Preventive Veterinary Medicine

Free-roaming dog populations pose challenges to public health and animal welfare, and their management and protection is legally mandated in many countries to control zoonotic diseases such as rabies, reduce bites, mitigate fear of aggressive dogs, and preserve animal welfare. Although international organisations recommend holistic dog population management with surgical sterilisation as a key component, stakeholders report a lack of evidence-based guidance on effective and cost-efficient strategies. To address this, we developed a deterministic model of a closed free-roaming dog (FRD) population, including dogs dependent on and independent of humans, parameterised using data from southern India. We evaluated a wide range of sterilisation strategies varying duration, interval, frequency, intensity, targeting different population subsets, and fixed or responsive approaches. Strategies were assessed based on their effects on independent FRD population size and implementation costs. Female sterilisation coverage (FSC) strongly determined population reduction and cost-effectiveness. Repeated, short sterilisation periods with appropriately timed intervals and high early intensity best balanced population reduction and cost-efficiency. Excluding dependent dogs (cared for by humans) severely constrained population reduction, while male sterilisation enhanced impact at little extra cost, assuming direct effects on pregnancy rates. Costs and population impacts can be visualised on an interactive web application (https://field.shinyapps.io/DogPopSimApp), allowing practitioners and policymakers to explore sterilisation strategy outcomes. Overall, cost-effective population reduction required sustained commitment and coverage-driven strategies that balance welfare, population reduction and the operational needs of dog-mediated rabies control.

Evidence suggests that increased local temperatures are associated with higher prevalence of antimicrobial resistance (AMR) in environmental bacteria. This study investigates the association between local climate and the proportion of antimicrobial-resistant Escherichia coli isolated from 2,766 farm environment samples from 53 English dairy farms. To do this, a non-linear Bayesian model that specifically accounts for decreased test sensitivity at low E. coli abundance was developed and used to estimate the proportion of isolates resistant to four antimicrobials (amoxicillin, cephalexin, streptomycin and tetracycline) from colony count data. Mean 7-day temperature and relative humidity at the farm location was modelled using a generalised additive model formulation. A higher proportion of E. coli isolates were resistant to cephalexin and streptomycin in samples collected from adult cow collecting yards, than heifer housing sheds. In contrast, a greater proportion of E. coli isolates from heifer housing sheds were resistant to amoxicillin and tetracycline. Evidence that local temperature is associated with an increase in the proportion of E. coli isolates resistant to streptomycin (20{degrees}C increase associated with a 5.0-fold increase; 95% CI: 1.03-33.0) and tetracycline (2.6-fold increase; 90% CI: 1.1-5.2) was observed. Additionally, relative humidity was associated with an increase in the proportion of isolates resistant to amoxicillin streptomycin and tetracycline. The influence of weather on the proportion of antimicrobial-resistant E. coli varied between samples collected from adult animals in collecting yards and heifers in housing sheds. These findings highlight the importance of considering weather conditions, sample characterises and seasonality when designing on-farm AMR surveillance systems. ImportanceUnderstanding how environmental conditions are associated with variability in AMR prevalence is critical for developing robust livestock AMR surveillance and anticipating the potential effects of climate change. The non-linear Bayesian modelling approach developed here adjusts for E. coli abundance associated variability in test sensitivity, enabling the influence of risk factors associated with the proportion of antimicrobial-resistant E. coli within samples to be more accurately estimated. Applying this approach to 2,766 faecal samples from 53 dairy farms in Southwest England indicated that the proportion of antimicrobial-resistant E. coli generally increased under warmer and wetter conditions. These findings suggest that environmental conditions can influence the prevalence of AMR E. coli in dairy farm environments and demonstrate the importance of accounting for weather related variability in livestock AMR surveillance. Adjusting for these associations in livestock AMR surveillance could improve the accuracy of modelling AMR trends and strengthen the assessment of climate-associated AMR risks.

The relationship between Campylobacter levels in broiler caeca and on carcass skin is central to quantitative microbial risk assessment along the poultry production chain, underpinning modelling of intervention impacts, including EFSA assessments of the public health impact of control measures. However, this relationship is typically inferred from monitoring data generated under sampling designs that do not preserve pairing between specimens and may involve pooling. In this study, we used a simulation framework to evaluate whether commonly used sampling strategies allow reliable recovery of the caecal-skin relationship. A simulated broiler population was generated, assigning caecal and skin loads to individual birds based on a specified linear relationship. Sampling was conducted under paired and unpaired designs, with and without pooling, reflecting approaches used in surveillance programmes and in policy-oriented models. Regression models were fitted to sampled data across 1,000 simulations for a range of assumed slopes. Under paired sampling, estimated slopes closely matched the true relationship across most scenarios. In contrast, unpaired sampling consistently failed to recover the association, with estimated slopes centred around zero regardless of the true slope. These findings were robust to variation in within-flock prevalence, residual error, and intercept. The results show that sampling design fundamentally affects identifiability of relationships between stages of the production chain. This has implications for interpretation of parameters derived from monitoring data and used in quantitative Campylobacter risk assessments informing policy. Parameters derived from unpaired and pooled monitoring data should therefore be interpreted with caution when used to support risk assessment and decision-making. Campylobacter; broiler chickens; sampling strategy; unpaired sampling; carcass contamination; quantitative microbial risk assessment; simulation.

ContextThe movement of vertebrate hosts across urbanized landscapes can play a key role in the transmission of direct-contact diseases. Understanding how wildlife hosts move in urban landscapes, and how transmission is affected by their landscape-constrained and disease-altered movements, is imperative for better predicting the spread of disease. ObjectiveWe assess how the movement of red foxes (Vulpes vulpes) according to landcover type, and their infection status, affect the spread of mange (caused by Sarcoptes scabiei) in an urbanized landscape. MethodsWe developed a mange transmission model (MTM) using an agent-based model to compare two movement behaviours of foxes in Scarborough (Ontario, Canada): random and landcover-based. We further assessed the effects of movement on disease transmission by considering the foxs infection status and comparing a range of movement probability scenarios. We quantified the number of effective contact events and the effective reproduction number (Re) according to each scenario. ResultsWe found that both landcover-dependent movement and infection status influenced the spread of mange within fox populations. The number of effective contact events and effective reproduction number Re was greatest when landscape heterogeneity was included in the model and foxes moved through paths of least resistance to movement, and when susceptible and infected foxes had an equal probability of leaving a fragmented habitat patch. ConclusionsOur findings suggest that mange spread may be accelerated along movement corridors in fragmented, heterogenous landscapes. As urban areas expand and remnant habitat within these is further lost and animals are relegated to fewer movement pathways, disease transmission may increase.

BackgroundBeef feedlots are increasing concerns that antibiotic use in beef cattle selects for antibiotic resistance, but limitations of primary studies and previous syntheses make it difficult to confirm a consistent effect. We conducted a rigorous systematic review and meta-analysis to summarise: 1) the effect during and after antibiotic administration; 2) its moderation by time since administration started/ended. MethodsEligible studies longitudinally compared beef cattle administered antibiotics to those that were not, measuring resistance determinants in faeces and/or environments. Information sources included Web of Science, CAB Abstracts, and Medline (last searches: 05/03/25). Risk of bias was assessed using RoB 2 and ROBINS-I. Meta-analysis was conducted where feasible, using individual participant data where necessary. ResultsThe 33 included studies were mostly small trials of North American feedlot cattle, all with high risks of bias. Meta-analysis of 11 studies of tylosin, ceftiofur, and chlortetracycline indicated positive effects on absolute abundance of resistance both during (SMDH = 0.4; 95% CI = 0.11 to 0.69, p = <0.01) and after (SMDH = 0.52; 95% CI = 0.33 to 0.71, p = <0.01) antibiotic administration. Log-transformed time was positively associated with effect size during (Slope = 0.63; 95% CI = 0.1 to 1.16, p = 0.02), and negatively associated after (Slope = -0.65; 95% CI = -1.24 to -0.06, p = 0.03) DiscussionAvailable evidence indicates time-dependent selection for antibiotic resistance in beef cattle, warranting further regulation to limit human health risks. Simultaneously, uncertainty about precise effect sizes warrants further research. FundingBBSRC Registrationhttps://doi.org/10.17605/OSF.IO/RXQHT

Global efforts to prevent and mitigate the impacts of high pathogenicity avian influenza (HPAI) H5 on domestic animals, humans, and wildlife rely on timely and transparent information that is both accurate and interpretable across countries and sectors. International epidemiological and genomic databases, such as the World Animal Health Information System (WAHIS), the Global Animal Disease Information System (EMPRES-i+), the Global Initiative on Sharing All Influenza Data (GISAID), and the National Center for Technological Bioinformation Virus Portal (NCBI) provide essential information for surveillance, research, and decision-making. To evaluate how well these resources capture recent wildlife impacts, we consolidated information from these databases and complementary public sources including government reports, scientific literature, and news articles, on wildlife mortality associated with HPAI H5 in the Americas from November 2021 to July 2024. The consolidated dataset comprised 615,883 wild birds (287 spp.) and 63,409 wild mammals (39 spp.). In comparison, WAHIS represented 16,902 wild birds (261 spp.) and 6,323 wild mammals (31 spp.) while EMPRES-i+ captured a substantially smaller portion of affected host diversity for both wild birds (105 spp.) and wild mammals (27 spp.). Genomic databases (GISAID and NCBI) represented 7,027 whole genome equivalents of H5 viruses from wild birds (175 spp.) and 371 from wild mammals (26 spp.). These discrepancies indicate that international databases, while essential, provide an incomplete picture of HPAI impacts on wildlife, with significant geographic and taxonomic asymmetries attributable to differences in surveillance capacity, reporting practices, sequencing effort, and data-sharing pathways. Studies and management strategies relying on these resources without complementary validation may therefore mistake data gaps for real-world epidemiological patterns. Strengthening data reporting standards, improving validation procedures, and integrating international databases with national reports, scientific publications, and other sources will enhance the reliability of epidemiological analyses and support more effective One Health surveillance, risk assessment, and conservation action. Author summaryHigh pathogenicity avian influenza (HPAI) H5 viruses, often called bird flu viruses, can cause severe disease in birds and mammals, including humans. Because of their relevance for human health, livestock production, and wildlife conservation, international databases were established to share information on when and where these viruses are detected, which species are affected, and what virus strains are found. These databases are essential tools for governments, scientists, and conservation practitioners working to track outbreaks, understand how these viruses spread and evolve, and guide surveillance and response. In this study, we compiled and compared information on recent HPAI H5 events in wildlife in the Americas available in international databases with information from other public sources, including reports from governments, scientific literature, and news articles. We found important discrepancies in how countries and species affected were represented across sources. As a result, international databases might not fully capture the actual distribution or conservation impact of HPAI H5 on wildlife. Our findings also show why decision-makers and scientists should interpret database-derived patterns carefully. We provide recommendations to improve international databases to address these gaps and better inform One Health risk assessment and wildlife conservation actions.

IntroductionWith rising tick-borne disease (TBD) cases and the geographical expansion of tick populations, the need for effective surveillance and public education regarding local risk is crucial. This study assessed the effectiveness of tick-check stations as a tool for tick surveillance, and their impact on community knowledge, attitude, and practices (KAP) related to ticks and TBD. MethodsTo assess the effectiveness of tick-check stations for surveillance, we evaluated station engagement and compared tick density estimates, species composition, and life-stage distributions with those obtained through concurrent active surveillance. In addition, we compared submission numbers and tick species and life stages to those collected through a mail-in submission system conducted by the Colorado Department of Public Health and Environment (CDPHE). To quantify feasibility, we estimated effort per tick and compared effort across simulated sampling scenarios. Finally, in-person surveys were conducted at trailheads to assess baseline tick KAP and to evaluate differences between sites with and without tick-check stations ResultsEngagement with tick-check stations was sustained throughout the study. Temporal tick densities estimated from tick-check station submissions were correlated with density estimates from active surveillance (R = 0.534), and species composition and life-stage distributions did not significantly differ between methods. Tick-check stations required less effort per tick than active surveillance when sampling sites were nearby or tick densities were low, whereas sites that were farther away or had higher tick densities required less effort per tick under a hybrid surveillance approach. When asked to list tick-borne pathogens in Colorado, 47% of survey participants who had read tick-check station signage identified Rocky Mountain spotted fever compared with 20% of participants in the control group (p = 0.007; odds ratio). Notably, a low proportion of survey participants (24%) reported performing tick-checks to prevent tick bites. ConclusionTick-check stations can provide tick density estimates comparable to active surveillance while requiring less effort in many scenarios, particularly in low-density settings. Our findings also highlight opportunities for targeted outreach to address gaps in TBD knowledge. As both a surveillance and educational tool, tick-check stations offer a sustainable approach for expanding tick monitoring in resource-limited settings.

Pastureland provides critical resources for livestock production. Despite the growing knowledge on pest regulating services provided by insectivorous bats, their role in pastureland systems remains understudied. We analysed the diets of four Irish bat species, Plecotus auritus, Pipistrellus pygmaeus, Myotis nattereri, and Rhinolophus hipposideros, to identify and categorise agricultural pest species based on metabarcoding studies in the pastureland rich agro-ecosystems of Ireland. Pest richness varied among bat species, with P. auritus consuming 22 known pest species, 13 in P. pygmaeus, 9 in R. hipposideros, and 7 in M. nattereri. Pest occurrence frequency varied in overall diets: R. hipposideros exhibited the highest (83%), followed by P. auritus (71%), M. nattereri (59%), and P. pygmaeus (32%). Importantly, 26 of the 35 pest species (74%) were consumed by only one bat species. Key pastureland pests, Tipula oleracea and T. paludosa, were detected across all bat species, with frequency of occurrence reaching up to 100% at several roosts. Livestock-associated disease vectors (Culicoides scoticus and C. pulicaris) were also consumed. Differences in diet among species reflect complementary foraging strategies, activity patterns, and habitat use, highlighting the collective role of bat communities in pest regulation. These findings provide additional evidence that insectivorous bats contribute to ecosystem services in pasturelands, potentially supporting reductions in pesticide use and enhancing sustainable pastureland management. By demonstrating the functional importance of bats beyond biodiversity conservation, this study underscores their value in nature-based solutions for sustainable agriculture and informs conservation and policy strategies in Ireland. Highlights- Clear habitat bias in studies of ecosystem services by insectivorous bats. - Bat species forage on diverse agricultural pests in pastureland-dominated areas. - Bat foraging behaviour influences consumption of specific pest species. - Bats consume major pasture pests, Tipula oleracea and T. paludosa. - Different bat species target different pests, enhancing overall control.

Naturally occurring variation in the bovine mannose receptor C-type 1 gene (MRC1) may shape macrophage responses to Mycobacterium (M.) bovis, a key driver of bovine tuberculosis (bTB). We identified four coding region SNPs in MRC1 across Bos taurus (Holstein Friesian, Brown Swiss) and Bos indicus (Boran, Sahiwal) cattle breeds, including a non-synonymous variant, rs380943118 (c.2963G>A; Ser988Asn) in C-type lectin-like domain (CTLD) 6, most prevalent in Sahiwal cattle. Structural modelling suggested that the S988N substitution, which is spatially separated from the monosaccharide binding site of CTLD4, might indirectly affect glycan binding, perhaps through a conformational change in the receptor. Monocyte-derived macrophages upregulated MR expression during differentiation, with heterozygous (G/A) animals showing higher MR expression and increased uptake of GFP-M. bovis BCG, although differences were not statistically significant. Anti-CD206 blockade did not inhibit BCG internalization, either indicating that this specific antibody did not bind to a CTLD involved in ligand binding or that MR is not the sole entry receptor. These results highlight naturally occurring MRC1 polymorphisms that may influence MR structure and macrophage function, providing a foundation for future studies to assess their role in bTB susceptibility.

Giraffes, unlike other large mammals, typically pose minimal risk to humans, their animals, and crops, so they are traditionally not involved in human-wildlife conflict. Tree crops, however, are expanding across Africa, resulting in crop raiding by giraffes and retaliatory snaring, poaching, and killing of giraffes in response. The dynamics of giraffe crop raiding, however, are poorly understood, making effective interventions difficult to implement. To better understand key factors for humans and giraffes that mediate crop raiding, we used a multi-method approach to estimate giraffe abundance and activity, understand farmers perceptions and decisions, and test countermeasures around Garissa Giraffe Sanctuary in eastern Kenya. We hypothesized that 1) giraffe farm invasion would occur in dry seasons, 2) farms growing mangoes would be more likely to be invaded, 3) reducing invasion with only physical barriers would be less effective than adding behavior-based countermeasures, 4) perceptions would match giraffe activity and 5) countermeasure adoption would be driven by cost. We found that invasion and crop raiding primarily occur during the dry season and are associated with mangoes. Farmers are using many countermeasures. Effective countermeasures target giraffe behavior combined with physical barriers. Countermeasures are most effective when negative associations with humans are reinforced. Floodlights and speakers that play predator calls both reduce invasion, but only if used consistently. Overall, farmers perceptions matched giraffe dynamics. Availability was the most important factor in farmers willingness to try a countermeasure. Our results suggest that conflict can be reduced and there is interest from farmers in doing so, but use of countermeasures should be consistently applied and supported by making necessary equipment and instructions available.

Reliable early warning of infectious disease outbreaks remains a major challenge for surveillance systems, particularly for vector-borne pathogens whose transmission depends on interactions among hosts, vectors, and climate-sensitive environmental conditions. Data-driven forecasting offers a promising approach for predicting outbreak risk using surveillance and environmental data. This study develops a logit-weighted ensemble (LWE), a machine-learning framework that predicts outbreak occurrence 1-6 months ahead at the administrative unit-month scale using routinely available outbreak notifications and gridded climate data. Bluetongue virus (BTV), an arbovirus of ruminants transmitted by Culicoides biting midges, provides a well-characterised system in which transmission is strongly shaped by climate, making it a useful system for applying and testing this approach. The framework is evaluated using surveillance data collected between 2005 and 2024 from France, Greece, and Italy, selected for their long-running and high-quality outbreak surveillance records. Across all three countries, the LWE achieved the strongest and most stable predictive performance under a recall-focused evaluation that prioritises correctly identifying outbreak months. It outperformed or matched 14 benchmark models, with differences becoming more pronounced at longer lead times (month +3 onward), when predictions are more uncertain and outbreaks are relatively rare. Predictability varied across countries, with the highest performance in Greece, strong performance in France, and lower, more variable performance in Italy, reflecting differences in how consistently outbreaks occur and spread across regions. Overall, the results demonstrate that horizon-aware, climate-informed forecasting can reliably identify months and locations at elevated risk of outbreak occurrence up to six months in advance, supporting surveillance planning and preparedness across heterogeneous European settings. The ensemble framework provides a robust and portable strategy for outbreak prediction using routinely collected surveillance and environmental data. Author SummaryPredicting infectious disease outbreaks before they occur remains a major challenge, particularly for diseases influenced by environmental conditions. In this study, we focus on bluetongue, a viral disease of livestock transmitted by biting midges, where transmission is strongly affected by climate and seasonal patterns. We develop a method that uses routinely collected outbreak reports and climate data to estimate where and when outbreaks are more likely to occur, up to six months in advance. We apply this approach across three European countries with a history of bluetongue outbreaks. We find that combining climate information with recent outbreak patterns can provide useful early signals of increased risk. Predictions are most accurate at shorter timeframes, but longer-range forecasts can still support planning and preparedness. Because our approach uses widely available data, it could be applied in other regions or to similar environmentally driven diseases. However, it does not include factors such as vaccination, animal movement, or detailed information on vector populations, which may also influence how outbreaks develop. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/726753v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@45e41borg.highwire.dtl.DTLVardef@82c787org.highwire.dtl.DTLVardef@1f97888org.highwire.dtl.DTLVardef@1586747_HPS_FORMAT_FIGEXP M_FIG C_FIG

Objectives: Research on mental health in agriculture has increased in recent years; however, it remains largely focused on farmers themselves and is predominantly male-oriented. The mental health of farm wives and partners, many of whom play integral roles in farm operations, business management, and family life, remains difficult to characterise. This study therefore aims to explore the prevalence and causes of mental health challenges among farm wives and partners, and to investigate their use of, and barriers to, mental health support services. Methods: Quantitative data was collected using over 450 structured questionnaire responses that assessed mental health prevalence, contributing stressors and support service utilisation. Results: Findings indicate that there is a high prevalence of mental ill health amongst farm wives, seemingly due to industry stressors and support role overwhelm. Interpersonal relationships played a significant role in the types of mental distress experienced and highlighted the toll that farm life can take on farm wives' social and emotional connections. Despite a range of formal and informal support services being available, and effective when used, significant barriers to accessing these services were identified, including both practical difficulties and self-stigmatisation due to cultural beliefs. Conclusions: Farm wives and partners experience substantial mental health burdens linked to their diverse and often underrecognized roles within agricultural systems. In future, targeted interventions are needed to reduce stigma, improve service accessibility, and recognize women's contributions within farm enterprises. Further research and dedicated investment are also essential to better understand and help improve the mental health of this overlooked population within agricultural industries.

Traditional agricultural safety assessments often rely on raw incident counts that emphasize exposure but underrepresent outcome severity. This study presents a multi-criteria impact framework to distinguish frequency-driven activity patterns from severity-driven risk across the U.S. Midwest. Agricultural incident records from 2012 to 2023 across seven states were analyzed using descriptive statistics, county-level mapping, and quartic kernel density estimation. Comparative impact indices were constructed using Analytic Hierarchy Process (AHP) and Geometric-Fuzzy AHP weighting schemes to integrate incident frequency, outcome severity, and post-incident survivability. Results indicate that while overall incident frequency is strongly concentrated in northwestern Iowa, reflecting intensive agricultural activity, fatal outcomes exhibit a broader spatial footprint extending across central and northern Iowa and into central-southern Minnesota. Severity-weighted mapping further consolidates northwestern Iowa and the Minnesota-Iowa corridor as dominant high-impact zones. At the regional scale, Geometric-Fuzzy AHP produced consistently lower mean scores and reduced dispersion than AHP, yielding smoother spatial gradients while preserving the primary hotspot structure. These findings demonstrate that frequency-based mapping alone fails to capture the multi-dimensional nature of agricultural risk. By explicitly linking incident locations with survival infrastructure, this research provides an evidence-based framework for targeting safety interventions and improving rural emergency medical service planning.

Wildlife feeding during the wild turkey (Meleagris gallopavo) hunting season is legal in many states within the United States, but hunting turkeys with the aid of bait is unlawful in most states. The most common policy to prevent wildlife feeding from acting as bait is to restrict hunting within a defined radius. However, the effect of wildlife feeders on turkey harvest risk and the effectiveness of distance restrictions on mitigating that influence have not been investigated. During 2024-2025, we used GPS transmitters to track 30 adult male turkeys during the spring hunting season on private land with active feeders in Florida, USA, where hunting turkeys within a 91 m radius of a feeder was unlawful. We used Cox proportional hazard models to link risk of hunter harvest with unique feeders visited daily, number of feeders within a home range, and average morning distance and roosting distance to feeders at multiple temporal scales. Hunters harvested 53% of the tagged turkeys. Risk of hunter harvest increased with the number of unique feeders visited the previous day and after the first three days of hunting season with the number of active feeders within a home range. As distance from the most recent roost site and average morning distance to a feeder decreased, risk of hunter harvest increased. We estimated that risk of hunter harvest would be reduced by over 50% if distance restrictions were increased from 100 m to 200 m, by nearly 75% with an increase from 100 m to 300 m, and by nearly 90% with an increase from 100 m to 500 m. To completely eliminate the influence of wildlife feeders on risk of hunter harvest would require a restriction distance well beyond a 500m radius, which is impractical given that this radius would result in an area twice the average private landowner property size in the region. Thus, if wildlife feeding during the turkey hunting season is to be allowed, it will act as bait, in which case, the acceptable level of its influence as bait can be achieved with the appropriate hunting radius restriction.

Johnes disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is an important livestock disease in Japan. We typed 48 field isolates from cattle feces collected on 13 farms in Kushiro Subprefecture during 2024-2025 and 62 MAP-positive cattle fecal samples collected in the same region during 2025-2026 using a real-time PCR assay targeting the Type S-specific arylsulfatase gene. No Type S strains were detected among the cultured isolates or fecal samples examined in this study, suggesting that the cattle cases analyzed were more consistent with Type C than with Type S strains. Broader surveys are needed to define MAP strain diversity in Japan and improve control strategies.

The emergence and spread of vector-borne pathogens, such as the mosquito-borne parasite Dirofilaria immitis, the causative agent of canine heartworm disease pose increasing challenges for public and veterinary health. These parasites have expanded from Southern Europe into Central and Eastern Europe, where locally acquired transmission is increasingly documented in pets and wild animals, and is now considered an emerging infectious disease. Understanding the dynamics of its emergence and public awareness is essential for effective surveillance and intervention. Here, we evaluated whether human online search behavior, as measured by Google Trends (GT), reflects patterns of dirofilariosis emergence in Hungary. We analyzed GT data for heartworm-related terms from 2012 to 2025, exploring geographical and temporal trends. Our analyses revealed a sustained increase in search interest over time beginning in 2012, with pronounced rise after 2015. Spatially, the highest relative search volumes were concentrated in southeastern Hungary. We found a strong positive association between regional search activity and mosquito infection rate. These findings demonstrate that GT-derived data accurately mirror both the temporal increase and geographic distribution of canine dirofilariosis, supporting the use of digital epidemiology as a complementary surveillance tool to inform public health and veterinary strategies for emerging vector-borne diseases.

Infections at the animal-human or wildlife-livestock interfaces have severe health and socio-economic consequences. Combined with empirical data, mathematical models can contribute to a better understanding of the reservoirs of these infections, which is a priority for mitigating their impact by using appropriate management interventions. Taking brucellosis in the Bargy massif (French Alps) as an example of a zoonosis at the wildlife-livestock interface, we developed and calibrated a multi-host model integrating data on direct and environment-mediated cross-species contacts from field observations. Estimates of the basic reproduction number (R0) allowed to identify the population of Alpine ibex (Capra ibex) and its environment as an essential host in the reservoir, driving both pathogen maintenance (within-species R0[&ge;]1: 1.66, 95% credible interval: 1.42-2.03) and its transmission to livestock (between-species R0>0: 0.035, 0.01-0.05). Our approach can be adapted to other multi-host pathogens, which will contribute to improve the understanding and management of these complex systems.

Occupational activities play a critical role in shaping patterns of infectious disease transmission, as work-related contact, exposure duration, and environmental conditions vary substantially across jobs. Evidence from infectious disease outbreaks, including tuberculosis and other respiratory infections, shows that occupations involving frequent close contact and crowded settings face elevated exposure risk, highlighting the need for systematic occupational risk assessment to inform public health action. This study adopts a data-driven approach to characterize occupational risk for infectious disease transmission in the Philippine workforce. Using job-specific indicators such as encounter frequency, work shift duration, and crowd density, unsupervised clustering methods were applied to group occupations into distinct risk profiles. Unlike predefined sector-based classifications, this approach identifies latent groupings that capture shared exposure characteristics and socioeconomic context. The resulting clusters reveal a clear gradient in exposure risk across occupations, with high-risk roles often concentrated among jobs with lower to moderate income levels, while lower-risk occupations tend to be associated with higher income and greater structural capacity for risk mitigation. These findings provide a framework for occupational risk stratification that is directly relevant for public health planning. Overall, this study demonstrates the value of integrating occupational and socioeconomic data to support targeted workplace interventions, risk-informed surveillance, and more equitable allocation of public health resources for infectious disease prevention and control in the Philippine context.

Background Rabies is a zoonotic neglected public health problem associated with animal bites, especially domestic carnivores claiming 59,000 deaths annually predominantly in developing countries of Africa and Asia. There is a high risk of exposure among rural communities endemic with animal rabies where adoption of prevention strategies is minimal. This study determined the prevalence of suspected rabies exposure, associated factors, and delayed post-exposure care-seeking among animal-bite human cases in Busia district, Uganda. Methods: This was a cross-sectional study that involved 332 consecutively sampled animal bite human cases that occurred within the period 2023 to 2024. Data for the bite cases from records were collected using a data abstraction tool. In addition, interviewer-administered semi-structured questionnaires were used to collect data on sociodemographic, animal-related and environmental characteristics. Approximate bite locations were collected using Global Positioning System (GPS) coordinates via Kobo collect. Analysis was carried out in STATA 17 using mixed effects modified Poisson regression for factors associated with suspected rabies exposure. Results: The median age of the bite cases was 18 (IQR: 9-36) with the male gender predominantly affected. The prevalence of suspected rabies exposure was 53.6% (95% Confidence interval - CI: 46.8-60.3). Factors associated were urban versus (vs) rural residence (adjusted prevalence ratio-aPR: 1.04, 95%CI: 1.00-1.08), being bitten by a stray animal (aPR: 1.28, 95% CI: 1.22-1.35) and wild animal (aPR: 1.22, 95% CI: 1.14-1.30) vs domestic animal, vaccination status of the biting animal i.e. vaccinated vs unvaccinated (aPR: 0.76, 95% CI: 0.69-0.85), provoked vs unprovoked bites (aPR: 0.82, 95% CI: 0.79-0.86), and distance to nearest river ([&ge;]5km) vs <5km (aPR: 0.93, 95% CI: 0.87-0.99). The prevalence of delayed post-exposure seeking was 23.0% (95% CI: 16.5-31.1) among the suspected rabies exposures. Conclusion: The study reveals a high prevalence of suspected rabies exposure. Factors associated are multidimensional i.e. are of human, animal and environmental origin. The one health paradigm should be emphasized during routine surveillance of rabies-related cases. The study observed that 1 in 5 bite cases delayed to seek care post bite exposure. We recommend collaborations between sectors, routine vaccination and awareness campaigns, and monitoring of wild carnivore populations and environmental dynamics in rabies-related surveillance.

We report an unusual mortality event affecting the isolated Egyptian fruit bat (Rousettus aegyptiacus) population in northern Cyprus, combining clinical admissions, microbiological findings, and roost surveys to assess magnitude and potential drivers. From January to June 2025, the Cyprus Wildlife Research Institute (CWRI) Wildlife Hospital received an unprecedented surge in admissions with 12 individuals (versus sporadic admissions in prior years), with high acute mortality: most bats arrived moribund and died within 12-24 hours. Clinical records noted localized purulent lesions and abscesses in a substantial fraction of cases. Bacteriological culture and PCR assays recovered Staphylococcus aureus from abscesses and skin swab samples in multiple individuals; isolates exhibited susceptibility to tested antibiotics. Necropsy sampling confirmed viable S. aureus in several specimens despite prolonged storage. Concurrent targeted surveys of ten known roosts in Feb-Mar 2026 documented marked declines at multiple historical sites, including reductions from hundreds to single-digit counts at formerly large colonies and the complete absence of bats at multiple roosts. No clear evidence of recent human disturbance, extreme weather anomalies, or reduced food availability was found; shotgun cartridges from historical hunting were present, but no direct anthropogenic cause was apparent. While S. aureus infections--seasonally concentrated in winter--are consistent with observed lesions and may have contributed to morbidity and mortality, causality for the population-level declines remains unknown, and other factors (toxins, unassessed pathogens, multi-factor stressors) cannot be excluded. Given the genetic isolation and conservation significance of the Cyprus Egyptian fruit bat population, these findings are concerning. We recommend urgent, coordinated longitudinal population monitoring, expanded pathogen surveillance including whole-genome sequencing of S. aureus isolates, toxicological screening, and development of a species recovery plan incorporating emergency response, habitat protection, and public outreach.