Wild boar visits to commercial pig farms in southwest England: implications for disease transmission.

Contact between wild animals and farmed livestock may result in disease transmission with huge financial, welfare and ethical consequences. Conflicts between people and wildlife can also arise when species such as wild boar (Sus scrofa) consume crops or dig up pasture. This is a relatively recent problem in England where wild boar populations have become re-established in the last 20 years following a 500-year absence. The aim of this pilot study was to determine if and how often free-living wild boar visited two commercial pig farms near the Forest of Dean in southwest England. We placed 20 motion-sensitive camera traps at potential entry points to, and trails surrounding, the perimeter of two farmyards housing domestic pigs between August 2019 and February 2021, covering a total of 6030 trap nights. Forty wild boar detections were recorded on one farm spread across 27 nights, with a median (range) of 1 (0 to 7) night of wild boar activity per calendar month. Most of these wild boar detections occurred between ten and twenty metres of housed domestic pigs. No wild boar was detected at the other farm. These results confirm wild boar do visit commercial pig farms, and therefore, there is potential for contact and pathogen exchange between wild boar and domestic pigs. The visitation rates derived from this study could be used to parameterise disease transmission models of pathogens common to domestic pigs and wild boars, such as the African swine fever virus, and subsequently to develop mitigation strategies to reduce unwanted contacts.

Social and environmental factors affect tuberculosis related mortality in wild meerkats.

Tuberculosis (TB) is an important and widespread disease of wildlife, livestock and humans world-wide, but long-term empirical datasets describing this condition are rare. A population of meerkats (Suricata suricatta) in South Africa's Kalahari Desert have been diagnosed with Mycobacterium suricattae, a novel strain of TB, causing fatal disease in this group-living species. This study aimed to find characteristics associated with clinical TB in meerkats. These characteristics could subsequently be used to identify 'at-risk' animals within a population, and target these individuals for control measures. We conducted a retrospective study based on a unique, long-term life-history dataset of over 2000 individually identified animals covering a 14-year period after the first confirmatory diagnosis of TB in this population in 2001. Individual- and group-level risk factors were analysed using time-dependent Cox regression to examine their potential influence on the time to development of end-stage TB. Cases of disease involved 144 individuals in 27 of 73 social groups, across 12 of 14 years (an incidence rate of 3·78 cases/100 study years). At the individual level, increasing age had the greatest effect on risk of disease with a hazard ratio of 4·70 (95% CI: 1·92-11·53, P < 0·01) for meerkats aged 24-48 months, and a hazard ratio of 9·36 (3·34-26·25, P < 0·001) for animals aged over 48 months (both age categories compared with animals aged below 24 months). Previous group history of TB increased the hazard by a factor of 4·29 (2·00-9·17, P < 0·01), and an interaction was found between this variable and age. At a group level, immigrations of new group members in the previous year increased hazard by a factor of 3·00 (1·23-7·34, P = 0·016). There was weaker evidence of an environmental effect with a hazard ratio for a low rainfall (<200 mm) year of 2·28 (0·91-5·72, P = 0·079). Our findings identify potential individual characteristics on which to base targeted control measures such as vaccination. Additional data on the dynamics of the infection status of individuals and how this changes over time would complement these findings by enhancing understanding of disease progression and transmission, and thus the implications of potential management measures.

Beyond aggression: Androgen-receptor blockade modulates social interaction in wild meerkats.

In male vertebrates, androgens are inextricably linked to reproduction, social dominance, and aggression, often at the cost of paternal investment or prosociality. Testosterone is invoked to explain rank-related reproductive differences, but its role within a status class, particularly among subordinates, is underappreciated. Recent evidence, especially for monogamous and cooperatively breeding species, suggests broader androgenic mediation of adult social interaction. We explored the actions of androgens in subordinate, male members of a cooperatively breeding species, the meerkat (Suricata suricatta). Although male meerkats show no rank-related testosterone differences, subordinate helpers rarely reproduce. We blocked androgen receptors, in the field, by treating subordinate males with the antiandrogen, flutamide. We monitored androgen concentrations (via baseline serum and time-sequential fecal sampling) and recorded behavior within their groups (via focal observation). Relative to controls, flutamide-treated animals initiated less and received more high-intensity aggression (biting, threatening, feeding competition), engaged in more prosocial behavior (social sniffing, grooming, huddling), and less frequently initiated play or assumed a 'dominant' role during play, revealing significant androgenic effects across a broad range of social behavior. By contrast, guarding or vigilance and measures of olfactory and vocal communication in subordinate males appeared unaffected by flutamide treatment. Thus, androgens in male meerkat helpers are aligned with the traditional trade-off between promoting reproductive and aggressive behavior at a cost to affiliation. Our findings, based on rare endocrine manipulation in wild mammals, show a more pervasive role for androgens in adult social behavior than is often recognized, with possible relevance for understanding tradeoffs in cooperative systems.

Peste des petits ruminants virus in Heilongjiang province, China, 2014.

During March 25-May 5, 2014, we investigated 11 outbreaks of peste des petits ruminants in Heilongjiang Province, China. We found that the most likely source of the outbreaks was animals from livestock markets in Shandong. Peste des petits ruminants viruses belonging to lineages II and IV were detected in sick animals.

Novel cause of tuberculosis in meerkats, South Africa.

The organism that causes tuberculosis in meerkats (Suricata suricatta) has been poorly characterized. Our genetic analysis showed it to be a novel member of the Mycobacterium tuberculosis complex and closely related to the dassie bacillus. We have named this epidemiologically and genetically unique strain M. suricattae.

Global prioritization of endemic zoonotic diseases for conducting surveillance in domestic animals to protect public health.

Zoonotic diseases (zoonoses) originating from domestic animals pose a significant risk to people's health and livelihoods, in addition to jeopardizing animal health and production. Effective surveillance of endemic zoonoses at the animal level is crucial to assessing the disease burden and risk, and providing early warning to prevent epidemics in animals and spillover to humans. Here we aimed to prioritize and characterize zoonoses for which surveillance in domestic animals is important to prevent human infections at a global scale. A multi-criteria qualitative approach was used, where disease-specific information was obtained across literature of the leading international health organizations. Thirty-two zoonoses were prioritized, all of which have multi-regional spread, cause unexceptional human infections and have domestic animal hosts as important sources or sentinels of zoonotic infections. Most diseases involve multiple animal hosts and/or modes of zoonotic transmission, where a lack of specific clinical signs in animals further complicates surveillance. We discuss the challenges of animal health surveillance in endemic and resource-limited settings, as well as potential avenues for improvement such as the multi-disease, multi-sectoral and digital surveillance approaches. Our study will support global capacity-building efforts to strengthen the surveillance and control of endemic zoonoses at their animal sources. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Assessing the potential impact of applying a higher sensitivity test to selected cattle populations for the control of bovine tuberculosis in England.

Bovine tuberculosis (bTB) continues to be the costliest, most complex animal health problem in England. The effectiveness of the test-and-slaughter policy is hampered by the imperfect sensitivity of the surveillance tests. Up to half of recurrent incidents within 24 months of a previous one could have been due to undetected infected cattle not being removed. Improving diagnostic testing with more sensitive tests, like the interferon (IFN)-gamma test, is one of the government's top priorities. However, blanket deployment of such tests could result in more false positive results (due to imperfect specificity), together with logistical and cost-efficiency challenges. A targeted application of such tests in higher prevalence scenarios, such as a subpopulation of high-risk herds, could mitigate against these challenges. We developed classification machine learning algorithms (using 80% of 2012-2019 bTB surveillance data as the training set) to evaluate the deployment of IFN-gamma testing in high-risk herds (i.e. those at risk of an incident in England) in two testing data sets: i) the remaining 20% of 2012-19 data, and ii) 2020 bTB surveillance data. The resulting model, classification tree analysis, with an area under a receiver operating characteristic (ROC) curve (AUC) > 95, showed a 73% sensitivity and a 97% specificity in the 2012-2019 test dataset. Used on 2020 data, it predicted eight percent (3 510 of 41 493) of eligible active herds as at-risk of a bTB incident, the majority of them (66% or 2 328 herds) experiencing at least one. Whilst all predicted at-risk herds could have preventive measures applied, the additional application of IFN-gamma test in parallel interpretation to the statutory skin test, if the risk materialises, would have resulted in 8 585 additional IFN-gamma reactors detected (a 217% increase over the 2 710 IFN-gamma reactors already detected by tests carried out). Only 18% (330 of 1 819) of incidents in predicted high-risk herds had the IFN-gamma test applied in 2020. We therefore conclude that this methodology provides a better way of directing the application of the IFN-gamma test towards the high-risk subgroup of herds. Classification tree analysis ensured the systematic identification of high-risk herds to consistently apply additional measures in a targeted way. This could increase the detection of infected cattle more efficiently, preventing recurrence and accelerating efforts to achieve eradication by 2038. This methodology has wider application, like targeting improved biosecurity measures in avian influenza at-risk farms to limit damage to the industry in future outbreaks.

Survey of infections transmissible between baboons and humans, Cape Town, South Africa.

Baboons on South Africa's Cape Peninsula come in frequent contact with humans. To determine potential health risks for both species, we screened 27 baboons from 5 troops for 10 infections. Most (56%) baboons had antibodies reactive or cross-reactive to human viruses. Spatial overlap between these species poses low but potential health risks.

Serologic responses correlate with current but not future bacterial shedding in badgers naturally infected with Mycobacterium bovis.

Bovine tuberculosis is a challenging cattle disease with substantial economic costs in affected countries. Eradication in parts of the United Kingdom and Ireland is hindered by transmission of the causative agent Mycobacterium bovis between cattle and European badgers (Meles meles). Diagnostic tests in badgers are of limited accuracy but may help us understand and predict disease progression. This study aimed to determine the practical ability of a commercially available serologic test, the Dual Path Platform VetTB assay (DPP), to predict mycobacterial shedding (i.e. infectiousness) and disease progression in badgers, and whether test outcomes were associated with re-capture. Clinical samples collected from 2014 to 2019 from a wild, naturally infected population of badgers in southwest England were tested using mycobacterial culture (from sputum, urine, faeces, abscesses and bite wounds), an interferon-gamma release assay and the DPP assay. Data were analysed at both individual badger and social group levels using generalised linear and cumulative-link mixed models, and linear regression. Only the highest DPP readings [optical density relative light unit (RLU) levels] were associated with mycobacterial shedding [odds ratio (OR) for DPP levels > 100 RLU in individual badgers: 79.6, 95%CI: 14.7-848; and for social groups: OR: 7.28, 95%CI: 2.94-21.44; compared with levels < 100 RLU]. For individual badgers, RLU levels at first capture were not associated with disease progression at subsequent captures. Finally, badgers with very high DPP levels (> 1000 RLU) were four times less likely to be recaptured (OR: 0.24, 95%CI: 0.07-0.83) than those without a detectable DPP response, which might indicate enhanced mortality. We conclude that DPP levels of > 100 RLU identify badgers that are likely to be shedding M. bovis. Levels of > 1000 RLU identify badgers that are much less likely to be re-captured. These results provide insights into the potential value of existing tests in intervention strategies for managing M. bovis in badgers.

Trait-Based Vaccination of Individual Meerkats (Suricata suricatta) against Tuberculosis Provides Evidence to Support Targeted Disease Control.

Individuals vary in their potential to acquire and transmit infections, but this fact is currently underexploited in disease control strategies. We trialled a trait-based vaccination strategy to reduce tuberculosis in free-living meerkats by targeting high-contact meerkats (socially dominant individuals) in one study arm, and high-susceptibility individuals (young subordinates) in a second arm. We monitored infection within vaccinated groups over two years comparing the results with untreated control groups. Being a member of a high-contact group had a protective effect on individuals' survival times (Hazard Ratio = 0.5, 95% Confidence Interval, CI: 0.29-0.88, p = 0.02) compared to control groups. Over the study, odds of testing positive for tuberculosis increased more than five-fold in control groups (Odds Ratio = 5.40, 95% CI = 0.94-30.98, p = 0.058); however, no increases were observed in either of the treatment arms. Targeted disease control approaches, such as the one described in this study, allow for reduced numbers of interventions. Here, trait-based vaccination was associated with reduced infection rates and thus has the potential to offer more efficient alternatives to traditional mass-vaccination policies. Such improvements in efficiency warrant further study and could make infectious disease control more practically achievable in both animal (particularly wildlife) and human populations.

The importance of fine-scale predictors of wild boar habitat use in an isolated population.

Predicting the likelihood of wildlife presence at potential wildlife-livestock interfaces is challenging. These interfaces are usually relatively small geographical areas where landscapes show large variation over small distances. Models of wildlife distribution based on coarse data over wide geographical ranges may not be representative of these interfaces. High-resolution data can help identify fine-scale predictors of wildlife habitat use at a local scale and provide more accurate predictions of species habitat use. These data may be used to inform knowledge of interface risks, such as disease transmission between wildlife and livestock, or human-wildlife conflict.This study uses fine-scale habitat use data from wild boar (Sus scrofa) based on activity signs and direct field observations in and around the Forest of Dean in Gloucestershire, England. Spatial logistic regression models fitted using a variant of penalized quasi-likelihood were used to identify habitat-based and anthropogenic predictors of wild boar signs.Our models showed that within the Forest of Dean, wild boar signs were more likely to be seen in spring, in forest-type habitats, closer to the center of the forest and near litter bins. In the area surrounding the Forest of Dean, wild boar signs were more likely to be seen in forest-type habitats and near recreational parks and less likely to be seen near livestock.This approach shows that wild boar habitat use can be predicted using fine-scale data over comparatively small areas and in human-dominated landscapes, while taking account of the spatial correlation from other nearby fine-scale data-points. The methods we use could be applied to map habitat use of other wildlife species in similar landscapes, or of movement-restricted, isolated, or fragmented wildlife populations.

Machine learning classification methods informing the management of inconclusive reactors at bovine tuberculosis surveillance tests in England.

Bovine tuberculosis (bTB) remains one of the most complex, challenging, and costly animal health problems in England. Identifying and promptly removing all infected cattle from affected herds is key to its eradication strategy; the imperfect sensitivity of the diagnostic testing regime remaining a serious obstacle. The main diagnostic test for bTB in cattle in England, the Single Intradermal Comparative Cervical Tuberculin Test (SICCT: also known as the skin test), can produce inconclusive results below the reactor threshold. The immediate isolation of inconclusive reactor (IR) animals followed by a 60-day retest may not prevent lateral spread within the herd (if it is substandard, allowing transmission) or transmission to wildlife. Over half of IR-only herds that went on to have a positive skin test result (a bTB herd 'incident') in 2020, had it triggered by at least one IR not clearing their 60-day retest, instead of by another test within the previous 15 months. Machine learning classification algorithms (classification tree analysis and random forest), applied to England's 2012-2020 IR-only surveillance herd tests, identified at-risk tests for an incident at the IRs' 60-day retest. In this period, 4 739 out of 22 946 (21 %) IR-only surveillance tests disclosing 6 296 out of 42 685 total IRs, had an incident at retest (2 716 IRs became reactors and 3 580 IRs became two-time IRs). Both models showed an AUC above 80 % in the 2012-2019 dataset. Classification tree analysis was preferred due to its easy-to-interpret outputs, 70 % sensitivity, and 93 % specificity in the 20 % of 2019-2020 testing dataset. The paper aimed to identify IR-only surveillance tests at-risk of an incident at the 60-day retest to target them with appropriate measures to mitigate the IRs' risk. Sixteen percent (341 out of 2 177) of IR-only herd tests were identified as high-risk in the 2020 dataset, with 265 (78 %) of these having at least one reactor or IR at retest. Severe-level reinterpretation of the high-risk IR-only disclosing tests identified in this dataset would turn 68 out of the 590 (12 %) IRs into reactors, generating 23 incidents, the majority (19 or 83 %) part of the 265 incidents that would have been declared at the retest. Classification tree analysis used to identify IR-only high-risk tests in herds eligible for severe interpretation would enhance the sensitivity of the test-and-slaughter regime, cornerstone of the bTB eradication programme in England, further mitigating the risk of disease spread posed by IRs.

Combining Analytical Approaches and Multiple Sources of Information to Improve Interpretation of Diagnostic Test Results for Tuberculosis in Wild Meerkats.

Diagnostic tests are used to classify individual animals' infection statuses. However, validating test performance in wild animals without gold standard tests is extremely challenging, and the issue is further complicated in chronic conditions where measured immune parameters vary over time. Here, we demonstrate the value of combining evidence from different diagnostic approaches to aid interpretation in the absence of gold standards, large sample sizes, and controlled environments. Over a two-year period, we sampled 268 free-living meerkats (Suricata suricatta) longitudinally for Mycobacterium suricattae (a causative agent of tuberculosis), using three ante-mortem diagnostic tests based on mycobacterial culture, and antigen-specific humoral and cell-mediated immune responses, interpreting results both independently and in combination. Post-mortem cultures confirmed M. suricattae infection in 22 animals, which had prior ante-mortem information, 59% (13/22) of which were test-positive on a parallel test interpretation (PTI) of the three ante-mortem diagnostic assays (95% confidence interval: 37-79%). A similar ability to detect infection, 65.7% (95% credible interval: 42.7-84.7%), was estimated using a Bayesian approach to examine PTI. Strong evidence was found for a near doubling of the hazard of death (Hazard Ratio 1.75, CI: 1.14-2.67, p = 0.01), associated with a positive PTI result, thus demonstrating that these test results are related to disease outcomes. For individual tests, small sample sizes led to wide confidence intervals, but replication of conclusions, using different methods, increased our confidence in these results. This study demonstrates that combining multiple methodologies to evaluate diagnostic tests in free-ranging wildlife populations can be a useful approach for exploiting such valuable datasets.

A comparison of the value of two machine learning predictive models to support bovine tuberculosis disease control in England.

Nearly a decade into Defra's current eradication strategy, bovine tuberculosis (bTB) remains a serious animal health problem in England, with c.30,000 cattle slaughtered annually in the fight against this insidious disease. There is an urgent need to improve our understanding of bTB risk in order to enhance the current disease control policy. Machine learning approaches applied to big datasets offer a potential way to do this. Regularized regression and random forest machine learning methodologies were implemented using 2016 herd-level data to generate the best possible predictive models for a bTB incident in England and its three surveillance risk areas (High-risk area [HRA], Edge area [EA] and Low-risk area [LRA]). Their predictive performance was compared and the best models in each area were used to characterize herds according to risk. While all models provided excellent discrimination, random forest models achieved the highest balanced accuracy (i.e. average of sensitivity and specificity) in England, HRA and LRA, whereas the regularized regression LASSO model did so in the EA. The time since the last confirmed incident was resolved was the only variable in the top-ten ranking in all areas according to both types of models, which highlights the importance of bTB history as a predictor of a new incident. Risk categorisation based on Receiver Operating Characteristic (ROC) analysis was carried out using the best predictive models in each area setting a 99 % threshold value for sensitivity and specificity (97 % in the LRA). Thirteen percent of herds in the whole of England as well as in its HRA, 14 % in its EA and 31 % in its LRA were classified as high-risk. These could be selected for the deployment of additional disease control measures at national or area level. In this way, low-risk herds within the area considered would not be penalised unnecessarily by blanket control measures and limited resources be used more efficiently. The methodology presented in this paper demonstrates a way to accurately identify high-risk farms to inform a targeted disease control and prevention strategy in England that supplements existing population strategies.

Who infects whom? Social networks and tuberculosis transmission in wild meerkats.

Transmission of infectious diseases is strongly influenced by who contacts whom. Despite the global distribution of tuberculosis (TB) in free-living wild mammal populations, little is known of the mechanisms of social transmission of Mycobacterium bovis between individuals. Here, I use a network approach to examine for correlations between five distinct types of intra- and intergroup social interaction and changes in TB status of 110 wild meerkats (Suricata suricatta) in five social groups over two years. Contrary to predictions, the most socially interactive animals were not at highest risk of acquiring infection, indicating that in addition to contact frequency, the type and direction of interactions must be considered when quantifying disease risk. Within social groups, meerkats that groomed others most were more likely to become infected than individuals who received high levels of grooming. Conversely, receiving, but not initiating, aggression was associated with M. bovis infection. Incidence of intergroup roving by male meerkats was correlated with the rovers themselves subsequently testing TB-positive, suggesting a possible route for transmission of infection between social groups. Exposure time was less important than these social interactions in influencing TB risk. This study represents a novel application of social network analysis using empirical data to elucidate the role of specific interactions in the transmission of an infectious disease in a free-living wild animal population.

Risk factor analysis for "diagnosis not reached" results from bovine samples submitted to British veterinary laboratories in 2013-2017.

Routine diagnostic data from laboratories are an important source of information for passive animal health surveillance. In Great Britain, the Veterinary Investigation Diagnosis Analysis (VIDA) database includes records of diagnostic submissions made to a nationwide network of 28 veterinary post-mortem facilities (VPFs). Data on "diagnosis not reached" (DNR), i.e. where submissions do not lead to a confirmed diagnosis, are analysed quarterly to look for unexpectedly high incidences of DNRs which could indicate the presence of a new or emerging disease in British livestock populations. The objective of the present study was to provide a better understanding about the reasons of DNR occurrence and to inform improvements of the coverage and reporting of this kind of surveillance data. A subset of the VIDA database comprising diagnostic submissions from cattle received from 2013 to 2017 (122,444 records) was analysed. A mixed-effects multivariable logistic regression model, accounting for clustering by farm and county, was used to investigate associations between potential predictors and DNR. The variables included in the model were: VPF identity, animal sex, age, production purpose, main presenting sign of the animal from which the sample was obtained, and sample submission type. The variable that showed the strongest association with DNR was the main presenting sign of the animal, followed by submission type, VPF identity, animal age, sex, and production purpose, in that order. Submissions from animals with abortion as the main clinical sign had the highest odds ratio (OR 21.6, 95 % confidence interval [CI] 19.6-23.9, with mastitis taken as the baseline). Submissions where neither carcasses (i.e. a whole dead animal provided for post-mortem examination) nor foetuses (i.e. an unborn dead animal) were provided had approximately 12 times the odds of being DNR, compared to submissions of a carcass (OR 11.6, 95 % CI 10.7-12.5). In addition, submission type and main presenting sign can be considered as important confounders in the association between the other predictors and DNR. This study has helped characterise DNR occurrence and suggests some possible improvements that could be made to the passive surveillance system investigated, such as encouraging greater carcase submission, accounting for identified issues when interpreting increased occurrence of DNR and further investigating reduced submissions or greater DNR occurrence in some geographical regions.

Towards a unified generic framework to define and observe contacts between livestock and wildlife: a systematic review.

Wild animals are the source of many pathogens of livestock and humans. Concerns about the potential transmission of economically important and zoonotic diseases from wildlife have led to increased surveillance at the livestock-wildlife interface. Knowledge of the types, frequency and duration of contacts between livestock and wildlife is necessary to identify risk factors for disease transmission and to design possible mitigation strategies. Observing the behaviour of many wildlife species is challenging due to their cryptic nature and avoidance of humans, meaning there are relatively few studies in this area. Further, a consensus on the definition of what constitutes a 'contact' between wildlife and livestock is lacking. A systematic review was conducted to investigate which livestock-wildlife contacts have been studied and why, as well as the methods used to observe each species. Over 30,000 publications were screened, of which 122 fulfilled specific criteria for inclusion in the analysis. The majority of studies examined cattle contacts with badgers or with deer; studies involving wild pig contacts with cattle or with domestic pigs were the next most frequent. There was a range of observational methods including motion-activated cameras and global positioning system collars. As a result of the wide variation and lack of consensus in the definitions of direct and indirect contacts, we developed a unified framework to define livestock-wildlife contacts that is sufficiently flexible to be applied to most wildlife and livestock species for non-vector-borne diseases. We hope this framework will help standardise the collection and reporting of contact data; a valuable step towards being able to compare the efficacy of wildlife-livestock observation methods. In doing so, it may aid the development of better disease transmission models and improve the design and effectiveness of interventions to reduce or prevent disease transmission.

Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making.

Identifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Exploratory machine learning techniques can uncover complex non-linear relationships and interactions within disease causation webs, and enhance our knowledge of TB risk factors and how they are interrelated. Classification tree analysis was used to reveal associations between predictors of TB in England and each of the three surveillance risk areas (High Risk, Edge, and Low Risk) in 2016, identifying the highest risk herds. The main classifying predictor for farms in England overall related to the TB prevalence in the 100 nearest cattle herds. In the High Risk and Edge areas it was the number of slaughterhouse destinations and in the Low Risk area it was the number of cattle tested in surveillance tests. How long ago the last confirmed incident was resolved was the most frequent classifier in trees; if within two years, leading to the highest risk group of herds in the High Risk and Low Risk areas. At least two different slaughterhouse destinations led to the highest risk group of herds in England, whereas in the Edge area it was a combination of no contiguous low-risk neighbours (i.e. in a 1 km radius) and a minimum proportion of 6-23 month-old cattle in November. A threshold value of prevalence in 100 nearest neighbours increased the risk in all areas, although the value was specific to each area. Having low-risk contiguous neighbours reduced the risk in the Edge and High Risk areas, whereas high-risk ones increased the risk in England overall and in the Edge area specifically. The best classification tree models informed multivariable binomial logistic regression models in each area, adding statistical inference outputs. These two approaches showed similar predictive performance although there were some disparities regarding what constituted high-risk predictors. Decision tree machine learning approaches can identify risk factors from webs of causation: information which may then be used to inform decision making for disease control purposes.

Accuracy of three diagnostic tests for determining Mycobacterium bovis infection status in live-sampled wild meerkats (Suricata suricatta).

Accurate diagnosis of Mycobacterium bovis infection (bovine tuberculosis [bTB]) in live animals is notoriously problematic. The aim of the present study was to evaluate the diagnostic potential of 2 new serologic tests (multiantigen print immunoassay [MAPIA] and lateral flow immunoassay rapid test [RT]) in comparison with mycobacterial culture of tracheal washes for determining M. bovis infection status in a free-ranging population of wild meerkats (Suricata suricatta). During a longitudinal study lasting 2.5 years, 240 individually identifiable meerkats were each sampled up to 8 times under anesthesia every 3 months. Diagnostic accuracy was determined through Bayesian and maximum likelihood estimations of sensitivity, specificity, and likelihood ratios for each diagnostic test when used independently and in parallel to classify the disease status of individual meerkats in the absence of a gold standard. Culture of tracheal washes was highly specific (0.99; 95% confidence interval [CI] = 0.77, 1.00) but of low sensitivity (0.36; 95% CI = 0.24, 0.50) for diagnosing M. bovis-infected individuals. The longitudinal nature of the study with repeated sampling of the same individual animals served simultaneously to improve chances of detecting infection and increase confidence in a negative result in individual animals repeatedly testing negative. Although MAPIA and RT were individually of limited diagnostic use, interpreting the results of these 2 tests in parallel produced estimates of sensitivity (0.83; 95% CI = 0.67, 0.93) and specificity (0.73; 95% CI = 0.62, 0.82) high enough to usefully inform decision making when determining exposure to bTB in wild meerkats and potentially other species in which bTB poses a diagnostic challenge.