Contribution of herd management, biosecurity, and environmental factors to the risk of bovine tuberculosis in a historically low prevalence region.

Eradication of bovine tuberculosis (bTB) in certain historically low-prevalence regions remains elusive. A complete characterisation of the husbandry practices, biosecurity, and environment where farms are located is crucial to implement targeted in-farm risk mitigation protocols. Here, a detailed survey performed in 94 dairy cattle farms located in Navarra, a low-prevalence region of Spain between 2016 and 2020 was carried out. Data on 73 biosecurity, farm-, and environmental-level factors potentially associated with the risk of bTB occurrence were evaluated using an ordinal logistic regression model: farms were classified based on their prevalence index, a score linked to each farm to account for the severity and recurrence of bTB cases: 22.3% of the farms had a score of 1, 21.3% a score of 2, 26.6% a score of ≥ 3, and 29.8% were negative herds. A statistically significant association between a higher prevalence index and the frequency of badger sightings along with the lease of pastures to sheep during Winter was identified. Farms that detected badgers on a monthly to daily basis in the surroundings and those that leased pastures for sheep flocks during Winter were four [odds ratio, 95% CI (4.3; 1.1-17.5)] and three (3.1; 1.0-9.9) times more likely to have the highest prevalence index, respectively (predicted probabilityprevalence index≥3 = 0.7; 95% CI 0.3-0.9). Conversely, farms that used a vehicle to transport animals from holdings to pastures were less likely (0.1; <0.1-0.3) to present higher levels of prevalence index compared with farms that used none (on foot). Results suggested that the combined effect of farm- and environmental-level risk factors identified here may be hampering disease eradication in Navarra, highlighting the need to implement targeted protocols on farms and grazing plots. An increased awareness of monitoring sheep and wildlife in direct or indirect contact with cattle herds in historically low bTB prevalence areas should be raised.

The impact of changing the cut-off threshold of the interferon-gamma (IFN-γ) assay for diagnosing bovine tuberculosis in Ireland.

In Ireland, the interferon-gamma (IFN-γ) assay is routinely used as an ancillary test interpreted in parallel with the single intradermal comparative tuberculin test (SICTT) to maximize the detection of bovine tuberculosis (bTB) infected animals. Up until 2018, a positive test result was recorded in the IFN-γ ELISA assay following whole blood stimulation with purified protein derivative (PPD)-bovine (B), PPD-avian (A) and nil sample (N), using the interpretation criteria, B-N > 50 optical density units (OD), B > 100 and B-A > 0. Following a review of available data, the threshold of the B-A component changed to B-A > 80. As predicting the impact of changing the cut-off thresholds for the IFN-γ test de novo is challenging, the aims of this study were to follow animals that initially tested negative using the new IFN-γ assay interpretation criteria and investigate their future risk of disclosure with bTB, with a focus on animals that otherwise would have been removed when using the older interpretation criteria (0 < B-A ≤ 80). Enrolled animals (n = 28,669 cattle from 527 herds) were followed up for two years (2019-2021), or to point of bTB detection or death. At the end of follow-up, 1151 (4.0%) of enrolled animals were bTB cases. The majority of these cases were diagnosed using SICTT (80.5%). The cumulative number of positive animals that would have been removed if the old cut-off (0 < B-A ≤ 80) was used amounted to 1680 cattle (5.9% of the enrolled cohort). Of these, 127 (7.5%) were diagnosed with bTB during follow-up. In contrast, 1024 of the 1151 cattle which subsequently tested positive during the study period following a negative IFN-γ test would not have been identified with the old or new IFN-γ cut-off criteria. Survival analysis showed that animals that would have been removed under the old interpretation criteria were at increased risk of a positive diagnosis with bTB during follow-up compared to other test negative animals. A newly developed risk prediction model (using a Cox proportional hazard model) showed that age, animal number of SICTT tests, number of inconclusive SICTT tests, B-A (IFN-γ assay), B-N (IFN-γ assay), animals from store herds and the percentage of the rest of the herd that were positive during the breakdown were statistically significantly associated with bTB detection. However, inclusion of the IFN-γ OD variables did not show added value in terms of prediction performance of the model.

Difference in differences analysis evaluates the effects of the badger control policy on bovine tuberculosis in England.

Persistent tuberculosis (TB) in cattle populations in England has been associated with an exchange of infection with badgers (Meles meles). A badger control policy (BCP) commenced in 2013. Its aim was to decrease TB incidence in cattle by reducing the badger population available to provide a wildlife reservoir for bovine TB. Monitoring data from 52 BCP intervention areas 200-1600 km2 in size, starting over several years, were used to estimate the change in TB incidence rate in cattle herds, which was associated with time since the start of the BCP in each area. A difference in differences analysis addressed the non-random selection and starting sequence of the areas. The herd incidence rate of TB reduced by 56% (95% Confidence Interval 41-69%) up to the fourth year of BCP interventions, with the largest drops in the second and third years. There was insufficient evidence to judge whether the incidence rate reduced further beyond 4 years. These estimates are the most precise for the timing of declines in cattle TB associated with interventions primarily targeting badgers. They are within the range of previous estimates from England and Ireland. This analysis indicates the importance of reducing transmission from badgers to reduce the incidence of TB in cattle, noting that vaccination of badgers, fertility control and on farm biosecurity may also achieve this effect.

Whole-Genome sequencing in routine Mycobacterium bovis epidemiology - scoping the potential.

Mycobacterium bovis the main agent of bovine tuberculosis (bTB), presents as a series of spatially-localised micro-epidemics across landscapes. Classical molecular typing methods applied to these micro-epidemics, based on genotyping a few variable loci, have significantly improved our understanding of potential epidemiological links between outbreaks. However, they have limited utility owing to low resolution. Conversely, whole-genome sequencing (WGS) provides the highest resolution data available for molecular epidemiology, producing richer outbreak tracing, insights into phylogeography and epidemic evolutionary history. We illustrate these advantages by focusing on a common single lineage of M. bovis (1.140) from Northern Ireland. Specifically, we investigate the spatial sub-structure of 20 years of herd-level multi locus VNTR analysis (MLVA) surveillance data and WGS data from a down sampled subset of isolates of this MLVA type over the same time frame. We mapped 2108 isolate locations of MLVA type 1.140 over the years 2000-2022. We also mapped the locations of 148 contemporary WGS isolates from this lineage, over a similar geographic range, stratifying by single nucleotide polymorphism (SNP) relatedness cut-offs of 15 SNPs. We determined a putative core range for the 1.140 MLVA type and SNP-defined sequence clusters using a 50 % kernel density estimate, using cattle movement data to inform on likely sources of WGS isolates found outside of core ranges. Finally, we applied Bayesian phylogenetic methods to investigate past population history and reproductive number of the 1.140 M. bovis lineage. We demonstrate that WGS SNP-defined clusters exhibit smaller core ranges than the established MLVA type - facilitating superior disease tracing. We also demonstrate the superior functionality of WGS data in determining how this lineage was disseminated across the landscape, likely via cattle movement and to infer how its effective population size and reproductive number has been in flux since its emergence. These initial findings highlight the potential of WGS data for routine monitoring of bTB outbreaks.

Assessment of the diagnostic performance of intradermal tuberculin test and post-mortem inspection for the diagnosis of bovine tuberculosis according to WOAH guidelines.

Bovine tuberculosis (bTB) constitutes a global challenge for public and animal health with still some deficiencies regarding its diagnosis. This study aimed to estimate the accuracy of the single intradermal tuberculin test (SIT) and post-mortem inspection for different diagnostic objectives following WOAH guidelines. Tissue samples from 59 microbiological culture/PCR-positive and 58 microbiological culture/PCR-negative cattle were evaluated. The diagnostic sensitivity and specificity, the positive and negative probability indices as well as the positive and negative predictive values (PPV and NPV) of each technique were estimated for different pretest probabilities. The SIT with strict interpretation demonstrated moderate precision in confirming the absence of infection in populations historically free of bTB, with a 12.1% rate of false positives, but also detecting positive animals in the early stage of the eradication programs, with a 13.6% rate of false negatives. The diagnostic performance for ruling out bTB was notably high (NPV > 90%) in animals with a pre-test probability (PTP) below 42%. Post-mortem inspection constituted an interesting alternative tool to confirm suspected and positive cases for SIT, particularly in areas with bTB prevalence exceeding 19%, where implementing SIT and eradication measures may be impractical. In these areas, the likelihood that animals with tuberculosis-like lesions are affected by the disease surpasses 90%. Similarly, in herds with a PTP below 25%, the absence of bTB could be confidently ruled out with over 90% certainty. These findings highlight the effectiveness of SIT and post-mortem inspection as valuable techniques for current eradication programs and controlling bTB in high-prevalence areas where molecular techniques may not be feasible.

Wildlife vaccination strategies for eliminating bovine tuberculosis in white-tailed deer populations.

Many pathogens of humans and livestock also infect wildlife that can act as a reservoir and challenge disease control or elimination. Efficient and effective prioritization of research and management actions requires an understanding of the potential for new tools to improve elimination probability with feasible deployment strategies that can be implemented at scale. Wildlife vaccination is gaining interest as a tool for managing several wildlife diseases. To evaluate the effect of vaccinating white-tailed deer (Odocoileus virginianus), in combination with harvest, in reducing and eliminating bovine tuberculosis from deer populations in Michigan, we developed a mechanistic age-structured disease transmission model for bovine tuberculosis with integrated disease management. We evaluated the impact of pulse vaccination across a range of vaccine properties. Pulse vaccination was effective for reducing disease prevalence rapidly with even low (30%) to moderate (60%) vaccine coverage of the susceptible and exposed deer population and was further improved when combined with increased harvest. The impact of increased harvest depended on the relative strength of transmission modes, i.e., direct vs indirect transmission. Vaccine coverage and efficacy were the most important vaccine properties for reducing and eliminating disease from the local population. By fitting the model to the core endemic area of bovine tuberculosis in Michigan, USA, we identified feasible integrated management strategies involving vaccination and increased harvest that reduced disease prevalence in free-ranging deer. Few scenarios led to disease elimination due to the chronic nature of bovine tuberculosis. A long-term commitment to regular vaccination campaigns, and further research on increasing vaccines efficacy and uptake rate in free-ranging deer are important for disease management.

Molecular detection and characterization of the Mycobacterium tuberculosis complex subspecies responsible for bovine tuberculosis in Punjab, Pakistan.

Bovine tuberculosis (bTB), traditionally associated with Mycobacterium bovis, presents significant public health and economic challenges worldwide. This study investigated the causative agents of bTB in slaughtered cattle and buffalo in Lahore, Pakistan. Of the 3,581 animals screened, 34 were identified with gross TB-like lesions. The lesions were processed for culture, PCR, and Sanger sequencing to identify the causative agents of the disease. The results identified 10 Mycobacterium orygis and 8 Mycobacterium tuberculosis sensu stricto isolates. Whole-genome sequencing was performed on two M. orygis isolates, and the sequences were phylogenetically compared to 93 publicly available M. orygis sequences. The results also demonstrated that the JB21 and JB22 primers, which have been previously commonly applied to detect M. bovis in Pakistan, are unable to distinguish between M. tuberculosis complex subspecies. The identification of M. orygis and M. tuberculosis as causative agents of bTB in this slaughterhouse in Punjab may have important implications in identifying cases of zoonotic TB in humans and applying appropriate molecular tools to identify the prevalence of the disease. The data from this study align with recent findings suggesting M. orygis is the predominant cause of bTB in South Asia.IMPORTANCEThe study findings hold significant relevance to the Journal of Clinical Microbiology, as they directly impact the field. The first-time identification of Mycobacterium orygis and Mycobacterium tuberculosis as the predominant causative agents of bovine tuberculosis in Lahore, Pakistan underscores the urgent need for enhanced diagnostic methods. The study emphasizes the importance of improved assays for the accurate detection and differentiation of Mycobacterium subspecies. Additionally, the research addresses zoonotic risk assessment and public health implications, advocating for a multidisciplinary approach that integrates clinical microbiology with veterinary and human health sectors. These insights contribute to clinical microbiology knowledge, shaping effective strategies for disease prevention, surveillance, and control. The study's potential to advance the field makes it well suited for publication in the Microbiology Spectrum journal.

Using machine learning to improve bovine tuberculosis control in herd level outbreaks.

Bovine tuberculosis (bTB), a chronic disease of cattle, is caused by the Mycobacterium bovis infection. Despite having a serious social and economic impact in the United Kingdom and Ireland, there is no antemortem gold standard diagnostic test. Tuberculin skin tests (CICT) are commonly used as a control measure with the interferon gamma (IFN-γ) assay being applied in certain circumstances. This paper utilizes data gathered describing tuberculin regression in reactors (test positive cattle) following the CICT at 72 ± 4 h post injection in herds with large bTB outbreaks. The work then applies machine learning techniques (Decision Trees, Bagging Trees and Random Forests, alongside several balancing approaches) to predict which cattle were likely to be truly infected with tuberculosis, enabling identification of atypical breakdowns that require extra investigation and providing a mechanism for quality assurance of the existing CICT bTB surveillance scheme. The analysis showed that Random Forests (RF) trained using SMOTE balancing had the joint best performance and accuracy (0.90). The importance of the two components of the interferon gamma assay within the RF model also indicated that varying the assay threshold for large outbreaks would be beneficial. Furthermore, the combined use of the RF and IFN- γ models could lead to the improved detection of infection within breakdown herds, reducing the scale and duration of outbreaks. An additional use of these models would be for quality assuring the current bTB surveillance based on CICT and post mortem inspection. Quality control is well recognized as an essential component of a disease surveillance/eradication programme.Clinical Relevance- Bovine tuberculosis remains a disease that is hard to control on a national level. The use of the machine learning model could lead to significant improved detection of infection within breakdown herds, reducing the scale and duration of outbreaks. Advanced modelling, such as this, has the potential to strengthen the efficacy of disease surveillance and the eradication strategy and can meaningfully contribute to animal disease national control plans.