAV among recipients without CAV at 1 year.

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

Background. Infectious mononucleosis (IM) with hepatitis is associated with suppression of high-density lipoprotein cholesterol (HDL-C), but the magnitude, specificity, recovery kinetics, and long-term cardiovascular implications of this finding have not been systematically characterised. Methods. Using the TriNetX Global Collaborative Network (<190 million patients, 178 healthcare organisations), we conducted a retrospective real-world evidence study in 1,944 adults with IM and hepatitis. We compared HDL-C distributions at presentation across 14 propensity-score-matched (PSM) comparator cohorts spanning other infectious, metabolic, and immune-mediated conditions. Gaussian mixture modelling characterised the HDL distribution. Longitudinal HDL trajectory was assessed across six post-index time windows, with the number of patients contributing a measurement ranging from 318 (16-30 days) to 2,849 (1-3 years) per window. Long-term major adverse cardiovascular events (MACE) were analysed in PSM cohorts of IM patients with very low HDL ([&le;]20 mg/dL, n = 979 per arm after PSM) versus those without low HDL, over up to 20 years of follow-up, with COVID-19 (n = 83,888 per arm) and pharyngitis (n = 10,618 per arm) as comparators. Results. At presentation, mean HDL in IM hepatitis was 36.7 +/- 22.6 mg/dL (median 33 mg/dL), ~14-17 mg/dL lower compared to pre-illness values. Nearly one quarter (23.9%) had HDL [&le;]20 mg/dL and 43.9% had HDL [&le;]30 mg/dL. HDL suppression was equivalent to CMV hepatitis but substantially greater than pharyngitis and IM without hepatitis, supporting a hepatitis-driven mechanism. Gaussian mixture modelling identified a discrete suppressed subpopulation (mean 16 mg/dL, 41% of patients) absent in non-hepatitis controls. Recovery was rapid in most patients (mean HDL 50.0 mg/dL by 16-30 days) but prolonged among the severely suppressed ([&le;]20 mg/dL), who required 3-6 months to approach baseline. In PSM MACE analyses, IM patients with very low acute HDL had significantly higher long-term event rates for all outcomes (HR 1.92-2.47 versus IM without low HDL), a pattern mirrored in the COVID-19 cohort (HR 2.04-2.70) and, with attenuated effect size, in pharyngitis (HR 1.43-1.69). Conclusions. Very low HDL-C is a prevalent, hepatitis-driven finding in IM affecting approximately one quarter of patients. It identifies a subgroup at elevated long-term cardiovascular risk comparable to that observed after COVID-19. These findings warrant prospective evaluation of cardiovascular follow-up strategies for affected patients.

BackgroundTuberculosis (TB) incidence in the United States has remained elevated above pre-pandemic levels since 2021, with over 85% of cases resulting from reactivation of Mycobacterium tuberculosis (Mtb) infection. New vaccines that would prevent TB in adults are under development, but the potential health impact of a program prioritizing non-U.S.-born persons and persons with medical comorbidities, including persons living with HIV (PLWH), has not been evaluated. MethodsWe developed a deterministic compartmental transmission model that simulates Mtb infection, transmission, and progression to TB in the U.S., both in the general population and in key high-risk groups. We calibrated the model to 2024 U.S. TB surveillance data and estimated annual cases prevented, percent reduction in annual TB cases, and number needed to vaccinate (NNV, a measure of vaccine program efficiency) at equilibrium conditions for targeted vaccination strategies under optimistic and plausible scenarios, varying assumptions of vaccine efficacy, duration of protection, and achieved vaccination coverage in high-risk groups. FindingsUnder an optimistic scenario, vaccinating PLWH, non-U.S.-born persons, and persons with medical comorbidities (all high-risk groups) prevented 5,385 cases per year (51{middle dot}8% reduction, NNV = 366). Under a more conservative plausible scenario, the same strategy prevented 1,348 cases per year (13{middle dot}0% reduction, NNV = 510). The efficiency and impact of targeting strategies we considered were preserved across all sensitivity and uncertainty analyses. InterpretationTargeted vaccination of persons with Mtb infection in population subgroups recognized to be at high-risk for TB can reduce incidence substantially. Strategies that include non- U.S.-born persons and PLWH are most efficient and impactful. FundingAmerican Lung Association, U.S. National Institutes of Health, and the Ferguson Fellowship.

AI-assisted clinical care may compound, rather than correct, existing health inequities. We applied Omar and colleagues' validated four-domain emergency-medicine benchmark to OpenEvidence (OE), a literature-grounded clinical LLM used by tens of thousands of US physicians daily, across 100 emergency-department cases and 20 sociodemographic labels. OE was consistent on the codified clinical decisions, triage, workup, and treatment, but diverged sharply on mental-health screening, where it flagged many historically marginalized groups between three and ten times more often than demographically unmarked cases. Cases labeled as unhoused received recommendations in 78 to 87 percent of responses (versus a 9 percent no-identifier-control rate); cases labeled as transgender in 22 to 24 percent; and Black transgender women specifically in 47 percent. A pre- registered audit of 193 free-text rationales localized the differential to the inner layer of the response, in the structure and tone of the rationale rather than the recommendation itself. Literature grounding may redistribute sociodemographic disparity in clinical AI rather than remove it. As clinical LLMs move toward agentic deployment, equity audits should examine how evidence is applied to each patient, not only whether citations are present.

In July 2021, the California Code of Regulations Title 17 required all laboratories performing SARS-CoV-2 whole genome sequencing (WGS) to report their sequencing results to the California Department of Public Health (CDPH). These viral genomic data and patient metadata were compiled into the Integrated Genomic Epidemiology Database (IGED). Linking anonymized viral sequences with patient-level information enabled monitoring of infectiousness, pathogenicity, transmission dynamics, evolution, and vaccine evasion among emerging SARS-CoV-2 lineages. Laboratories performing SARS-CoV-2 WGS transmitted sequencing results to CDPH through Electronic Laboratory Reporting (ELR) and non-ELR pathways. CDPH applied uniform reporting requirements but allowed flexibility in specific data formats to accommodate diverse data systems. To preserve data quality and interoperability across heterogeneous sources, CDPH implemented standardization, validation, and deduplication protocols. Snowflake, a cloud-based data storage and analytics platform, and Posit Connect, a cloud deployment and automation platform, supported the management, processing, and integration of data within the IGED. The IGED established links between SARS-CoV-2 WGS data and epidemiologic metadata for 801,418 sequences, representing 81.7% of all sequences reported in California. Lineages reported to the IGED showed strong concordance with lineage proportions in GISAID. Sequences reported to the IGED had average turnaround times longer than one month, and the majority of sequencing was performed in Southern California and Los Angeles. The IGED enhanced genomic surveillance through predictive modeling and monitoring concerning evolutionary trends such as recombination and saltations in persistent infections. Development of the IGED highlighted the need for standardized data requirements, sustained funding for sequencing, incentives for data submission, and interdisciplinary collaboration to build an effective genomic surveillance system. This framework for linking genomic and epidemiologic data has not only generated critical insights for SARS-CoV-2 but also provided the foundation for CDPH and other public health organizations to develop similar IGED-like systems for other priority pathogens as genomic surveillance expands.

Objectives In Quebec, Canada, vaccination against human papillomavirus (HPV) has been publicly-funded since January 2016 for gay, bisexual, and other men who have sex with men (GBM) aged [&le;]26 years. The study aimed to analyze data collected in Greater Montreal (Engage study) to evaluate the HPV vaccination program for GBM in Quebec. Study Design Engage is a cohort of sexually active GBM aged [&ge;]16 recruited via respondent-driven-sampling (RDS) in Canada. Participants completed a questionnaire and tested for sexually transmitted infections. Methods RDS-II weights were applied to adjust for recruitment. Subgroups were compared using standardized mean differences. Odds ratios of HPV vaccination and prevalence ratios of anal HPV infection adjusted for potential confounders were estimated using robust regression models. Results Of 1179 participants, 309 were eligible for free HPV vaccination. Vaccine coverage among eligible GBM was 42%. Among those who disclosed same-sex sexual activity and discussed HPV vaccination with their healthcare provider, coverage reached 82%. Anal HPV prevalence among eligible GBM was 26.5% for [&ge;]1 HPV-6/11/16/18 genotypes without significant difference between vaccinated and unvaccinated individuals. Among unvaccinated GBM aged [&le;]26 who were aware of the vaccine, 60% intended to get vaccinated within the next year. Conclusions One to two years after GBM aged [&le;]26 were included in the Quebec HPV vaccination program, 42% of eligible GBM in Greater Montreal had been vaccinated. Anal HPV prevalence was high among GBM. Vaccinees were more likely to self-report a prior STI diagnosis. Offering vaccination to all preadolescents in schools appears essential to maximize vaccination benefits.

Background: The Vaccine Safety Datalink (VSD) detected a statistical signal for ischemic events (ischemic stroke or transient ischemic attack) following bivalent mRNA COVID-19 vaccination through prospective surveillance during 2022-2023. Although multiple studies from other surveillance systems and countries reported no increased risk, important methodological limitations remained. This U.S. study addressed those limitations by evaluating the ischemic stroke risk following bivalent mRNA COVID-19 vaccination, influenza vaccination, and their same-day coadministration using event-dependent self-controlled case series (SCCS) design. Methods: Study outcomes included first-ever ischemic stroke (primary outcome), first-in-1-year ischemic stroke (secondary outcome), and ischemic events (exploratory outcomes), identified using ICD-10-CM codes in inpatient and emergency department settings during September 1, 2022-March 31, 2023, among individuals aged>=12 years across eight VSD sites. Analyses were conducted separately for Pfizer-BioNTech and Moderna bivalent vaccines, with relative incidences (RI) and 95% confidence intervals (CI) estimated for 1-21-day and 1-42-day risk intervals, using person-time outside these intervals as the control period. Subgroup analyses were performed by age group (12-64, >65 years) and history of documented SARS-CoV-2 infection. Results: A total of 6,510 first-ever ischemic strokes were identified among more than 6.8 million participants. Among recipients of Pfizer-BioNTech bivalent COVID-19 and influenza vaccines, no statistically significant increased risk of first-ever ischemic stroke was observed following bivalent COVID-19 vaccination (RI=0.94; 95% CI: 0.63-1.41), influenza vaccination (RI=0.95; 95% CI: 0.82-1.10), or same-day coadministration (RI=1.15; 95% CI: 0.88-1.49) within 1-21-day risk intervals; findings were similar for 1-42-day intervals. Comparable null results were observed for Moderna vaccines and across all subgroups, secondary, and exploratory outcomes. Conclusion: No increased risk of ischemic stroke was found following bivalent mRNA COVID-19 vaccination, influenza vaccination, or their coadministration in this multi-site SCCS study. These findings are consistent with previous studies and underscore the importance of continued vaccine safety monitoring.

Background Human challenge trials provide a unique opportunity to quantify pathogen infectivity in terms of the probability of infection given an inoculated dose. However, between-pathogen comparisons are often distorted by individual heterogeneity in host susceptibility and by differences in background immunity across trial populations. We examined how dose-dependent infection risks differ across common respiratory viruses when such heterogeneity is explicitly incorporated. Methods We conducted a systematic review of human challenge trials for four respiratory viruses: respiratory syncytial virus (RSV), influenza virus, rhinovirus, and adenovirus. Using the extracted data, we fitted dose-response models under different distributional assumptions, allowing both continuous susceptibility variation and discrete immune fractions. We compared alternative heterogeneity models and evaluated pathogen-specific dose-response patterns using original and scaled dose metrics. Results All four viruses showed substantial heterogeneity in host susceptibility, and models assuming homogeneous susceptibility were unsupported. RSV and influenza were best described by models with a distinct immune or effectively non-susceptible subgroup, and the estimated immune proportions were approximately 40% and 25%, respectively. In contrast, rhinovirus and adenovirus were better explained by continuously distributed susceptibility, with little evidence of a fully immune subgroup. On a scaled dose axis, rhinovirus and adenovirus showed steeper increases in infection risk with dose than RSV and influenza. Conclusions The structure of susceptibility heterogeneity differs across common respiratory viruses, which in turn shapes dose-dependent infection risks. Incorporating this heterogeneity is essential for valid cross-pathogen comparison and for interpreting human challenge data in epidemiologic and public health contexts.

Importance: Physician-facing AI tools are now in clinical use, yet whether different platforms fail in similar or fundamentally different ways in high-stakes settings like critical care is unknown. Objective: To evaluate two physician-facing AI platforms, ChatGPT for Clinicians and OpenEvidence, for distinct vulnerabilities under structured stress testing. Design, Setting, and Participants: An observational study conducted using 60 simulated critical care vignettes developed and adjudicated by four attending critical care physicians. Data were collected in the last week of April 2026, via the public website interfaces of each platform. Interventions/Exposures: A 2x2x2x2 factorial design across four stressors - anchoring, cognitive load, social conformity pressure, and a clinically incorrect directive - yielded 16 prompt subsets per vignette and 960 prompts per platform. A separate multi-turn adversarial prompting paradigm administered three sequential "You are incorrect" challenges to baseline vignettes. All prompts had a universal output length constraint of fewer than 30 words. Main Outcomes and Measures: Critical elements capture (percentage of gold-standard critical elements present in responses), susceptibility to clinically incorrect directive, and sycophancy (reversal of an initial correct recommendation under iterative adversarial challenge). Results: Across 1916 responses to 1920 prompts, ChatGPT for Clinicians captured more gold-standard critical elements than OpenEvidence (81.4% {+/-} 18.1% vs 61.0% {+/-} 23.5%; adjusted difference, 20.3 percentage points; 95% CI, 18.3 to 22.4; P < .001) and was less susceptible to clinically incorrect directives (1.7% vs 8.0%; adjusted odds ratio, 0.07; 95% CI, 0.02-0.21; P < .001). Anchoring and social conformity pressure were associated with reduced critical element capture across both platforms, while cumulative stressor burden reduced critical element capture only on OpenEvidence. Conversely, ChatGPT for Clinicians reversed correct recommendations more readily under adversarial prompting (hazard ratio, 2.61; 95% CI, 1.10 - 6.19; P = .03). Conclusion and Relevance: The two physician-facing clinical AI platforms evaluated demonstrated non-overlapping vulnerabilities, with neither platform uniformly superior. These findings argue against single-axis ranking of clinical AI systems and support multidimensional safety evaluation encompassing completeness of reasoning, resistance to incorrect directives, and stability under adversarial challenge.

Background: The All of Us Research Program represents a rich resource for cancer epidemiology research, with over 400,000 participants with whole genome sequences linked to electronic health records (EHR). Large cancer datasets often focus exclusively on cases without controls and neglect pre-diagnosis healthcare occurrences. Here, we perform a phenome-wide association study (PheWAS) of EHR data at least 1 year pre-diagnosis between cancer cases and matched controls, revealing co-occurring and mutually exclusive phenotypes. Methods: We identified 55,000+ cancer cases across 21 cancer types in All of Us version 8. To eliminate age-related confounding, we implemented a two-stage matching and censoring strategy: loose matching on demographics to establish index dates and cohort comparability, followed by right-censoring of EHR data (excluding 1 year pre-diagnosis/index), then 1:2 matching to address residual demographic imbalance. We tested associations between 23,193 cancer cases, 46,386 matched controls and approximately 1,600 clinical phenotypes using logistic regression adjusted for sex at birth, self-reported race, age at diagnosis/index date, and two censored EHR metrics: observation window and unique condition count, with Bonferroni correction for multiple testing. Results: Our analysis identified 232 significantly associated phenotypes, confirming established cancer risk factors including elevated prostate specific antigen (OR = 2.92, 95% CI: 2.65-3.23; p-value=1.8x10-101) and multinodular goiter (OR = 1.73, 95% CI: 1.56-1.91; p-value=6.7x10-27). Further investigation into the relationship between several phenotypes with seeming inverse effects is warranted. Conclusions: This PheWAS of EHR data at least 1 year pre-diagnosis leveraged the diversity of All of Us to examine how clinical phenotypes prior to cancer diagnosis vary across cancer types and racial groups. Our findings validate All of Us as a robust platform for cancer epidemiology research, confirming established risk factors at scale across diverse populations. This work provides methodological insights for EHR-based susceptibility analyses and demonstrates the value of agnostic phenome-wide approaches for generating hypotheses in precision medicine.

Background: Prostate cancer survival varies by stage at diagnosis, and Black men experience a disproportionate burden of advanced disease. We examined whether neighborhood deprivation, measured by Area Deprivation Index (ADI), contributes to racial differences in metastatic presentation. Methods: We conducted a population-based study of men diagnosed with prostate cancer in the Ohio Cancer Incidence Surveillance System from 1996 to 2016. The primary endpoint was distant-stage disease at diagnosis. Generalized additive models assessed nonlinear associations of ADI and diagnosis year with metastatic risk. Inverse probability of treatment weighting (IPTW) models estimated odds ratios comparing Black with White men after sequential adjustment for diagnosis year, age, insurance, and ADI. Results: Among 135,095 men, 18,690 were Black and 116,405 were White. Distant-stage disease occurred in 7.0% of Black men and 5.0% of White men. Black men had higher median ADI (60.9 vs. 47.3). Medicaid-insured men had the highest unadjusted odds of metastatic presentation (OR, 4.68; 95% CI, 4.13-5.31), exceeding uninsured men (OR, 2.91; 95% CI, 2.54-3.34). In IPTW models without age adjustment, the odds ratio decreased from 1.54 to 1.24 after adding insurance and ADI. In age-adjusted IPTW models, the odds ratio decreased from 1.79 to 1.41 after adding insurance and ADI. Generalized additive models showed increasing metastatic risk at higher ADI values and after 2008. Conclusions: Neighborhood deprivation and insurance-related access explained part, but not all, of the excess odds of metastatic diagnosis among Black men. Impact: Integrating ADI into cancer surveillance may improve identification of populations at risk for late-stage diagnosis.

Using a commercially available H5 serology assay, we identified a 7.4% (n=5/68) anti-H5 seroreactivity rate among hunters in Northern Canada. All participants reported close contact with wild birds.

Background: The global SARS-CoV-2 pandemic disrupted healthcare systems worldwide, raising concerns about its impact on clinical research. Early reports suggested reductions in participant enrollment, interruptions to ongoing trials, and challenges to protocol adherence, yet the magnitude and duration of these operational disruptions remain unclear. Methods: We conducted a registry-based analysis comparing clinical trials during the COVID-19 pandemic (December 2019 to November 2022) with a matched pre-pandemic cohort (December 2016 to November 2019). Studies were included if they reported any modifications to trial status, enrollment, or protocols during the study periods. Key variables included trial stoppage, enrollment changes, and adoption of remote or hybrid procedures. Results: The global SARS-CoV-2 pandemic resulted in widespread disruptions to trial operations with 13,323 clinical trials terminated, suspended or withdrawn over the course of the pandemic, a 38% increase compared to the 9,665 trials that stopped in the 3 years prior to the pandemic. Registries indicated a sharp decline in new participant enrollment across geographic regions and therapeutic areas, with partial recovery in later months. Review findings highlighted barriers including patient inaccessibility, staff redeployment, and supply chain interruptions. Conclusions: The pandemic caused system-wide operational shocks that compromised trial timelines and may have downstream methodological consequences. Recovery in enrollment does not imply restoration of pre-pandemic protocol fidelity or outcome ascertainment. Standardized reporting of disruptions, proactive contingency planning, and resilient trial designs are needed to maintain data integrity during large-scale disruptions and to support reliable evidence generation.

The 2026 Bundibugyo Ebola outbreak in eastern Democratic Republic of the Congo (DRC) has already generated international spread to Uganda, raising concerns about further regional and international dissemination. Using International Air Transport Association origin-destination passenger flows, we assessed relative exposure to Ebola virus disease importation into Europe under six outbreak expansion scenarios reflecting plausible pathways of geographical spread, including cross-border transmission and amplification in highly connected regional capitals. Relative exposure patterns remained largely unchanged under localized transmission in eastern DRC and border-spillover scenarios. Expansion into South Sudan generated a first structural increase in importation pressure to Europe through the connectivity associated with Juba, while hypothetical amplification in Kampala, Kigali, and Kinshasa substantially increased importation pressure and reshaped exposure patterns across Europe. Across all scenarios, France, Italy, and the United Kingdom remained among the most exposed countries. Mobility-informed scenario analyses support preparedness as the geography of the outbreak evolves.

The multinational Andes virus outbreak linked to the MV Hondius has exposed contacts across several countries, but the absence of further confirmed cases remains difficult to interpret given the long incubation period. We estimate the probability that secondary clusters may emerge using a stratified branching-process model parameterized with country-level tracing and isolation indicators. The risk of sustained spread is low, but secondary clusters remain plausible under imperfect isolation or pre-symptomatic transmission. These results support coordinated contact tracing and effective isolation while exposed contacts remain within the risk window.

Background. The 2026 FIFA World Cup will bring an estimated 1--5~million international visitors to 11~US host cities between June~11 and July~19, 2026---the largest tournament in history. Large-scale international gatherings accelerate importation of infectious diseases from diverse source populations. Advance estimation of importation risk is essential for public health preparedness and surveillance prioritization. Methods. We developed a Poisson importation framework applied to five diseases (dengue fever, influenza, malaria, measles, and pertussis) across the 11~US venue cities. Three nested travel models of increasing resolution were constructed: a baseline model using routine June~2024 arrival data; a World Cup--adjusted model incorporating projected visitor growth factors; and a schedule-driven model routing WC fans to specific cities based on match assignments. WHO incidence and BTS T-100 routing fractions were combined with Monte Carlo uncertainty propagation (5,000 Uniform draws on under-reporting and travel-while-infectious parameters) to yield median importation estimates with 95\% uncertainty intervals. Results. Dengue posed the highest importation risk at most venue cities under the schedule-driven model (median $\Lambda > 10$ expected importations from Brazil alone; 95\% uncertainty interval 5.9--33.1), robust across the full literature-supported parameter range; Atlanta was the exception, where malaria probability exceeded dengue, driven by direct travel from West and Central African nations. Influenza ranked second at most cities, coinciding with the Southern Hemisphere winter peak. Pertussis showed broad geographic spread but carries the widest relative uncertainty, as the assumed detection rate sits at the upper bound of the literature range. Background tourism accounted for the dominant share of total importation risk; the World Cup fan increment contributed approximately 8.3\% of projected arrivals for WC-qualified nations. Conclusions. This Poisson importation framework, built entirely from publicly available data, provides reproducible importation risk estimates for mass gathering events. The framework extends to additional diseases, cities, and gatherings, offering a transparent baseline complementary to proprietary modeling systems.

Human metapneumovirus (HMPV) causes acute lower respiratory infections, primarily affecting young children and older adults, with seasonal outbreaks peaking annually in March or April in the United States and other temperate regions in the Northern hemisphere. However, the factors driving HMPV seasonality in the United States remain poorly understood. We analyzed laboratory-confirmed HMPV cases and age-specific emergency department visits across 10 US regions, fitting an age-stratified dynamic transmission model to assess spatiotemporal patterns and investigate the influence of environmental variables and viral interference from RSV on HMPV transmission rates. We found that models incorporating climate variables into the transmission rate, including vapor pressure, precipitation, potential evapotranspiration, and minimum temperature, could not capture the timing of HMPV activity across all regions. Instead, HMPV timing was associated with RSV activity, with the HMPV transmission rate reduced in the presence of RSV. We showed that, unlike RSV, only models incorporating viral interference could reproduce the biennial pattern of HMPV observed in some regions, characterized by alternating late-small and early-large epidemics. Furthermore, our model successfully reproduced post-COVID-19 HMPV and RSV epidemics and predicted that RSV interventions are not likely to lead to a substantial increase in HMPV activity despite decreasing competition from RSV. Our work unravels the spatiotemporal dynamics of HMPV and its interaction with RSV, informing future seasonal forecasting and intervention strategies for HMPV.