Avian influenza virus (AIV) is a significant global concern, causing widespread mortality in wild birds, domestic poultry and, more recently, wild and domestic mammals. This study presents a retrospective analysis of AIV detections in the Republic of Ireland. Data were sourced from official surveillance databases, peer-reviewed literature and grey literature sources. Spatiotemporal, host-specific and subtype patterns were assessed using descriptive statistics, chi-square tests, linear regression and kernel density estimations. A total of 2,888 confirmed AIV detections were recorded across 25 of Ireland's 26 counties. Wild birds accounted for 98.7% of detections, with domestic birds comprising 1.3%, and two detections in foxes. H5N1 highly pathogenic avian influenza (HPAI) was the most prevalent subtype (96.7%), followed by H5N8 HPAI and H6N1. Spatial clustering was observed in urban areas, particularly Dublin. The highest seasonal peak occurred during summer, contrasting with traditional winter-associated patterns. Several detections occurred in migratory species outside of typical residency periods, suggesting potential climate-related shifts in migration behaviour. This study represents the first review of AIV surveillance data in Ireland to date. The findings highlight evolving patterns in virus distribution, seasonality and host dynamics, with implications for national surveillance strategies. Continued cross-species monitoring and integration of ecological data are essential to inform effective management strategies.

Syndromic surveillance, which monitors clinical or production data as potential indicators of disease, can complement existing diagnostic testing strategies for a more comprehensive surveillance system. Consistently recorded mortality data with established identification and traceability routes across cattle sectors could be useful indicators to monitor in a syndromic surveillance system. Ireland is progressing toward the eradication of bovine viral diarrhoea (BVD) virus following a programme initiated in 2013 to identify and remove calves that test positive for BVD. As the country prepares for BVD-free status under the EU Animal Health Law, stakeholders must consider strategies to detect possible re-emergence. Historical data from the eradication programme provides a unique opportunity to evaluate mortality-based syndromic surveillance for this purpose.This study aimed to develop a syndromic surveillance model based on calf mortality data and evaluate its use for early detection of BVD re-emergence in Ireland. For years 2014 through 2023, mixed-effects Cox proportional hazards models were built using calf mortality up to 100 days of age. Herd-level frailty estimates were extracted from these models for each year, which were then clustered to identify subgroups of herds with distinct temporal patterns in herd-level mortality hazard. Four separate thresholds were used to flag herds with increased calf mortality hazard. Overall, these flags demonstrated high specificity (86-92%) but low sensitivity (11-22%) for herd-level BVD status, suggesting that this approach alone would not reliably detect BVD re-emergence. Nonetheless, this method could support Ireland’s ability to achieve and sustain BVD-free status while providing valuable insights for similar surveillance efforts more broadly. This methodology is adaptable to other species, diseases, and syndromes, making it a versatile tool for animal health surveillance.

Anthropogenic disturbance of ecosystems modulates zoonosis dynamics, but its effects in multi-host systems are poorly understood. Perturbation events, such as forest clearfelling, impact disease dynamics uniquely in each disease-host-ecology system. We investigated how clearfell, across a density gradient of badgers, fallow, red, and sika deer, affected the relative risk of bovine tuberculosis (bTB) breakdown (positive diagnostic test results) in cattle. We hypothesized that higher wildlife densities would increase the risk of bTB breakdown, and that clearfell would alter its spatio-temporal risk—reducing it in the short term when nearby but increasing it in the longer term as regenerating sites may attract wildlife. We fit conditional logistic regressions to farm data (n = 33,054) across Ireland to evaluate the effect of clearfell activities at different distances from farms (2–5 km) and time lags after clearfell (0–12 or 24–36 months). Higher densities of active badger setts and sika deer were associated with higher relative risk of breakdown in cattle. The sika-deer-breakdown association, however, was driven by one county with extremely high relative deer abundance. Clearfell activities were associated with decreased relative risk of breakdown when red and sika deer were present, potentially due to dispersal of deer away from farms. These patterns only existed when relative deer densities were high and as area (ha) of clearfell increased. We provide evidence that clearfell may disturb wildlife, influencing breakdown risk in cattle herds and depending on wildlife species behaviour and population dynamics. Our study highlights the importance of monitoring and targeted management of deer populations in concert with other disease control goals.

INTRODUCTION: Coxiella burnetii, the causative agent of Q fever, is a notifiable zoonotic pathogen in Ireland. While typically subclinical in ruminants, infection is associated with reproductive losses. In humans, disease can range from asymptomatic to more serious complications. Ruminants have been identified as the main reservoir for human infection. Ireland’s dairy cattle industry has expanded substantially in recent years, yet current data on the national prevalence of Coxiella burnetii in dairy herds are limited. Understanding the herd-level prevalence and associated risk factors is essential for informing disease management and control strategies. METHODS: Bulk milk tank testing results from an Irish dairy cooperative herd health programme were analysed to determine the apparent and true prevalence of Coxiella burnetii antibodies, using 2022 data. Further analysis was conducted to determine the relationship between Coxiella burnetii prevalence and co-morbid disease and herd characteristics. RESULTS: 2687 dairy herds were included in the sample. The true prevalence of Coxiella burnetii antibodies was 61.7%. Coxiella burnetii prevalence was associated with increasing herd size, whereas higher herd average EBI and lower replacement rates were associated with decreased odds of infection. CONCLUSION: This study provides updated data, revealing the highest herd-level prevalence of Coxiella burnetii antibodies reported to date in Ireland. The association with larger herd size is particularly relevant with the substantial growth in the national dairy herd over the past decade. These findings reinforce the need for further research into transmission dynamics, impact on production and zoonotic risk.

BACKGROUND:Bovine tuberculosis (bTB), caused primarily by Mycobacterium bovis, remains a major challenge across the island of Ireland. Despite decades of an eradication programme that encompasses cattle testing, movement restrictions, and badger culling, bTB prevalence has increased in recent years. The epidemiology of bTB is complex, with inter-species (e.g. badger-cattle) and intra-species (e.g. cattle-cattle) transmission of infection. This study utilised whole-genome sequencing (WGS) to investigate the genetic diversity and spatial distribution of M. bovis across the island of Ireland as a route to help elucidate transmission of infection. RESULTS:A total of 5,875 M. bovis isolates from cattle and badgers were analysed to identify strain diversity, geographic clustering, and patterns of strain sharing within and between host species. Our findings reveal significant regional variation in strain distribution, with certain clades predominantly confined to specific regions, while others are more widely dispersed. Strong genetic similarities between cattle and badger isolates support the role of badgers as infection reservoirs. Furthermore, a subset of herds contained multi-strain infections and amongst these herds there were 'controlled finishing units' (CFUs), where infection was more likely driven by inward cattle movements than local transmission. CONCLUSIONS:By integrating phylogenetic analysis with spatial mapping and cattle movement data, this study provides new insights into M. bovis transmission pathways and highlights the value of WGS in refining Ireland's bTB control strategies.

Bovine tuberculosis (bTB), a zoonotic disease caused by Mycobacterium bovis , continues to challenge eradication efforts in Ireland and the UK, partly due to the role of the European badger ( Meles meles ) as a wildlife reservoir. Traditional management strategies often rely on sett (burrow) locations to infer badger distribution, which implicitly assumes a correlation with abundance. This study uses data from Ireland’s national badger culling and vaccination programme (2019–2025) to decouple badger and sett distributions using spatial point process modelling via log-Gaussian Cox processes. By separately modelling the environmental drivers of main sett and badger distributions, and validating outputs for ecological realism with independent badger body weight data, we demonstrate that sett and badger densities are governed by distinct ecological processes. Sett densities are driven by landscape features such as elevation, slope, and proximity to forest edges, while badger densities are more influenced by recent culling history and pasture availability. Our results reveal a spatial mismatch between high-density sett areas and high-density badger areas, highlighting the need for refined metrics in wildlife-based bTB management. These findings underscore the importance of integrating independently derived wildlife distribution models into disease control policies for more sustainable and effective bTB management. ### Competing Interest Statement The authors have declared no competing interest.

1.Land-use change can significantly alter wildlife movement patterns and behaviour which may increase the risk of zoonotic disease transmission, particularly diseases with wildlife maintenance hosts that intersect regularly with farmland such as bovine tuberculosis (bTB).2.This study utilized an agent-based model (ABM) with high-resolution input data to simulate badger responses to clearfell forestry across 100 sites in Ireland. The model environment was constructed from multiple GIS datasets, including sett locations, habitat, farm boundaries, and forestry operations. Animal movement and behaviour in the model were parameterised using detailed analysis of GPS tracking data from over 200 collared badgers in the wild.3.Our results show that badger density and herd type were the strongest predictors of alien encounters—instances where badgers move beyond their established territories and enter farms outside their usual home range. These encounters, if used as a proxy for bTB transmission risk, represent opportunities for badgers to interact with livestock, particularly cattle, increasing the likelihood of disease spread. Dairy herds, despite being fewer in number compared to beef and suckler enterprises, had a higher risk of alien encounters. Larger clearfell events significantly increased alien encounters, supporting the hypothesis that landscape disturbances elevate disease risk.4.Contrary to expectations, badger dispersal distance did not significantly affect encounter rates. Instead, landscape heterogeneity, including clearfell size and farm density, played a more substantial role in modulating encounters.5.Validation of the ABM using empirical bTB breakdown data confirmed a positive correlation between simulated badger encounter rates per study site and real world bTB outbreaks within those sites.6.This research provides a novel spatial framework for predicting bTB risk in disturbed landscapes, offering a valuable tool for Ireland's efforts to mitigate wildlife-livestock disease transmission. By integrating high-resolution GIS data and GPS-tracked wildlife movements, this approach enhances the accuracy of agent-based models (ABMs) for applied ecology and wildlife conflict research.

In the Republic of Ireland, the herd-level incidence of bovine tuberculosis (bTB), caused by Mycobacterium bovis, reached 6.40

Infection with Mycoplasma bovis (M. bovis) can present as a range of clinical manifestations of varying severity in beef and dairy cattle worldwide and can seriously impact cattle health and welfare. The objectives of this study were to characterise the strains and genetic diversity within isolates of M. bovis collected from bovine clinical samples in Ireland and Scotland, and to provide place these isolates a global phylogenetic context. We performed Illumina whole genome sequencing 19M. bovis strains from 19 unique Irish animals and 5M. bovis strains from 4 unique Scottish animals. The strains were then analysed against 117 downloaded Genbank assemblies to create a phylogenetic tree. The Irish strains clustered into 2 main groups which were identifiable as sequence type 29 (Group 1) and sequence type 21 (Group 2) using the pubMLST system. The Scottish strains all fell within Group 1 of our analysis and were identifiable as sequence type 29 using the pubMLST system. No novel sequence types were found. The Irish and Scottish strains are similar to the genetic variation of M. bovis currently seen in Europe and may suggest separate introductions. The impact of disease associated with M. bovis in cattle emphasises the importance of within and between herd biosecurity as well as the global nature of infectious disease due to widespread international cattle movement.

Between October 2018 and December 2020, an opportunistic collection of tissues from 218 foxes was undertaken to investigate the prevalence of Mycobacterium bovis (M. bovis) in this species. A pooled sample of lymph nodes, lung and other tissues from each fox, was cultured for the presence of M. bovis. The organism was not isolated from any fox samples, but non-tuberculous mycobacteria were recovered from 20 foxes. These results suggest that it is unlikely that foxes represent a significant wildlife source of M. bovis in Ireland.

Bovine Viral Diarrhoea (BVD) has been the focus of a successful eradication programme in Ireland, with the herd-level prevalence declining from 11.3 2013 to just 0.2 development of predictive models for targeted surveillance becomes increasingly important to mitigate the risk of disease re-emergence. In this study, we evaluate the performance of a range of machine learning algorithms, including binary classification and anomaly detection techniques, for predicting BVD-positive herds using highly imbalanced herd-level data. We conduct an extensive simulation study to assess model performance across varying sample sizes and class imbalance ratios, incorporating resampling, class weighting, and appropriate evaluation metrics (sensitivity, positive predictive value, F1-score and AUC values). Random forests and XGBoost models consistently outperformed other methods, with the random forest model achieving the highest sensitivity and AUC across scenarios, including real-world prediction of 2023 herd status, correctly identifying 219 of 250 positive herds while halving the number of herds that require compared to a blanket-testing strategy.

Bluetongue virus serotype 3 emerged in northern Europe in 2023 and 2024. As of September 2025, Ireland is bluetongue free. However, to inform control decisions in the event of a possible incursion, a surveillance plan to detect cases and estimate prevalence is required. We created an active surveillance plan for 20 km radius temporary control zones (TCZs) after initial case detection. Potential TCZs (n = 1,062) covering Ireland were generated, and surveillance sample sizes were estimated based on cattle data in each TCZ. A two-stage (herd and animal level) design accounted for within-herd clustering. We simulated implementation of the surveillance plan in each TCZ to understand surveillance performance in the Irish cattle population. Within herd prevalence of 30 https://www.arcgis.com/apps/dashboards/16722dde78d240f4a96303173bc6da2c .

Mycoplasmosis (due to infection with Mycoplasma bovis) ) is a serious disease of beef and dairy cattle that can adversely affect health, welfare, and productivity. Mycoplasmosis can lead to a range of often severe, clinical presentations. Mycoplasma bovis infection can present either clinically or subclinically, with the potential for recrudescence of shedding in association with stressful periods. Infection can be maintained within herds because of intermittent shedding. Mycoplasma bovis is recognized as poorly responsive to treatment, which presents a major challenge for control in infected herds. Given this, particular focus is needed on biosecurity measures to prevent introduction into uninfected herds in the first place. A robust and reliable laboratory test for surveillance is important for both herd-level prevention and control. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) of 3 diagnostic tests (1 PCR and 2 ELISA tests) on bulk tank milk (BTM), for the herd-level detection of M. bovis using Bayesian latent class analysis (BLCA). In autumn 2018, BTM samples from 11,807 herds, covering the majority of the main dairy regions in Ireland had been submitted to the Department of Agriculture testing laboratory for routine surveillance and were made available for study. A stratified random sample approach was used to select a cohort of herds for testing from this larger sample set. A final study population of 728 herds had BTM samples analyzed using a Bio-X ELISA (ELISA 1), an IDvet ELI- SA (ELISA 2) and a PCR test. A BLCA was conducted to estimate the Se and Sp of the 3 diagnostic tests applied to BTM for the detection of herd-level infection. An overall latent class analysis was conducted on all herds within a single population (a 3-test, 1-population model). The herds were also split into 2 populations based on herd size (small herds had <82 cattle; a 3-test, 2-population model) and separately into 3 regions in Ireland (Leinster, Munster, and Connacht/Ulster; a 3-test, 3-population model). The latent variable of interest was the herd-level M. bovis infection status. In total, 363/728 (50%) were large herds, 7 (1.0%) were positive on PCR, 88 (12%) positive on ELISA 1, and 406 (56%) positive on ELISA 2. Based on the 2-population model, the Se (95% Bayesian credible interval [BCI] was 0.03 (upper and lower limits: 0.02, 0.05), 0.22 (0.18, 0.27), and 0.94 (0.88, 0.98) for PCR, ELISA 1, and ELISA 2, respectively. The Sp (95% BCI) was 0.99 (0.99, 1.0), 0.97 (0.95, 0.99), and 0.92 (0.86, 0.97) for PCR, ELISA 1, and ELISA 2, respectively. The herd-level true prevalence was estimated at 0.43 (BCI 0.35, 0.5) for smaller herds. The true prevalence was estimated at 0.62 (BCI 0.55, 0.69) for larger herds. The true prevalence was estimated at 0.56 (BCI 0.49, 0.463) in the 1-population model. For the 3-population model, the Se (95% BCI) ) was 0.03 (0.02, 0.05), 0.24 (0.18, 0.29), and 0.95 (0.9, 0.98) for PCR, ELISA 1, and ELISA 2 respectively. The Sp (95% BCI) was 0.99 (0.99, 1.0), 0.98 (0.96, 0.99), and 0.88 (0.79, 0.95) for PCR, ELISA 1 and ELISA 2, respectively. The herd-level true prevalence (95% BCI) was estimated at 0.65 (0.56, 0.73), 0.38 (0.28, 0.46), and 0.53 (0.4, 0.65) for populations 1, 2, and 3 respectively. Across all 3 models, the range in true prevalence was 38% to 65% of Irish dairy herds infected with M. bovis. The operating characteristics vary substantially between tests. The IDvet ELISA had a relatively high Se (the highest Se of the 3 tests studied) but it was estimated at 0. 95 at its highest in 3-test, 3-population model. This test may be an appropriate test for herd-level screening or prevalence estimation within the context of the endemically infected Irish dairy cattle population. Further work is required to optimize this test and its interpretation when applied at herd-level to offset concerns related to the lower than optimal test Sp.

Farm fragmentation refers to spatial disaggregation of farms into smaller, often highly separated parcels of land. This can create a number of problems; administrative, economic, environmental and epidemiological. Ireland has a high proportion of fragmented farms, although this an issue not unique to Ireland. From a epidemiological perspective, where a farm is heavily fragmented, there is uncertainty in assigning a location to where livestock have spent time on that farm. We explore techniques to quantify the extent and regional variation in fragmentation and the between-fragment distances of fragmented farms in Ireland with the aim of reducing this uncertainty. The findings, which have made available as an online resource, allow for more precision in spatial analyses of bovine populations and help enhance surveillance and field epidemiology.

We describe the computation of metrics to inform the selection of areas for a regionalised approach to bovine tuberculosis eradication in Ireland. Our aim was not to recommend suitable regions but to elucidate the criteria used in metric selection and comment on the diversity of metric values amongst regions. The 26 counties of Ireland were compared using 20 metrics, grouped into five categories: region size and cattle population, herd fragmentation, cattle movement, bovine TB testing, badger population and control. Fragmentation metrics, measuring the proportion of herds with land in at least two counties, varied considerably by county, from 1% to 24 %. Between 25 % and 92 % of moves into herds came from a different county, illustrating the likely disruption in trade that a regionalized approach could entail. Cattle movement networks were combined with a risk model to calculate the proportion of moves which would be deemed risky under a risk-based trading regime and these results were compared to a more traditional approach based on the herd type and test history of each herd, with many fewer moves potentially restricted using the latter approach. We show how correlation between region size and some of the metrics complicates their interpretation.

Postnatal mortality among replacement stock has a detrimental effect on the social, economic, and environmental sustainability of dairy production. Calf mortality rates vary between countries and show differences in temporal trends; most, however, are characterized by high levels of between-farm variability. Explaining this variation can be difficult because herd-level information on management practices relevant to calf health is often not available. The Irish Johne's Control Programme (IJCP) contains a substantial on-farm monitoring program called the Veterinary Risk Assessment and Management Plan (VRAMP). Although this risk assessment is largely focused on factors relevant to the transmission of paratuberculosis, many of its principles are good practice biocontainment policies that are also advocated for the protection of calf health. The objectives of this study were (1) to quantify mortality in ear-tagged Irish dairy calves between 2016 and 2020 using both survival and risk approaches, (2) to determine risk factors for 100-d cumulative mortality hazard in ear-tagged Irish dairy calves between 2016 and 2020, (3) to determine whether 100-d cumulative mortality hazard was higher in ear-tagged calves within herds registered in the IJCP versus those that were not registered in the IJCP and whether there were differences between these cohorts over time, and (4) within IJCP herds, to determine whether VRAMP score or changes in VRAMP score were associated with 100-d cumulative mortality hazard. Excluding perinatal mortality, the overall 100-d cumulative mortality hazard was 4.1%. Calf mortality was consistently underestimated using risk approaches that did not account for calf censoring. Cox proportional hazards models showed that cumulative mortality hazard was greater in male calves; particularly, calves born to Jersey breed dams and those with a beef breed sire. Mortality hazard increased with increasing herd size, was highest in calves born in herds that contract-reared heifers, and lowest in those born in mixed dairy-beef enterprises. Mortality hazard decreased over time with the mortality hazard in 2020 being 0.83 times that of 2016. Mortality hazard was higher in IJCP-registered herds than nonregistered herds (hazard ratio 1.06, 95% CI 1.01–1.12), likely reflecting differences in herds that enrolled in the national program. However, we detected a significant interaction between IJCP status (enrolled vs. not enrolled) and year (hazard ratio 0.96, 95% CI 0.92–1.00), indicating that the decrease in mortality hazard between 2016 and 2020 was greater in IJCP herds versus non-IJCP herds. Finally, increasing VRAMP scores (indicating higher risk for paratuberculosis transmission) were positively associated with increased calf mortality hazard. Postnatal calf mortality rates in Irish dairy herds declined between 2016 and 2020. Our study suggests that implementation of recommended biocontainment practices to control paratuberculosis in IJCP herds was associated with a reduction in calf mortality hazard.

"Immunosuppressed Pets as a Conduit for Zoonotic Tuberculosis." American Journal of Respiratory and Critical Care Medicine, 208(6), pp. 732–733

Bovine tuberculosis (bTB), caused by Mycobacterium bovis, is one of the most challenging and persistent health issues in many countries worldwide. In several countries, bTB control is complicated due to the presence of wildlife reservoirs of infection, i.e. European badger (Meles meles) in Ireland and the UK, which can transmit infection to cattle. However, a quantitative understanding of the role of cattle and badgers in bTB transmission is elusive, especially where there is spatial variation in relative density between badgers and cattle. Moreover, as these two species have infrequent direct contact, environmental transmission is likely to play a role, but the quantitative importance of the environment has not been assessed. Therefore, the objective of this study is to better understand bTB transmission between cattle and badgers via the environment in a spatially explicit context and to identify high-risk areas. We developed an environmental transmission model that incorporates both within-herd/territory transmission and between-species transmission, with the latter facilitated by badger territories overlapping with herd areas. Model parameters such as transmission rate parameters and the decay rate parameter of M. bovis were estimated by maximum likelihood estimation using infection data from badgers and cattle collected during a 4-year badger vaccination trial. Our estimation showed that the environment can play an important role in the transmission of bTB, with a half-life of M. bovis in the environment of around 177 days. Based on the estimated transmission rate parameters, we calculate the basic reproduction ratio (R) within a herd, which reveals how relative badger density dictates transmission. In addition, we simulated transmission in each small local area to generate a first between-herd R map that identifies high-risk areas.

This poster will present details of a novel research project which aims to assist the Irish government to mitigate the impact of various incidents on Ireland such as infectious diseases for animals, contamination of animal feedstuffs, nuclear accidents/incidents/events abroad, radioactive contamination, environmental pollution, fire, and volcanic eruptions impacting Ireland.Atmospheric dispersion modelling is the mathematical simulation of how air pollutants disperse in the atmosphere. It is performed using computer simulations which use algorithms to solve mathematical equations that govern the dispersion of airborne particles. Dispersion models estimate the downwind ambient concentration of air pollutants emitted from 1) man-made sources (e.g. industrial plants, vehicular traffic, accidental chemical/nuclear releases), and 2) natural sources (e.g. small insects, pollen, dust or volcanic ash). Dispersion models can also be used to predict future concentrations of particles under specific scenarios (e.g. pollen forecasting based on weather data, the spread of Bluetongue virus, and the spread of Foot & Mouth disease).Currently, Ireland’s national meteorological service (Met Éireann) provides numerical weather prediction data to Ireland’s Environmental Protection Agency (EPA), to simulate the dispersion of nuclear material into the atmosphere. Met Éireann supports the EPA’s modelling capability by producing a daily automated simulated nuclear release. Met Éireann performs operational Bluetongue Virus forecasting, which is sent to relevant agricultural stakeholders, and supports University College Dublin (UCD) in their modelling of Foot and Mouth disease.As climate change continues, a range of pests previously unknown in Ireland are likely to find favourable conditions here, which could potentially harm native species of plants and animals. Investigation of potential sources of these pests, and assessment of their ability to travel over large distances on prevailing winds, could help prevent losses of livestock, crops and biodiversity.To improve Ireland’s national dispersion modelling capabilities, Met Éireann propose to commence a 4-year research project in 2023 using dispersion models and high-resolution meteorological data to build forecast capacity for a range of airborne particles that can affect human, animal and plant health. Such airborne particles include bioaerosols (vector-borne diseases, pollen and fungal spores), forest fire smoke, volcanic ash plumes and Saharan dust. High-resolution meteorological data and ensemble prediction systems will be employed to identify the locations in Ireland that are likely to be affected by various aerosols under different weather conditions. Met Éireann seeks to be an authoritative source for dispersion forecasts in Ireland, which could be of significant benefit to the agriculture industry and the significant number of Irish people who suffer from asthma, hay-fever and other respiratory illnesses.

Bovine tuberculosis (bTB) is a chronic infectious disease caused by Mycobacterium bovis which results in a significant economic cost to cattle industries and governments where it is endemic. In Ireland, the European badger is the main wildlife reservoir of infection. In this study, we investigated whether (motorway) road construction was associated with an increased risk of bTB in associated cattle herds. For this study, we considered three observation periods: pre-construction (2011-2014), construction (2015-2017) and post-construction (2018-2019). We selected 1543 herds situated, based on proximity, between >50 m and <5 km of the roadworks, and extracted information about their herd-size, herd-type, inward animal movements, bTB history, and distance to the roadworks. Generalized linear mixed models were performed, whose outcome were whether a herd experienced a bTB breakdown with ≥1 or ≥3 standard reactor/s, respectively. Herds located at a distance of >3 km from the roadworks were found to be at reduced risk of a bTB breakdown over the construction period compared with those situated within 1 km of the roadworks for ≥1 reactor/s (>3 km and construction vs. <1 km: OR: 0.595, 95 % Confidence Interval (CI): 0.354-0.999) or ≥3 reactors (>3 km and construction vs. <1 km: OR: 0.431, 95 % CI: 0.174-1.067). Other previously reported risk factors such as inward movements, herd-size and herd-type were also associated with bTB risk in the final models (≥1 reactor/s and ≥3 reactors). These findings appear to be consistent with bTB breakdowns being a consequence as opposed to coincident to road construction, given the temporal and spatial consistency of the evidence. The potential for badger social group disturbance leading to the spatial spread of infection to cattle herds, as previously described in the United Kingdom, could be a hypothetical mechanism to explain these findings. However, our findings are not consistent with previous Irish studies, including recent work from another road construction project, albeit running alongside and cross over an existing road rather than construction of a new road as in this case, or experiences from national targeted badger removal. Further research is warranted to verify this pattern occurs elsewhere, and the underlying biological mechanism. Until further data are available, we recommend that badgers are vaccinated, as a precautionary measure, in advance of the commencement of major roadworks.