Epidemiology and Infection

Background: High national vaccination coverage may conceal age-specific and spatially concentrated measles susceptibility. Objective: To assess whether published age-specific seropositivity results can be converted into a time-updated susceptibility profile that corresponds with subsequent measles incidence, while distinguishing susceptibility from infectious introductions and transmission conditions. Methods: For Israel, published 2015 age-specific seropositivity estimates were mapped to monthly birth cohorts and projected to 1 March 2018, accounting for births, aging, maternal antibody, routine vaccination, vaccine effectiveness, and uncertainty in assay interpretation. The primary outcome was reported age-specific incidence during the 2018-2019 outbreak; national and Jerusalem District case burdens were secondary outcomes. Published evidence from the Netherlands, Czechia, and Australia was compared using a common framework covering age distribution, assay classification, vaccination, importation, spatial concentration, and transmission context. Results: The estimated number susceptible in Israel on 1 March 2018 ranged from approximately 0.55 million (6.3% of the modelled population) to 1.92 million (22.4%), with a central estimate of 1.22 million (14.2%). Children aged <1 year had the highest central susceptible proportion (75.7%) and the highest later incidence (196.0 per 100,000). Jerusalem District accounted for 2,202 of 4,311 reported national cases, consistent with susceptibility concentrated in communities with lower first-dose coverage. The external comparisons showed that clustering amplified Dutch outbreak risk, survey design affected Czech estimates, and importation dominated Australian activity. Conclusions: Published seropositivity can identify immunity gaps, but useful outbreak-risk assessment must also represent susceptible density and distribution, introduction pressure, and local transmission conditions. Although the model was not designed to compare alternative vaccination schedules directly, its identification of substantial susceptibility during early childhood provides epidemiological support for Israel's recent decision to advance the second routine MMRV dose from 6 years to 18 months of age, thereby shortening the period during which young children remain dependent on single-dose protection.

Background Clostridioides difficile infections (CDI) continue to cause substantial morbidity and mortality, with increasing incidence in some countries post-COVID-19. Recurrence affects 10-20% of CDI patients: whether risk factors have changed over time is unclear, particularly post-COVID-19. Methods Using electronic health records from hospitals in Oxfordshire, UK, including results from community samples, we estimated the incidence of CDI recurrence across different calendar periods between 2000-2025. To investigate whether risk factors had changed over time, we used interaction tests within multivariable Fine-Gray regression models (modelling death as a competing risk), using backwards elimination, starting from 39 risk factors identified from the literature. Results Recurrence occurred within 1-year in 12.6%, 12.5%, 10.8% and 13.1% of CDI cases in "high virulence" 2003-2008 (n=527), "post-high virulence" 2008-2012 (n=252), "pre-pandemic" 2012-2020 (n=143) and "post-pandemic" 2020-2025 (n=110) periods respectively. The only factor with evidence of variation in its association with recurrence over time was surveillance classification (heterogeneity p=0.019; others p>0.05). Compared with hospital-onset cases, community-onset community-associated cases had increasing recurrence risks over time (subdistribution hazard ratios (SHRs)=2.05 (95% CI 1.27-3.31) in "post-pandemic" and 1.86 (1.16-2.98) in "pre-pandemic"), especially compared to the "high virulence" era of the early 2000s (SHR=0.81 (0.56-1.16)). This heterogeneity persisted after adjusting for baseline blood tests as a marker of severity (heterogeneity p=0.022). Conclusions Compared with hospital-onset hospital-associated cases, recurrence risks increased for community-onset community-associated CDI cases over time. Recurrence is a key contributor to overall CDI incidence and the underlying causes of increasing risks with community-onset community-associated cases should be explored further.

Communities exposed to flooding can experience markedly different mental health outcomes, yet conventional resilience indicators capture only part of the social and contextual conditions that may explain this variation. This study develops a multilevel and predictive framework for examining community resilience and depressive symptoms following flood exposure in Indonesia. Data were drawn from 20,303 respondents aged 15 years and older nested within 312 communities in the Indonesia Family Life Survey (IFLS-5). Depressive symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression Scale (CES-D-10), with Rasch Partial Credit Model calibration used to examine measurement properties. Bayesian multilevel models quantified between-community heterogeneity and assessed how far observable structural resources accounted for this variation. Community resilience was represented through two complementary constructs: structural resilience, based on observable socioeconomic and social-capital resources, and Latent Community Protective Capacity (LCPC), a model-derived proxy for residual contextual variation in depressive-symptom risk. Approximately 6 percent of variation was attributable to between-community differences, while observable structural resources explained only part of this heterogeneity. Structural resilience and LCPC were weakly correlated (r = 0.155). Moderation analyses provided no clear evidence that structural resilience altered the flood-depression association, while LCPC showed a directionally consistent but uncertain buffering pattern. Predictive models incorporating community-level information improved discrimination, with the best-performing model reaching an ROC-AUC of approximately 0.71. The findings suggest that observable resource-based indices provide an incomplete account of community-level mental health vulnerability and that residual contextual measures may provide complementary information, while requiring cautious interpretation and independent validation.

Official Lyme disease incidence figures for England are based on the number of cases with a laboratory-confirmed diagnosis from the national reference laboratory. However, these figures underestimate the true incidence of acute cases since patients can also be diagnosed clinically without the need for laboratory testing. The use of a multiplication factor has been proposed to estimate total Lyme disease incidence from the number of laboratory-confirmed cases. The current multiplication factor is derived from data spanning 1998 to 2016 and is thought to be outdated, in part due to the introduction of new national Lyme disease management guidance in 2018. The aim of this study was to analyse more recent laboratory and primary care data to identify trends in case numbers and to derive updated estimates of the total annual number of acute Lyme disease cases in England. Data spanning 2013 to 2024 from the national reference laboratory and a primary care data source were analysed using seasonal time series models. The presence of structural breaks was investigated using the PELT or BinSeg methods, and annual multiplication factors were estimated using a Poisson regression model. No substantial change was found in the number of laboratory-confirmed Lyme cases from 2013 to 2024 in England, whereas the number of cases diagnosed in primary care increased significantly over the same period, with an average of an additional 498 cases per year. This has increased our best estimate of the multiplication factor from 2.35 (95% confidence interval, 1.81 - 2.88) to 6.20 (6.17 - 6.24), which is the average for the years 2020 - 2024. Use of this updated multiplication factor should improve the accuracy of annual symptomatic Lyme disease case estimates for England.

Globally, Hepatitis C Virus (HCV) infection is a leading cause of chronic liver disease and remains a significant public health threat in Ghana. However, there is a paucity of data on the prevalence and awareness of HCV among market sellers and informal workers in Ghana. This study sought to determine the prevalence and awareness of HCV among this population. This was a cross-sectional study conducted among 397 sellers and workers at Kejetia market from December 2024 to January 2025. HCV was diagnosed using the One Test Hepatitis C Virus Test Strip (Qingdao Hightop) for the qualitative detection of HCV antibodies. Data were analysed using SPSS version 26 and GraphPad Prism version 8.0. A p < 0.05 was considered statistically significant for awareness of HCV. The prevalence of HCV in this study was 4 (1.0%). Awareness of HCV was low at 112 (28.2%). Residence (p=0.021) and age group (p=0.040) were significantly associated with HCV awareness. Age 41-50 years was almost three times more likely to be aware of Hepatitis C infection compared to [&le;] 30 years. Although the prevalence of HCV was low, awareness of the infection was also limited; this highlights the need for public health authorities to implement targeted HCV education programs in major markets to effectively reach informal sector workers who are often overlooked by conventional campaigns.

Abstract Background. During an active outbreak, deaths divided by confirmed cases can be mistaken for biological severity or quality of care even though outcomes remain unresolved and reporting and ascertainment differ across places. We examined what health-zone case-fatality ratios measured during the 2026 Bundibugyo virus disease epidemic in the Democratic Republic of the Congo. Methods and principal findings. We analysed national and health-zone cumulative confirmed cases and deaths through 20 July 2026. We calculated reported crude case-fatality ratios, examined case-death reporting synchronisation, and fitted beta-binomial partial-pooling models with four-chain Markov chain Monte Carlo. Exploratory models added mapped clinical-facility availability or distance to the nearest mapped hospital. Reporting-date delay, under-ascertainment, and mortality forecasting were evaluated as diagnostics, scenarios, or developmental analyses rather than patient-level fatality estimation. The national ratio reached 40.4% (999/2,473). Among 14 zones with at least 20 cases, crude ratios ranged from 28.3% to 68.5%. In the primary model, North Kivu had higher posterior odds than Ituri, but with substantial uncertainty (OR 1.80, 95% credible interval 0.92-3.37); epidemic maturity was positively associated (1.49, 1.04-2.19). In exploratory models, greater mapped clinical-facility availability was associated with lower reported fatality (0.67, 0.48-0.96), while greater distance to a mapped hospital was associated with higher reported fatality (1.46, 1.06-2.00). Same-day case-death co-reporting was common, and materially different reporting-delay assumptions fitted similarly. Interpretation. Health-zone ratios revealed meaningful surveillance heterogeneity but did not identify biological fatality risk or causal effects of facilities, access, care, or conflict. Epidemic maturity, selective ascertainment, referral, and administrative reporting plausibly shaped the numerator and denominator. These ratios should guide investigation rather than rank health-zone performance; linked patient records are required for clinical fatality and competing-risks analyses.

Background Scrub typhus is a major cause of febrile illness in Southeast Asia, with particularly high burdens reported in northern Thailand. Environmental exposures and use of preventive measures remain incompletely understood. Methods We conducted a population-based cross-sectional serosurvey in Chiang Rai province, Thailand in 2022-2023. Adults aged [&ge;]15 years from 74 villages were selected by two-stage random sampling and interviewed regarding demographic characteristics, agricultural and domestic exposures, and preventive behaviours. IgG antibodies against Orientia tsutsugamushi were measured using a Luminex xMAP Intelliflex(R) platform. Mixed-effects logistic regression models stratified by ethnicity (highland people vs Thai) were used to identify factors associated with seropositivity, with village as a random intercept. Results Among 1,194 participants with valid serology, seroprevalence was higher among highland people than Thai ones (60% vs 13%, p<0.001). Seropositivity increased with age in both groups (p<0.001, and p=0.006), more markedly among highland people. Exposure to forest/high hill (adjusted odds ratio [aOR] 2.87, 95%CI 1.07-7.70) and agroforestry plantations (aOR 1.74, 95%CI 1.04-2.99) were associated with seropositivity among Thai participants, but not among highland people. High-risk occupations were associated with increased odds of seropositivity across both groups, p=0.006 and p=0.043. No clear associations were observed between seropositivity and duration of exposure, domestic/peridomestic risk factors, individual preventive practices, or overall prevention quality. Significant village-level heterogeneity persisted after adjustment, particularly among highland people. Conclusions Scrub typhus exposure is common in rural Chiang Rai and disproportionately affects highland people; whose risk was poorly explained by classical environmental risk factors, suggesting distinct and incompletely understood exposure pathways. Current prevention strategies based on conventional risk messaging may therefore have limited effectiveness in these high-risk communities.

Introduction Measles remains a major public health threat worldwide. Following a resurgence of cases, Ho Chi Minh City (HCMC) experienced a severe outbreak in 2024-2025 that differed from the 2018-2020 epidemic. We compared the epidemiology and transmission dynamics of these two outbreaks to inform future surveillance and vaccination strategies. Methods Line-list data for measles cases reported in HCMC between 2018 and 2025 were extracted from the infectious disease surveillance system. Demographic changes were assessed using cross-sectional age distributions and longitudinal birth-cohort analyses. Transmission dynamics were characterized by estimating the effective reproduction number (Rt) using the Cori and Wallinga-Teunis methods. Age-stratified Who-Infected-Whom transmission matrices were constructed to quantify within- and between-age-group transmission before and after interventions. Results The 2018-2020 outbreak primarily affected children under five years of age (median age 3.75 years, IQR 0.9-9.1), whereas the 2024-2025 outbreak shifted toward older age groups (median age 8.4 years, IQR 1-14). Birth-cohort analyses identified two major immunity gaps: children born during the COVID-19 immunization disruption (2019-2023) and adolescents and young adults born before 2014 who were not eligible for the 2018 outbreak response immunization campaign. The 2024-2025 outbreak also showed broader spatial spread and a reversed geographic trajectory compared with the earlier epidemic. Transmission patterns changed substantially, with the 2018-2020 outbreak sustained mainly by transmission among young children, whereas the 2024-2025 outbreak was driven by older age groups, with individuals >15 years acting as an important source of infection for infants. Conclusion The epidemiology of measles in HCMC has shifted from predominantly pediatric transmission to increasing involvement of adolescents and adults, reflecting accumulated immunity gaps across multiple birth cohorts. These findings suggest that age-restricted outbreak response immunization may leave residual susceptible populations that contribute to future outbreaks. Achieving sustainable measles elimination will require vaccination strategies that address historical immunity gaps across all vulnerable age groups, in addition to maintaining high routine childhood vaccination coverage.

Violent conflicts increasingly involve multiple armed actors competing for influence over shared civilian populations, creating complex dynamics that challenge conventional security analysis and policy design. We present a framework that adapts epidemiological methods informed by the conflict landscape in Nigeria to model multi-actor violent conflict as an epidemic process. We derive a basic insecurity reproduction number ($R_0$), identify violence-free and persistent-violence equilibria, and introduce a novel Civilian Harm Index (CHI) to quantify humanitarian impact. Sensitivity analyses identify recruitment, ideological support from civilian populations, and abduction as the key drivers of conflict persistence and civilian harm. The framework reveals several counterintuitive findings. Interventions that most effectively suppress violence transmission are not necessarily those that minimise civilian harm, demonstrating that epidemic control and humanitarian protection may require distinct optimisation criteria. Likewise, interventions effective against one armed actor may be ineffective, or even counterproductive, when applied uniformly across groups. In addition, prisoner exchange and ransom payments increase violence persistence and civilian harm. Although developed as an illustrative rather than predictive framework, our results show that epidemiological methods provide quantitative metrics for evaluating intervention priorities and trade-offs in complex multi-actor conflicts.

Background Decision-making in emergency services involving the allocation of scarce resources is a key challenge for large, complex organisations required to prioritise demand against multiple, often competing criteria. Emergency Medical Dispatch is a case in point, where Enhanced and Critical Care Teams (ECCTs) represent a scarce and lifesaving clinical resource. Despite its operational and system-level significance, the allocation of ECCTs remains under-researched. Methods We conducted a methodological development study using the P.A.T.H.S. framework (Participants, Artefacts, Transition Stages, Historicity, Setting). We designed and piloted this in our previous work under the 999 R.E.S.P.O.N.D. project, which examined the decision-making process for ECCT dispatch. We applied P.A.T.H.S. to 17 dispatch cases (comprising 100 decision-making episodes). We analysed five data sources: recordings of emergency calls and internal dispatch-related interactions, sequence-of-events records, policy documents, and ethnographic observations. A four-phase analysis--indexing & data mapping, transcription & coding, charting, and synthesising & outputs--was undertaken taking Interactional Sociolinguistics as the theoretical approach and methodology. Results P.A.T.H.S. enables the mapping of non-linear, multifactorial textual trajectories across human and non-human actors. The case example presented herein illustrates how information on key risk indicators (e.g. mechanism of injury) were often delayed, fragmented, or lost between the caller, call-handler, and written records. P.A.T.H.S. provides a framework and analytical tool capturing the textual trajectory of information flow, and trace how dispatch decision making unfolds. We subsequently developed a template and codebook for other researchers to further study complex decision making in multi-actoral systems using a textual trajectory approach. Conclusion This methodological development work demonstrates the potential of P.A.T.H.S. to capture and clarify complex decision-making processes. P.A.T.H.S. offers a practical and theoretically grounded framework for future research, training, and policy that addresses risk points in communication between oral and written forms among teams of actors, to support optimal deployment of scarce resources.

Background: Urogenital schistosomiasis caused by Schistosoma haematobium remains a major public health concern in the Lake Chad region. This study aimed to estimate the cumulative prevalence of S. haematobium infection and to identify factors associated with test positivity within an intervention zone of the Ngouri health district, Lac Province, Chad. Methods: A cross-sectional analytical study was conducted using routinely collected data from the Dawa Mobile Health system between February 2024 and April 2025. A total of 4,504 individuals were included after data cleaning and biological consistency correction (22 haematuria cases recoded as positive). Bivariate analysis (Chi-square test) and multivariable logistic regression were performed. Results: The overall prevalence was 19.5% (95% CI: 18.4% - 20.7%). No statistically significant association was found for age (p=0.163), sex (p=0.939), marital status (p=0.858), occupation (p=0.704), or urine appearance in the binary model (p=0.116). Education level approached significance (p=0.078), with the highest prevalence observed at the primary level (23.0%). In multivariable analysis, only primary education was independently associated with positivity (adjusted OR=1.29, 95% CI: 1.06 - 1.57; p=0.011). Conclusion: Nearly one in five participants tested positive, confirming the high endemicity of the Ngouri district. The homogeneous distribution across demographic subgroups suggests widespread community exposure, supporting mass drug administration beyond school-aged children, strengthened WASH interventions, and consolidation of mobile health surveillance. Keywords: Schistosoma haematobium; urogenital schistosomiasis; prevalence; Lake Chad; Chad; mobile health; logistic regression.

Problem: Response personnel in the 2026 Bundibugyo virus disease (BVD) outbreak in eastern Democratic Republic of the Congo work amid protracted conflict and fragile health systems. Working conditions, security exposure and grievance handling were widely reported as sources of stress but had not been documented systematically. Approach: We conducted an anonymous, self-administered online survey in French among BVD response personnel in North Kivu, Ituri and South Kivu, covering staffing, EPI supply, remuneration, security incidents, grievances and psychosocial well-being. Of 55 submissions, 54 respondents consented and were analysed; denominators fall further, to 21, for grievance items because of within-survey drop-off. Findings are descriptive. Local setting: Respondents (94% men) worked for the Ministry of Health, international non-governmental organizations, the Red Cross and as unpaid volunteers, mostly with one to three months on the response. Relevant changes: 61% judged staffing insufficient; 67% had one or no rest day per week. 63% reported EPI stock-outs and 43% had performed a task judged riskier than necessary. 44% had pay delayed, partial or unpaid though promised, and 85% had advanced personal funds for work costs. 67% reported a security incident in three months; among the smaller subgroup reaching the grievance questions, 57% feared reprisal. Lessons learnt: Staffing, supply, payment and security shortfalls compound one another and are visible to communities; weak grievance channels and fear of reprisal let them persist unaddressed. Working conditions, security and grievance management should be monitored as core infection-prevention-and-control and occupational-risk indicators, not as a peripheral human-resources matter.

Influenza surveillance has typically been carried out using influenza-like illness (ILI) rates and proportions of laboratory tests positive for influenza as metrics to monitor, with sample sizes for the number of tests to carry out based on the precision of the resulting estimate of proportions positive. The transition out of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic period has encouraged the establishment of integrated surveillance of respiratory pathogens, in the context of multiple surveillance objectives, as set out by WHO in its revised integrated surveillance guidance and Mosaic Respiratory Surveillance Framework. These objectives include outbreak detection, situational awareness and intensity evaluation, among others. We illustrate how to design respiratory surveillance in primary care, by considering multiple surveillance objectives for different metrics of different types of respiratory pathogen circulation seasons in England, the USA and Hong Kong. We focus on a proxy of influenza activity as a metric to compare between these countries/regions. Taking advantage of England's integrated sentinel primary care surveillance system, we propose further metrics to monitor: a proxy of respiratory activity, novelly defined as the product of an acute respiratory infection (ARI) consultation rate and the proportion of tests positive for \emph{at least one pathogen}; pathogen-specific ARI-based activity proxies for more detailed monitoring of influenza and SARS-CoV-2; and integrated monitoring of proportions positive for all pathogens tested. We use a simulation approach to determine sample sizes by optimising either the probability of, or time to, detection of different events in monitored metrics, according to the different surveillance objectives. We find that sample sizes to maximise detection probabilities or minimise detection times vary by metric, objective, event and country/region. At a national level, the current sample sizes used are sufficient to detect most events in most weeks for both the USA and Hong Kong, but for England the numbers of swabs taken for ILI consultations may not be sufficient in all weeks, particularly at the start of the season when outbreak detection is important. However, broadening the criteria for swabbing to acute respiratory symptoms does allow for sufficient sample sizes.

Background Early recognition of presentations consistent with the deliberate release of a Category A bioterrorism agent is essential for rapid isolation, public health notification, and containment. The ability of UK clinicians-in-training to recognise these syndromes is unstudied. This pilot assessed final-year UK medical students' ability to recognise these syndromes. Methods A pilot cross-sectional online survey of final-year UK medical students used single-best-answer clinical vignettes depicting syndromes associated with Category A bioterrorism agents (BT vignettes) and clinically overlapping non-bioterrorism syndromes (NBT vignettes). Performance was summarised as the proportion of vignettes correctly identified, with primary analysis comparing within-participant BT and NBT performance. Results Twenty-five participants completed the survey. Participants performed worse on BT vignettes (M = 0.55) than on NBT vignettes (M = 0.81), with a within-participant difference of -0.26 (95% CI [-0.35, -0.18]; t(24) = -6.33, p < 0.001; Cohen's dz = -1.27). Botulism (96.0%) and Ebola virus disease (88.0%) were recognised by most participants, while anthrax (40.0%), pneumonic plague (28.0%), and smallpox (24.0%) were recognised by fewer than half. Conclusion This pilot provides the first UK evidence of a substantial diagnostic deficit in final-year medical students' recognition of Category A bioterrorism agent syndromes.

Background Severe COVID-19 is associated with immune dysregulation, microbiome disruption, and increased vulnerability to secondary infections. However, the longer-term risk of acute gastroenteritis (AGE) following COVID-19 remains poorly studied. We compared AGE incidence among community-managed and hospitalised COVID-19 patients with test-negative controls and hospitalised sepsis patients. Methods We conducted a retrospective cohort study using electronic healthcare data. Patients with a recorded SARS-CoV-2 PCR test between 01 March 2020 and 01 March 2022 were allocated to test-negative, community COVID-19, or hospitalised COVID-19 cohorts. A hospitalised sepsis cohort was assembled for an age-, sex- and time-matched analysis. AGE incidence rates were calculated for the two years before enrolment and during follow-up. Negative binomial regression and conditional Poisson models estimated incidence rate ratios (IRRs) adjusting for demographic and clinical covariates. Results Among 788 103 patients, AGE incidence in the pre-pandemic period was higher among individuals later hospitalised with COVID-19 or sepsis compared with community cases. During follow-up, AGE incidence was slightly lower in community COVID-19 cases than test-negative controls (adjusted IRR 0.88 [95% CI 0.85-0.92]), but substantially higher among hospitalised COVID-19 patients (IRR 5.80 [95% CI: 5.38-6.25]). Matched analyses confirmed increased AGE rates compared with community controls (IRR 4.63 [95% CI: 4.15-5.17]) and hospitalised sepsis controls (IRR 1.41 [95% CI 1.21-1.64]). Conclusions COVID-19 hospitalisation is associated with a marked increase in AGE risk after discharge, exceeding that associated with sepsis hospitalisation. By contrast, lower AGE rates in community COVID-19 cases likely reflect baseline differences rather than protective effects of infection. Overall, these findings demonstrate heightened post discharge vulnerability to gastrointestinal illness after COVID 19 hospitalisation and support the need for improved follow up and surveillance strategies.

Introduction: Veterinary Professionals are at risk of contracting zoonoses, including potential emerging infections. Personal Protective Equipment (PPE) could reduce infection transmission risk, but use of it in the profession is low. Understanding Veterinary Professionals experiences with PPE could help identify facilitators and barriers, therefore informing a future strategy to improve usage. Methods: Two focus group discussion with veterinary nurses and two with veterinarians took place at a tertiary small animal teaching hospital. With an interpretivist epistemology, transcriptions were inductively coded and analysed using thematic analysis. Results: Veterinary Professionals frequently reported underusing PPE, despite significant zoonotic risk. Friction in interdisciplinary relationships negatively impacted veterinary professionals experiences with PPE: differing views on risk, policy and PPE, contrasting perceptions of each other, and challenges with communication impacted PPE decision-making. Barriers included an absence of initial recognition of risk, a staff culture of prioritising patient health over risk to self, friction in response to others PPE use or lack thereof, an overly complex policy, and a lack of cultural and institutional reaction to occupationally contracted zoonoses. Facilitators included effective inter and intradisciplinary communication and previous personal experience with serious zoonoses. Conclusion: Veterinary Professionals experiences with PPE, are shaped by social and professional factors, such as interdisciplinary friction, perceptions of self and others, of risk and policy. Recommendations are for the findings to be used locally to embed PPE use into everyday practice. At a national level, the United Kingdom Health Security Agency (UKHSA) should use the findings to evaluate current policies regarding PPE and zoonoses in veterinary practice and produce a simplified national guidance, in collaboration with veterinary professionals with lived experience. Further research into General Practice Veterinary Professionals experiences and the interdisciplinary social dynamics is recommended.

Genetic ancestry composition has been used extensively as a predictor of clinical outcomes, multiomic biomarkers, and endophenotypes, but the models have most often used untransformed ancestry fractions as explanatory variables. Under a simplex constraint results from this type of model can be uninterpretable and misleading. We demonstrate the use of multiple transformed log ratio coordinates of ancestral proportions as predictors in a study of excreted myeloperoxidase (MPO), a neutrophilic enzyme used as a fecal biomarker to measure intestinal inflammation and enteropathy. We show how to convert these difficult-to-interpret effects into association results for shifts between two ancestries under an isoancestry constraint (holding an ancestry fraction constant) using post-estimation average marginal effects and explain their geometric interpretation. We found that increasing proportion of African ancestry was associated with increased levels of excreted MPO in low-income household infants in three Northeastern Brazil studies, with a smaller negative secondary association of European ancestry, all under a tri-continental ancestry composition (African, European, Amerindian). While the maximum log(MPO) outcome variance explained by differences in genetic ancestry was modest at a few percent, we show evidence that these differences in heritability were context-dependent on measurement of MPO under active enteric infection. The results were attenuated by socio-economic status, but a signal remained. Overall, we found that African genomes carry additional alleles that enable a more intensive neutrophil response to enteric infection relative to European and Amerindian genomes, illuminating a possible host genetic background role in modulating immune response to enteric diseases and pathogens.

Background: Alpha-gal syndrome (AGS) is an emerging disease, increasingly recognized as a public health concern in the United States. The primary cause of AGS in the United States is the bite of Amblyomma americanum ticks. White-tailed deer serve both as a preferred food source and as transport for A. americanum. In this work, we aim to quantify the effect of white-tailed deer abundance on number of AGS cases in United States counties. Methods: To mitigate concerns about confounding, we used the front-door formula, leveraging biological knowledge about the causal process. Due to lack of official data, our analysis relied on data made available by citizen science efforts. Results: We found that a higher number of reported white-tailed deer sightings in 2020 was associated with the county-level presence of A. americanum in 2024. In turn, county-level presence of A. americanum was associated with a higher number of self-reported AGS cases. We estimated that if white-tailed deer abundance had increased by 50%, 75%, or 100% in 2020, there would have been 89 (95%CI: 12, 252), 126 (12, 354) or 159 (6, 448) additional AGS self-reported cases in the US in 2025. Conclusions: The estimated associations are compatible with an effect of white-tailed deer abundance on AGS in the country. Due to measurement error, the low granularity of the available data, the ecological nature of the design, and the modelling choices, our effect estimates should be interpreted cautiously. Further studies are needed to quantify the population-level effect of white-tailed deer on AGS.

Background: Optimising sampling frames for genomic surveillance of E. coli and Klebsiella may support interventions to mitigate bloodstream infections (BSIs), but approaches to estimating sample size and how these relate to bacterial population diversity at multiple genetic levels (strain/plasmid/antimicrobial resistance genes [ARGs]) are lacking. Methods: Using systematically collected, regionally-stratified, whole genome sequencing data from a 6-month genomic survey of English E. coli/Klebsiella BSI isolates (n=1,939; NEKSUS), we used Bayesian approaches to estimate the sample sizes required to capture genetic diversity at strain- (MLST, fastBAPS clusters), plasmid- and ARG-levels, using two diversity measures: 'coverage' (i.e. proportion of the bacterial population/plasmids/ARGs represented by features observed in the sample), and the number/proportion of unique features observed at varying surveillance sample sizes. Our method is available as a user-friendly R Shiny application, EpiSENTRY (https://01a0c3dd-90d2-e597-58a5-51391a468387.share.connect.posit.cloud/). Findings: Randomly sampling 1,400 E. coli/Klebsiella BSI isolates each achieved [&ge;]80% coverage of bacterial lineages and ARGs (i.e. capture features constituting [&ge;]80% of the population) at 95% certainty, but captured lower proportions of unique features (28% MLSTs, 59% fastBAPS clusters, 19% plasmid subcommunities, and 57% ARGs for E. coli; 49%, 87%, 50% and 83% for Klebsiella, respectively). Sample sizes required for 80% coverage of E. coli plasmid populations were higher than MLSTs (1,928 [95% CrI: 1,837-2,025] vs 1,351 [1,230-1,472]), while for Klebsiella, estimates were lower for plasmids (1,115 [1,052-1,181] vs 1,422 [1,331-1,515] for MLSTs). Lower sample sizes adequately captured 80% coverage of fastBAPS clusters and ARGs (649 [575-727] and 27 [23-30], respectively, for E. coli; 113 [84-145] and 83 [72-96] for Klebsiella), due to feature-specific frequency distributions. Klebsiella BSIs were more diverse than E. coli, with sampling 10.9% vs 3.2% of total annual BSIs in England needed to reach 80% MLST coverage. Plasmid populations and Klebsiella MLSTs were region-specific, emphasising the need for regionally-representative sampling. Interpretation: Bayesian analysis facilitates estimates of context-specific genomic surveillance sample sizes. Cost-effective targets for coverage require further optimisation.

Determining the appropriate sample size for desired statistical power is crucial for obtaining reliable research outcomes. While methods exist for multiple types of studies, the method for evaluating power of estimating spillover effects in sociometric network-based studies with non randomized interventions remain inadequately explored. We conducted a simulation study to assess how the design parameters (i.e., number of components, number of nodes, node degree, transitivity, and effect size) affects the statistical power for estimating spillover effects in non randomized, network-based studies. Both simulated networks and a real-world network from Transmission Reduction Intervention Project (TRIP) were used in this study. Our simulation results suggests that: (1) power increases with more nodes or a larger effect size, but not necessarily with more components when the number of nodes is fixed; (2) A higher node degree or greater transitivity results in reduced power; (3) Highly unbalanced networks (e.g., most of the nodes are in one component) can drastically reduce power. Furthermore, the power calculated using a closed-form expression developed in this work also shows that power remained the same or even decreases slightly with more components when the number of nodes are fixed, aligning with the simulation findings. All the results were specific to the inverse probability weighting estimator we employed in this study and assumptions it required. An alternative estimator or interference assumption may lead to different results.