Bovine tuberculosis: within-herd transmission models to support and direct the decision-making process

Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

Inferring bovine tuberculosis transmission between cattle and badgers via the environment and risk mapping

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.

Impacts of timing, length, and intensity of behavioral interventions to COVID-19 dynamics: North Carolina county-level examples

We sought to examine how the impact of revocable behavioral interventions, e.g., shelter-in-place, varies throughout an epidemic, as well as the role that the proportion of susceptible individuals had on an intervention's impact. We estimated the theoretical impacts of start day, length, and intensity of interventions on disease transmission and illustrated them on COVID-19 dynamics in Wake County, North Carolina, to inform how interventions can be most effective. We used a Susceptible, Exposed, Infectious, and Recovered (SEIR) model to estimate epidemic curves with modifications to the disease transmission parameter (β). We designed modifications to simulate events likely to increase transmission (e.g., long weekends, holiday seasons) or behavioral interventions likely to decrease it (e.g., shelter-in-place, masking). We compared the resultant curves' shape, timing, and cumulative case count to baseline and across other modified curves. Interventions led to changes in COVID-19 dynamics, including moving the peak's location, height, and width. The proportion susceptible, at the start day, strongly influenced their impact. Early interventions shifted the curve, while interventions near the peak modified shape and case count. For some scenarios, in which the transmission parameter was decreased, the final cumulative count increased over baseline. We showed that the timing of revocable interventions has a strong impact on their effect. The same intervention applied at different time points, corresponding to different proportions of susceptibility, resulted in qualitatively differential effects. Accurate estimation of the proportion susceptible is critical for understanding an intervention's impact. The findings presented here provide evidence of the importance of estimating the proportion of the population that is susceptible when predicting the impact of behavioral infection control interventions. Greater emphasis should be placed on the estimation of this epidemic component in intervention design and decision-making. Our results are generic and are applicable to other infectious disease epidemics, as well as to future waves of the current COVID-19 epidemic. Developed into a publicly available tool that allows users to modify the parameters to estimate impacts of different interventions, these models could aid in evaluating behavioral intervention options prior to their use and in predicting case increases from specific events.

Spatial and temporal variation in proximity networks of commercial dairy cattle in Great Britain

The nature of contacts between hosts can be important in facilitating or impeding the spread of pathogens within a population. Networks constructed from contacts between hosts allow examination of how individual variation might influence the spread of infections. Studying the contact networks of livestock species managed under different conditions can additionally provide insight into their influence on these contact structures. We collected high-resolution proximity and GPS location data from nine groups of domestic cattle (mean group size = 85) in seven dairy herds employing a range of grazing and housing regimes. Networks were constructed from cattle contacts (defined by proximity) aggregated by different temporal windows (2 h, 24 h, and approximately 1 week) and by location within the farm. Networks of contacts aggregated over the whole study were highly saturated but dividing contacts by space and time revealed substantial variation in cattle interactions. Cows showed statistically significant variation in the frequency of their contacts and in the number of cows with which they were in contact. When cows were in buildings, compared to being on pasture, contact durations were longer and cows contacted more other cows. A small number of cows showed evidence of consistent relationships but the majority of cattle did not. In one group where management allowed free access to all farm areas, cows showed asynchronous space use and, while at pasture, contacted fewer other cows and showed substantially greater between-individual variation in contacts than other groups. We highlight the degree to which variations in management (e.g. grazing access, milking routine) substantially alter cattle contact patterns, with potentially major implications for infection transmission and social interactions. In particular, where individual cows have free choice of their environment, the resulting contact networks may have a less-risky structure that could reduce the likelihood of direct transmission of infections.

Use of Network Analysis and Spread Models to Target Control Actions for Bovine Tuberculosis in a State from Brazil

Livestock movements create complex dynamic interactions among premises that can be represented, interpreted, and used for epidemiological purposes. These movements are a very important part of the production chain but may also contribute to the spread of infectious diseases through the transfer of infected animals over large distances. Social network analysis (SNA) can be used to characterize cattle trade patterns and to identify highly connected premises that may act as hubs in the movement network, which could be subjected to targeted control measures in order to reduce the transmission of communicable diseases such as bovine tuberculosis (TB). Here, we analyzed data on cattle movement and slaughterhouse surveillance for detection of TB-like lesions (TLL) over the 2016-2018 period in the state of Rio Grande do Sul (RS) in Brazil with the following aims: (i) to characterize cattle trade describing the static full, yearly, and monthly snapshots of the network contact trade, (ii) to identify clusters in the space and contact networks of premises from which animals with TLL originated, and (iii) to evaluate the potential of targeted control actions to decrease TB spread in the cattle population of RS using a stochastic metapopulation disease transmission model that simulated within-farm and between-farm disease spread. We found heterogeneous densities of premises and animals in the study area. The analysis of the contact network revealed a highly connected (~94%) trade network, with strong temporal trends, especially for May and November. The TLL cases were significantly clustered in space and in the contact network, suggesting the potential for both local (e.g., fence-to-fence) and movement-mediated TB transmission. According to the disease spread model, removing the top 7% connected farms based on degree and betweenness could reduce the total number of infected farms over three years by >50%. In conclusion, the characterization of the cattle network suggests that highly connected farms may play a role in TB dissemination, although being close to infected farms was also identified as a risk factor for having animals with TLL. Surveillance and control actions based on degree and betweenness could be useful to break the transmission cycle between premises in RS.

Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations

Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-infected-whom’ with unprecedented accuracy. Successful methods include ‘phylodynamic approaches’ that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for ‘between-scale’ processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks. However, the method can be sensitive to assumptions about within-host evolution.

Mycoplasma bovis outbreak in New Zealand cattle: An assessment of transmission trends using surveillance data

Mycoplasma bovis most likely infected New Zealand cattle in the latter half of 2015. Infection was detected in mid-2017 after which control activities were implemented. An official eradication programme commenced in mid-2018, which is ongoing. We examined farm-level tracing and surveillance data to describe the outbreak, analyse transmission trends and make inference on progress towards eradication. Results indicate that cattle movements were the primary means of spread. Although case farms were distributed throughout both islands of New Zealand, most animal movements off infected farms did not result in newly infected farms, indicating Mycoplasma bovis is not highly transmissible between farms. To describe and analyse outbreak trends, we undertook a standard descriptive outbreak investigation, including construction of an epidemic curve and calculation of estimated dissemination ratios. We then employed three empirical models-a non-linear growth model, time series model and branching process model based on time-varying effective reproduction numbers-to further analyse transmission trends and provide short-term forecasts of farm-level incidence. Our analyses suggest that Mycoplasma bovis transmission in New Zealand has declined and progress towards eradication has been made. Few incident cases were forecast for the period between 8 September and 17 December 2019. To date, no case farms with an estimated infection date assigned to this period have been detected; however, case detection is ongoing, and these results need to be interpreted cautiously considering model validation and other important contextual information on performance of the eradication programme, such as the time between infection, detection and implementation of movement controls on case farms.

Epidemiological Study of Mycobacterium bovis Infection in Buffalo and Cattle in Amazonas, Brazil

Bovine Tuberculosis (BTB) is an endemic disease in about one hundred countries, affecting the economy causing a decrease in productivity, condemnation of meat, and damaging the credibility on international trade. Additionally, Mycobacterium bovis the major causative agent for BTB can also infect humans causing a variety of clinical presentations. The aim of this study was to determine BTB prevalence and the main risk factors for the Mycobacterium bovis prevalence in cattle and buffalos in Amazonas State, Brazil. Tissue samples from 151 animals (45 buffalo and 106 cattle from five herds with buffalo only, 22 herds with cattle only, and 12 herds with buffalo and cattle) were obtained from slaughterhouses under State Veterinary Inspection. M. bovis were isolated on Stonebrink medium. The positive cultures were confirmed by polymerase chain reaction (PCR) testing. The apparent herd and animal prevalence rates were 56.4 and 5.40%, respectively. Regarding animal species, the apparent prevalence rates were 3% in cattle and 11.8% in buffalo. Generalized Linear Mixed Models (GLMM) with random effect were used to assess the association with risk factors on the prevalence. Species (buffalo), herds size (>100 animals) and the presence of both species (buffalo and cattle) in the herd were the major risk factors for the infection by Mycobacterium bovis in the region. The findings reveal an urgent need for evidence-based effective intervention to reduce BTB prevalence in cattle and buffalo and prevent its spread to the human population. Studies are needed to understand why buffalo are more likely to be infected by M. bovis than cattle in Amazon. Recommendations for zoning, use of data from the inspection services to generate information regarding BTB focus, adoption of epidemiological tools, and discouragement of practices that promote the mixing of cattle and buffalo, were made.

Reviewing age-structured epidemiological models of cattle diseases tailored to support management decisions: Guidance for the future

Mechanistic simulation models are being increasingly used as tools to assist with animal health decision-making in the cattle sector. We reviewed scientific literature for studies reporting age-structured cattle management models in application to infectious diseases. Our emphasis was on papers dedicated to support decision making in the field. In this systematic review we considered 1290 manuscripts and identified 76 eligible studies. These are based on 52 individual models from 10 countries addressing 9 different pathogens. We provide an overview of these models and present in detail their theoretical foundations, design paradigms and incorporated processes. We propose a structure of the characteristics of cattle disease models using three main features: [1] biological processes, [2] farming-related processes and [3] pathogen-related processes. It would be of benefit if future cattle disease models were to follow this structure to facilitate science communication and to allow increased model transparency.

Evaluation of the effectiveness of the surveillance system for tuberculosis in cattle in Spain

In Spain, the eradication of tuberculosis (TB) in cattle has had some setbacks and therefore we need to re-evaluate the current surveillance strategies. The Spanish cattle TB surveillance system consists of three components: a) routine skin testing, b) slaughterhouse surveillance, and c) pre-movement controls. Our objectives were to assess the effectiveness of the whole surveillance system and evaluate the relative contribution of each of those components, both at national and at the province level. The effectiveness was estimated based on both the sensitivity of detection per year and the time until detection. Moreover, we evaluated the impact of different factors on that effectiveness. We used a stochastic model that simulated the spread of Mycobacterium tuberculosis-complex (MTC) infection within cattle herds that was modified to incorporate the three components of TB surveillance. Input data, at the province level, on cattle movements off-farm or the frequency of the routine screening were provided by the Spanish Ministry for Agriculture, Fisheries and Food. Under the current conditions, 96.1% of Spanish herds were detected within one year after their infection (i.e. mean sensitivity of TB surveillance), although that detection took on average 178 days. The surveillance system effectiveness was highly dependent on the routine skin testing, responsible for the detection of almost 90% of the infected herds, while slaughterhouse surveillance and pre-movement controls contributed only to the identification of a small proportion of infected herds. We observed substantial differences in the effectiveness of the surveillance components among Spanish provinces, although in general, the sensitivities were high. The most influential factor on the efficiency of TB detection in Spain was, by far, the frequency of routine controls, followed by the sensitivity of the test used. In a context of reduced funding for cattle TB eradication, the frequency of testing should be adapted based on risk-based surveillance strategies, i.e. efforts should focus on herds more likely to be infected, but also in herds more likely to infect other herds. While slaughterhouse surveillance seems a cost-effective strategy, the use of pre-movement testing at least in areas of low incidence should be further evaluated.

Modeling the Accuracy of Two in-vitro Bovine Tuberculosis Tests Using a Bayesian Approach

Accuracy of new or alternative diagnostic tests is typically estimated in relation to a well-standardized reference test referred to as a gold standard. However, for bovine tuberculosis (bTB), a chronic disease of cattle, affecting animal and public health, no reliable gold standard is available. In this context, latent-class models implemented using a Bayesian approach can help to assess the accuracy of diagnostic tests incorporating previous knowledge on test performance and disease prevalence. In Uruguay, bTB-prevalence has increased in the past decades partially because of the limited accuracy of the diagnostic strategy in place, based on intradermal testing (caudal fold test, CFT, for screening and comparative cervical test, CCT, for confirmation) and slaughter of reactors. Here, we evaluated the performance of two alternative bTB-diagnostic tools, the interferon-gamma assay, IGRA, and the enzyme-linked immunosorbent assay (ELISA), which had never been used in Uruguay in the absence of a gold standard. In order to do so animals from two heavily infected dairy herds and tested with CFT-CCT were also analyzed with the IGRA using two antigens (study 1) and the ELISA (study 2). The accuracy of the IGRA and ELISA was assessed fitting two latent-class models: a two test-one population model (LCA-a) based on the analysis of CFT/CFT-CCT test results and one in-vitro test (IGRA/ELISA), and a one test-one population model (LCA-b) using the IGRA or ELISA information in which the prevalence was modeled using information from the skin tests. Posterior estimates for model LCA-a suggested that IGRA was as sensitive (75-78%) as the CFT and more sensitive than the serial use of CFT-CCT. Its specificity (90-96%) was superior to the one for the CFT and equivalent to the use of CFT-CCT. Estimates from LCA-b models consistently yielded lower posterior Se estimates for the IGRA but similar results for its Sp. Estimates for the Se (52% 95%PPI:44.41-71.28) and the Sp (92% 95%PPI:78.63-98.76) of the ELISA were however similar regardless of the model used. These results suggest that the incorporation of IGRA for detection of bTB in highly infected herds could be a useful tool to improve the sensitivity of the bTB-control in Uruguay.

Assessment of the bovine tuberculosis elimination protocol in the United States

In this study, we analyzed the performance of the USDA's bovine tuberculosis (bTB) elimination protocol in a 1,000-cow closed dairy herd using an agent-based simulation model under different levels of initial bTB infection. We followed the bTB test sensitivity and specificity values used by the USDA in its model assessment. We estimated the net present value over a 20-yr horizon for a bTB-free milking herd and for bTB-infected herds following the USDA protocol. We estimated the expected time to identify the infection in the herd once it is introduced, its elimination time, the reproductive number (R₀), and effective reproduction number (R_e) under the USDA protocol. The optimal number of consecutive negative whole-herd tests (WHT) needed to declare a herd bTB-free with a 95% confidence under different bTB prevalence levels was derived. Our results support the minimum number of consecutive negative WHT required by the USDA protocol to declare a herd bTB-free; however, the number of consecutive negative WHT needed to eliminate bTB in a herd depends on the sensitivity and specificity of the tests. The robustness of the protocol was analyzed under conservative bTB test parameters from the literature. The cost of implementing the USDA protocol when 1 infected heifer is introduced in a 1,000-cow dairy herd is about $1,523,161. The average time until detection and the time required to eliminate bTB-infected animals from the herd, after 1 occult animal is introduced in the herd, were 735 and 119 d, respectively.

The spread of bovine tuberculosis in Canadian shared pastures: Data, model, and simulations

Bovine tuberculosis (bTB), caused by Mycobacterium bovis, is a chronic disease typical of cattle. Nonetheless, it can affect many mammals including humans, making it one of the most widespread zoonotic diseases worldwide. In industrialized countries, the main pathways of introduction of bTB into a herd are animal trade and contact with infected wildlife. In addition, for slow-spreading diseases with a long latent period such as bTB, shared seasonal pastures might be a between-herd transmission pathway, indeed farmers might unknowingly send infected animals to the pasture, since clinical signs are rarely evident in early infection. In this study, we developed a dynamic stochastic model to represent the spread of bTB in pastures. This was tailored to Canadian cow-calf herds, as we calibrated the model with data sourced from a recent bTB outbreak in Western Canada. We built a model for a herd with seasonal management, characterized by its partition into a group staying in the main facility and the remaining group(s) moving to summer pastures. We used this model to estimate the time of the first introduction of bTB into the herd. Furthermore, we expanded the model to include herds categorized as high-risk contacts with the index herd, in order to estimate the potential for disease spread on shared pastures. Finally, we explored two control scenarios to be applied to high-risk farms after the outbreak detection. Our results showed that the first introduction likely happened 3 to 5 years prior to the detection of the index herd, and the probability of bTB spreading in pastures was low, but not negligible. Nevertheless, the surveillance system currently in place was effective to detect potential outbreaks.

A new bovine tuberculosis model for England and Wales (BoTMEW) to simulate epidemiology, surveillance and control

BACKGROUND

Bovine tuberculosis (bTB) is a zoonotic disease of cattle caused by Mycobacterium bovis, widespread in England and Wales. It has high incidence towards the South West of England and Wales, with much lower incidence to the East and North. A stochastic simulation model was developed to simulate M. bovis transmission among cattle, transfer by cattle movements and transmission from environmental reservoirs (often wildlife and especially badgers). It distinguishes five surveillance streams, including herd tests, pre-movement testing and slaughter surveillance. The model thereby simulates interventions in bTB surveillance and control, and generates outputs directly comparable to detailed disease records. An anonymized version of the executable model with its input data has been released. The model was fitted to cattle bTB records for 2008-2010 in a cross-sectional comparison, and its projection was compared with records from 2010 to 2016 for validation.

RESULTS

The fitted model explained over 99% of the variation among numbers of breakdowns in four defined regions and surveillance streams in 2010. It classified 7800 (95% confidence interval c. 5500 - 14,000) holdings within high incidence regions as exposed to infectious environmental reservoirs, out of over 31,000 cattle holdings identified as potentially exposed to such sources. The model was consistent with previous estimates of low M. bovis transmission rate among cattle, but cattle to cattle transmission was clearly required to generate the number of cattle cases observed. When projected to 2016, the model as fitted to 2010 continued to match the distribution of bTB among counties, although it was notable that the actual distribution of bTB in 2010 was itself a close match for its distribution in 2016.

CONCLUSIONS

The close model fit demonstrated that cattle movements could generate breakdowns as observed in low incidence regions, if persistent environmental reservoirs such as wildlife maintained infection levels in the high incidence regions. The model suggests that environmental reservoirs may be a challenge for control, because, although many holdings are exposed to infection from wildlife or the environment, they are a minority of holdings. Large impacts on disease in wildlife will be required to avoid each individual transmission event to cattle.

Assessing the variability in transmission of bovine tuberculosis within Spanish cattle herds

In Spain, despite years of efforts to eradicate bovine tuberculosis (bTB), the disease is still endemic, with some areas of high prevalence. In this context, the surveillance and control plans may need to be re-evaluated, and understanding the dynamics of bTB spread within Spanish herds may help to develop new strategies for reducing the time for detection of infected herds and for the elimination of bTB from the herds already infected. Here, we developed a compartmental stochastic model to simulate bTB within-herd transmission, fed it with epidemiological data from 22 herds (obtained from a previous work) and carried out parameter inference using Approximate Bayesian Computing methods We also estimated the "Within-herd transmission potential Number" (R_h), i.e. the average number of secondary cases generated by a single animal infected introduced into a totally susceptible herd, considering different scenarios depending on the frequency of controls. The median global values obtained for the transmission parameters were: for the transmission coefficient (β), 0.014 newly infected animals per infectious individual per day (i.e. 5.2 per year), for the rate at which infected individuals become infectious (α), 0.01 per day (equivalent to a latent period of 97 days), and for the rate at which infected individuals become reactive to the skin test (α₁), 0.08 per day (equivalent to a period of 12 days for an infected animal to become reactive). However, the results also evidenced a great variability in the estimates of those parameters (in particular β and α) among the 22 herds. Considering a 6-month interval between tests, the mean R_h was 0.23, increasing to 0.82 with an interval of 1 year, and to 2.01 and 3.47 with testing intervals of 2 and 4 years, respectively.

Optimal surveillance strategies for bovine tuberculosis in a low-prevalence country

Bovine tuberculosis (bTB) is a chronic disease of cattle that is difficult to control and eradicate in part due to the costly nature of surveillance and poor sensitivity of diagnostic tests. Like many countries, bTB prevalence in Uruguay has gradually declined to low levels due to intensive surveillance and control efforts over the past decades. In low prevalence settings, broad-based surveillance strategies based on routine testing may not be the most cost-effective way for controlling between-farm bTB transmission, while targeted surveillance aimed at high-risk farms may be more efficient for this purpose. To investigate the efficacy of targeted surveillance, we developed an integrated within- and between-farm bTB transmission model utilizing data from Uruguay's comprehensive animal movement database. A genetic algorithm was used to fit uncertain parameter values, such as the animal-level sensitivity of skin testing and slaughter inspection, to observed bTB epidemiological data. Of ten alternative surveillance strategies evaluated, a strategy based on eliminating testing in low-risk farms resulted in a 40% reduction in sampling effort without increasing bTB incidence. These results can inform the design of more cost-effective surveillance programs to detect and control bTB in Uruguay and other countries with low bTB prevalence.

Evaluation of the Mycobacterium tuberculosis SO2 vaccine using a natural tuberculosis infection model in goats

The development of new vaccines against animal tuberculosis (TB) is a priority for improving the control and eradication of this disease, particularly in those species not subjected to compulsory eradication programmes. In this study, the protection conferred by the Mycobacterium tuberculosis SO₂ experimental vaccine was evaluated using a natural infection model in goats. Twenty-six goats were distributed in three groups: (1) 10 goats served as a control group; (2) six goats were subcutaneously vaccinated with BCG; and (3) 10 goats were subcutaneously vaccinated with SO₂. Four months after vaccination, all groups were merged with goats infected with Mycobacterium bovis or Mycobacterium caprae, and tested over a 40 week period using a tuberculin intradermal test and an interferon-γ assay for mycobacterial reactivity. The severity of lesions was determined at post-mortem examination and the bacterial load in tissues were evaluated by culture. The two vaccinated groups had significantly lower lesion and bacterial culture scores than the control group (P<0.05); at the end of the study, the SO₂ vaccinated goats had the lowest lesion and culture scores. These results suggest that the SO₂ vaccine provides some protection against TB infection acquired from natural exposure.

Transmission dynamics and elimination potential of zoonotic tuberculosis in morocco

Bovine tuberculosis (BTB) is an endemic zoonosis in Morocco caused by Mycobacterium bovis, which infects many domestic animals and is transmitted to humans through consumption of raw milk or from contact with infected animals. The prevalence of BTB in Moroccan cattle is estimated at 18%, and 33% at the individual and the herd level respectively, but the human M. bovis burden needs further clarification. The current control strategy based on test and slaughter should be improved through local context adaptation taking into account a suitable compensation in order to reduce BTB prevalence in Morocco and decrease the disease burden in humans and animals. We established a simple compartmental deterministic mathematical model for BTB transmission in cattle and humans to provide a general understanding of BTB, in particular regarding transmission to humans. Differential equations were used to model the different pathways between the compartments for cattle and humans. Scenarios of test and slaughter were simulated to determine the effects of varying the proportion of tested animals (p) on the time to elimination of BTB (individual animal prevalence of less than one in a thousand) in cattle and humans. The time to freedom from disease ranged from 75 years for p = 20% to 12 years for p = 100%. For p > 60% the time to elimination was less than 20 years. The cumulated cost was largely stable: for p values higher than 40%, cost ranged from 1.47 to 1.60 billion euros with a time frame of 12 to 32 years to reach freedom from disease. The model simulations also suggest that using a 2mm cut off instead of a 4mm cut off in the Single Intradermal Comparative Cervical Tuberculin skin test (SICCT) would result in cheaper and quicker elimination programs. This analysis informs Moroccan bovine tuberculosis control policy regarding time frame, range of cost and levels of intervention. However, further research is needed to clarify the national human-bovine tuberculosis ratio in Morocco.

A review of risk factors for bovine tuberculosis infection in cattle in the UK and Ireland

Bovine tuberculosis (bTB) is an important disease of cattle caused by infection withMycobacterium bovis, a pathogen that may be extremely difficult to eradicate in the presence of a true wildlife reservoir. Our objective was to identify and review relevant literature and provide a succinct summary of current knowledge of risk factors for transmission of infection of cattle. Search strings were developed to identify publications from electronic databases to February 2015. Abstracts of 4255 papers identified were reviewed by three reviewers to determine whether the entire article was likely to contain relevant information. Risk factors could be broadly grouped as follows: animal (including nutrition and genetics), herd (including bTB and testing history), environment, wildlife and social factors. Many risk factors are inter-related and study designs often do not enable differentiation between cause and consequence of infection. Despite differences in study design and location, some risk factors are consistently identified, e.g. herd size, bTB history, presence of infected wildlife, whereas the evidence for others is less consistent and coherent, e.g. nutrition, local cattle movements. We have identified knowledge gaps where further research may result in an improved understanding of bTB transmission dynamics. The application of targeted, multifactorial disease control regimens that address a range of risk factors simultaneously is likely to be a key to effective, evidence-informed control strategies.