Analysis of the cattle movement network and its association with the risk of bovine tuberculosis at the farm level in Castilla y Leon, Spain

An Overview of a Re-Emerging Disease in Italy: Bovine Tuberculosis Outbreaks in Cattle from MTBC-Free Territories

Bovine tuberculosis (bTB) is a zoonotic disease with consequences for public health as well as the economy. In the EU, compulsory eradication programmes have been applied, and most territories in Italy have been reported as disease-free (FTs). However, outbreaks (OBs), i.e., an officially confirmed occurrence of bTB in one or more animals in an establishment, have continued to be reported. In this study we provide an overview of bTB in terms of OB numbers in cattle from Italian FTs. Legislative sources were collected to find the FTs, the relevant declaration of free status year (FSY), and regional control and surveillance plans. Then, descriptive and statistical analyses were applied to the collected OBs. A total of 12 regions and 19 provinces were declared FTs in the 20 years from 2003 to 2023. Differences in regional plans were observed with respect to the percentages of herds that were annually controlled (control frequency). Overall, 370 OBs were recorded. A non-statistically significant decrease in the OB incidence rate after the FSY was declared. However, a notable increase in OBs detected at slaughterhouses after the FSY suggests that control systems (serological tests) at the herd level are not completely effective. Differences in the herds' control frequencies among FTs seem to not have had a significant influence on the observed OB number. The Tuscany region was the most affected FT based on the OB numbers after the FSY (especially in the last year). Epidemiologically relevant primary determinants seem to be the farming system (semi-extensive and adjacent herds) and the cattle movements from positive incidence areas (trade and animal fairs). The role of wild boars in the disease maintenance cannot be excluded. The results of this study stress the need to revise bTB eradication and surveillance plans based on risk analysis.

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.

Genomic epidemiology sheds light on the emergence and spread of Mycobacterium bovis Eu2 Clonal Complex in Portugal

ABSTRACTAnimal tuberculosis (TB) remains a serious concern for animal and human health. Mycobacterium bovis circulates in multi-host systems, dominated by the European 2 clonal complex (Eu2) in Iberia. In this work, we use genomic epidemiology to infer the emergence, spread, and spatiotemporal patterns of Eu2 in the official epidemiological risk area of animal TB in Portugal. Phylogenetic analysis of 144 M. bovis whole-genome sequences from cattle, wild boar, and red deer, representing the 2002-2021 period, distinguished three Eu2 clades that evolved independently. The major Eu2 clade underwent phylodynamic inferences to estimate the time and location of outbreaks, host transitions, and spatial diffusion as well. The origin of this Eu2 clade was attributed to the red deer population in the Castelo Branco district, near the border with Spain. Most host transitions were intraspecific (80%), while interspecific transmissions between wildlife species (wild boar-red deer), and between wild boar and cattle, were highly supported. Phylogeographic reconstruction evidenced that most transitions (82%) occur within municipalities, highlighting local transmission corridors.Our study indicates that M. bovis continues to spread at the cattle-wildlife interface within the animal TB hotspot area, possibly driven by the foraging behaviour of wild boar near agricultural lands. Red deer seems to be an important driver of TB within wildlife hosts, while the wild boar links the multi-host wildlife community and livestock. This work highlights the value of combining genomic epidemiology with phylodynamic inference to resolve host jumps and spatial patterns of M. bovis, providing real-time clues about points of intervention.

Large scale spatio-temporal modelling of risk factors associated with tuberculosis exposure at the wildlife-livestock interface

The management of animal tuberculosis (TB) is a priority for European Union animal health authorities. However, and despite all the efforts made to date, a significant part of Spain has as yet been unable to obtain the officially tuberculosis-free (OTF) status. Information regarding wildlife disease status is usually scarce, signifying that the role played by wildlife is usually ignored or poorly assessed in large-scale TB risk factor studies. The National Wildlife Health Surveillance Plan in Spain now provides information on infection rates in wildlife reservoirs at a national level, but there are limitations as regards the sample size, the spatio-temporal distribution of the samples, and the lack of homogeneity of the diagnostic techniques employed. The objective of the study described herein was, therefore, to employ a Bayesian approach with the intention of identifying the risk factors associated with four TB rates in cattle: prevalence, incidence, maintenance and persistence in Spain during the period 2014-2019. The modeling approach included highly informative spatio-temporal latent effects with which to control the limitations of the data. Variation partitioning procedures were carried out, and the pure effect of each factor was mapped in order to identify the most relevant factors associated with TB dynamics in cattle in each region. This made it possible to disclose that the movement of cattle, particularly from counties with herd incidence > 1%, was the main driver of the TB dynamics in cattle. The abundance of herds bred for bullfighting was retained in all four models, but had less weight than the movements. After accounting for farm-related factors, the TB prevalence in wild boar was retained in all the models and was significantly related to incidence, maintenance and persistence. With regard to the incidence, variation partitioning revealed that wildlife was the most explicative factor, thus suggesting that it plays a role in the introduction of the pathogen into uninfected herds, and consequently highlighting its importance in breakdowns. These results show, for the first time on a national scale, that wild ungulates play a relevant role in the spatio-temporal variability of TB in cattle, particularly as regards their disease status. Moreover, the spatial representation of the pure effect of each factor made it possible to identify which factors are driving the disease dynamics in each region, thus showing that it is a valuable tool with which to focus efforts towards achieving the OTF status.

Bovine tuberculosis in Spain, is it really the final countdown?

Bovine tuberculosis (bTB) is a severe zoonotic disease that has major impacts on both health and the economy, and which has been subjected to specific eradication programmes in many countries for decades. This manuscript highlights the relevance of this disease in the context of the European Union (EU) and summarizes the epidemiological situation and the main tools (e.g. antemortem diagnostic tests, slaughterhouse surveillance, laboratories, comprehensive databases, etc.) used to control and eradicate bTB in the various EU countries with a focus on the situation in Spain. A comprehensive description of the specific bTB epidemiological situation in Spain is provided, together with an assessment of the evolution of different epidemiological indicators throughout the last decades. Moreover, the main features of the Spanish bTB eradication programme and its control tools are described, along with the studies carried out in Spain that have allowed the updating of and improvement to the programme over the years with the aim of eradication, which has been established for 2030.

Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Animal movements represent a major risk for the spread of infectious diseases in the domestic swine population. In this study, we adopted methods from social network analysis to explore pig trades in Austria. We used a dataset of daily records of swine movements covering the period 2015-2021. We analyzed the topology of the network and its structural changes over time, including seasonal and long-term variations in the pig production activities. Finally, we studied the temporal dynamics of the network community structure. Our findings show that the Austrian pig production was dominated by small-sized farms while spatial farm density was heterogeneous. The network exhibited a scale-free topology but was very sparse, suggesting a moderate impact of infectious disease outbreaks. However, two regions (Upper Austria and Styria) may present a higher structural vulnerability. The network also showed very high assortativity between holdings from the same federal state. Dynamic community detection revealed a stable behavior of the clusters. Yet trade communities did not correspond to sub-national administrative divisions and may be an alternative zoning approach to managing infectious diseases. Knowledge about the topology, contact patterns, and temporal dynamics of the pig trade network can support optimized risk-based disease control and surveillance strategies.

Once bitten twice shy: Risk factors associated with bovine tuberculosis recurrence in Castilla y Leon, Spain

Persistence of bovine tuberculosis (bTB) in cattle herd remains a major challenge in disease elimination due to the ineffectual removal of all infected animals in a bTB breakdown. Characterization of herds with a higher probability of experiencing further bTB breakdowns can help to implement specific risk-based policies for disease control and eradication. Here, our aim was to identify herd- and breakdown-level risk factors in bTB infected herds in Castilla y Leon, Spain, associated with a decreased time to recurrence and an increased risk of recurrence using a mixed effects Cox proportional hazards model and a multivariable logistic regression model, respectively. Results revealed that location (province), herd size and number of incoming animals/contacts were good predictors of a decreased time to bTB recurrence and an increased risk of becoming a recurrent herd. Additionally, the duration of the previous outbreak and the number of IFN-γ herd-tests applied in it were associated with increased odds of (an early) recurrence. Risk factors identified here can be used for early identification of herds in which bTB eradication may be more challenging and that should thus be subjected to increased control efforts. The characterization of high-risk herds may help to minimize the risk of reinfection and emphasize early detection and removal of bTB positive animals in the herd.

Can Ingoing Contact Chains and other cattle movement network metrics help predict herd-level bovine tuberculosis in Irish cattle herds?

We used logistic regression to investigate whether the risk of an Irish cattle herd undergoing a bovine tuberculosis (bTB) breakdown increased with the size of the Ingoing Contact Chain (ICC) of previous herd to herd cattle movements, in a sequence up to eight moves back from the most recent, direct, movement into the herd. We further examined whether taking into account the bTB test history of each herd in the chain would improve model fit. We found that measures of cattle movements directly into the herd were risk factors for subsequent bTB restrictions, and the number of herds that animals were coming from was the most important of these. However, in contrast to a previous study in Great Britain, the ICC herd count at steps more remote than direct movements into the herd did not result in better fitting models than restricting the count to direct movements. Restricting the ICC counts to herds which had previously or would in the future test positive for bTB resulted in improved model fits, but this was not the case if only the previous test status was considered. This suggests that in many cases bTB infected animals are moving out of herds before being identified through testing, and that risk-based trading approaches should not rely solely on the previous test history of source herds as a proxy for future risk. Model fit was also improved by the inclusion of variables measuring bTB history of the herd, bTB in neighbouring herds, herd size, herd type, the movement network measures "in strength" and "betweenness", altitude, modelled badger abundance and county. Rainfall was not a good predictor. The most influential measures of bTB in nearby herds (a proxy for neighbourhood infection) were the proportion of herds with a history of bTB whose centroids were within 6 km, or whose boundaries were within 4 km, of the index herd. As well as informing national control and surveillance measures, our models can be used to identify areas where bTB rates are anomalously high, to prompt further investigation in these areas.

Evidence, Challenges, and Knowledge Gaps Regarding Latent Tuberculosis in Animals

Mycobacterium bovis and other Mycobacterium tuberculosis complex (MTBC) pathogens that cause domestic animal and wildlife tuberculosis have received considerably less attention than M. tuberculosis, the primary cause of human tuberculosis (TB). Human TB studies have shown that different stages of infection can exist, driven by host-pathogen interactions. This results in the emergence of heterogeneous subpopulations of mycobacteria in different phenotypic states, which range from actively replicating (AR) cells to viable but slowly or non-replicating (VBNR), viable but non-culturable (VBNC), and dormant mycobacteria. The VBNR, VBNC, and dormant subpopulations are believed to underlie latent tuberculosis (LTB) in humans; however, it is unclear if a similar phenomenon could be happening in animals. This review discusses the evidence, challenges, and knowledge gaps regarding LTB in animals, and possible host-pathogen differences in the MTBC strains M. tuberculosis and M. bovis during infection. We further consider models that might be adapted from human TB research to investigate how the different phenotypic states of bacteria could influence TB stages in animals. In addition, we explore potential host biomarkers and mycobacterial changes in the DosR regulon, transcriptional sigma factors, and resuscitation-promoting factors that may influence the development of LTB.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.

Contact network epidemiology has been extensively used in the context of infectious diseases, primarily focusing on epidemic diseases. In this paper we use detailed recorded data about cattle exchange between farms in Turkey from 2007 to 2012, to build, analyze and characterize the directed-weighted complex network of shipments of cattle. Additionally, using outbreaks data about recorded cases of foot-and-mouth disease (FMD) in Turkey, we assess the correlation between the “farm’s” position in the network (importance) and the risk of being infected with FMD, which has been endemic in Turkey for a long time. We find some network measures that are more likely to identify high-risk and low-risk farms (in-degree and in-coreness, respectively) when proposing strategies for surveillance or containment of an infectious disease.

Characterization of the cattle movement network in the state of Goiás, Brazil

ABSTRACT The present study characterized the cattle movement network in the state of Goiás, Brazil, in the period from 2010 to 2016, by identifying the areas of greatest flow and the purposes for which these animals were moved. For this, an analysis of the data referring to 4,697,239 animal transit guides (GTA), specifically for bovines, issued between the years 2010 to 2016 was carried out. Data such as the number of animals transported, origin and destination, as well as purpose of movement (slaughter, finishing, reproduction, post-weaning, auctions, sports, exhibitions, exports, and others) were evaluated. The data were subjected to descriptive analysis, in which the animal movements were divided into quartiles. There was an intense movement of cattle throughout the state, mainly for the purposes of finishing and slaughter. Identifying the areas with the highest flow of animals, such as the northwest and south regions, is an important tool for planning actions and applying preventive measures against the spread of infectious agents through the territory of Goiás.

Characterization and influencing factors of the pig movement network in Hunan Province, China

In terms of pig production in China, Hunan was the third largest province where the number of hogs accounted for 9.0 % of the national number of hogs in 2017. To propose the precise strategy for supervision of pig movements in Hunan Province, a weighted directed one-mode network was constructed using the data from the electronic animal health certificate platform in 2017. The nodes were designed as districts in Hunan and edges as flows of pig movement between districts. Social network analysis was used to analyse network characteristics and generalized linear models were performed to ascertain the socioeconomic factors that affect the pig movement network. During 2017, the pig movement network within the Hunan Province was composed of 122 nodes and 8562 directed connections, with a total of 510,973 shipments and 17,815,040 pigs moved. The network displayed a small-world topology, which had a higher clustering coefficient (0.4 vs. 0.1) and shorter average shortest path length (1.8 vs. 3.7) compared with equivalent random networks. The degree centrality positively correlated with closeness centrality (r = 0.99, P < 0.001) as well as betweenness centrality (r = 0.91, P < 0.001). After restricting the cross-regional pig movements in areas with the top 10 % of degree centrality, the number of pigs was reduced by nearly 50 % in the network, whereas the number of pigs was reduced by 94.0 % when movement restrictions were implemented in areas with the top 50 % of degree centrality. Observed network metrics showed an upward trend during the months of 2017, peaking in November and December. Generalized linear models showed that the size of the human population and per capita gross domestic product were the most important socioeconomic drivers of pig movements. The pig movement network in Hunan Province is a small-world network in which the introduction and spread of diseases may be quicker. More human, material, and financial resources should be allocated to areas with higher centrality. Swine movements were seasonal, and the inspection and quarantine work should be reinforced in the fourth quarter, especially in November and December. Pig movements were more active in areas with larger populations and advanced economy, and stricter supervision in these areas should be implemented. Our findings contribute to understanding the movement of pigs and the associated influencing factors in a big pig producing province in China, and the supervision strategies proposed in this study can be extended to other regions in China if proved to be viable.

Spoligotype-specific risk of finding lesions in tissues from cattle infected by Mycobacterium bovis

Background

Although the pathogenic effect of members of the Mycobacterium tuberculosis complex in susceptible hosts is well known, differences in clinical signs and pathological findings observed in infected animals have been reported, likely due to a combination of host and pathogen-related factors. Here, we investigated whether Mycobacterium bovis strains belonging to different spoligotypes were associated with a higher risk of occurrence of visible/more severe lesions in target organs (lungs and/or lymph nodes) from infected animals. A large collection of 8889 samples belonging to cattle were classified depending on the presence/absence of tuberculosis-like lesions and its degree of severity. All samples were subjected to culture irrespective of the presence of lesions, and isolates retrieved were identified and subjected to spoligotyping. The association between the presence/severity of the lesions and the isolation of strains from a given spoligotype was assessed using non-parametric tests and Bayesian mixed multivariable logistic regression models that accounted for origin (region and herd) effects.

Results

Results suggested a difference in severity in lesioned samples depending on the strain’s spoligotype. An association between specific spoligotypes and presence of lesions was observed, with a higher risk of finding lesions in animals infected with strains with spoligotypes SB0120, SB0295 and SB1142 compared with SB0121, and in those coming from certain regions in Spain.

Conclusions

Our results suggest that strains belonging to certain spoligotypes may be associated with a higher probability in the occurrence of gross/macroscopic lesions in infected cattle, although these observational findings should be confirmed in further studies that allow accounting for the effect of other possible confounders not considered here, and ultimately through experimental studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-02848-3.

Evaluation of the performance of slaughterhouse surveillance for bovine tuberculosis detection in Castilla y Leon, Spain

Post-mortem inspection (PMI) of routinely slaughtered cattle in abattoirs is an extremely valuable tool for detecting bovine tuberculosis (bTB) infected herds that can supplement active surveillance activities. However, its true performance is difficult to assess due to the multiple factors that may affect it. Here, we determined relative efficiencies in the detection of bTB-compatible lesions and probabilities of subsequent laboratory confirmation of abattoirs located in Castilla y Leon, one of the regions with the largest cattle population in Spain, between 2010 and 2017. The slaughtered animal population was split based on the results of the ante-mortem tests (reactors or non-reactors), and two generalized linear multivariable mixed models were fitted to each subpopulation to calculate the risk of lesion detection and laboratory confirmation per abattoir while accounting for the effect of potential confounding variables. Throughout the 8-year period, ∼30,000 reactors and >2.8 million non-reactor animals in the ante-mortem tests were culled in the abattoirs under study. Bovine TB compatible lesions were detected in 4,710 (16%) reactors and 828 (0.03%) non-reactor animals, of which >95% were confirmed as infected through bacteriology. The probability of disclosure of bTB-like lesions was associated with the animal subpopulation, type of source unit, the herd size, the year of slaughter, the breed and age of the animal, and/or the season of slaughter. The probabilities of detection of bTB-like lesions varied largely depending on the abattoir in both subpopulations, ranging from 603 to 3,070 per 10,000 animals for the reactors and 0.2-16.1 per 10,000 animals for the non-reactor animals. Results obtained here will help to quantify the performance of PMI in abattoirs in Castilla y Leon and the between-abattoir variability, and to identify animals at increased risk of having bTB-like lesions detected during PMI based on animal- and farm-related factors.

Dynamic network connectivity influences the spread of a sub-lineage of porcine reproductive and respiratory syndrome virus

Swine production in the United States is characterized by dynamic farm contacts through animal movements; such movements shape the risk of disease occurrence on farms. Pig movements have been linked to the spread of a virulent porcine reproductive and respiratory syndrome virus (PRRSV), RFLP type 1-7-4, herein denoted as phylogenetic sub-lineage 1A [L1A]. This study aimed to quantify the contribution of pig movements to the risk of L1A occurrence on farms in the United States. Farms were defined as L1A-positive in a given 6-month period if at least one L1A sequence was recovered from the farm. Temporal network autocorrelation modelling was performed using data on animal movements and 1,761 PRRSV ORF5 sequences linked to 494 farms from a dense pig production area in the United States between 2014 and 2017. A farm's current and past exposure to L1A and other PRRSV variants was assessed through its primary and secondary contacts in the animal movement network. Primary and secondary contacts with an L1A-positive farm increased the likelihood of L1A occurrence on a farm by 19% (p = .04) and 23% (p = .03), respectively. While the risk posed by primary contacts with PRRS-positive farms is unsurprising, the observation that secondary contacts also increase the likelihood of infection is novel. Risk of L1A occurrence on a farm also increased by 3.0% (p = .01) for every additional outgoing shipment, possibly due to biosecurity breaches during loading and transporting pigs from the farm. Finally, use of vaccines or field virus inoculation on sow farms one year prior reduced the risk of L1A occurrence in downstream farms by 36% (p = .04), suggesting that control measures that reduce viral circulation and enhance immunological protection in sow farms have a carry-over effect on L1A occurrence in downstream farms. Therefore, coordinated disease management interventions between farms connected via animal movements may be more effective than individual farm-based interventions.

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.

Evaluation of Risk Factors Associated With Herds With an Increased Duration of Bovine Tuberculosis Breakdowns in Castilla y Leon, Spain (2010-2017)

The persistence of bovine tuberculosis (bTB) in certain cattle herds is a major concern in countries pursuing disease eradication worldwide. The chronic nature of the disease, the lack of performance of diagnostic tools, and the presence of wildlife reservoirs may lead infected herds to require longer periods to achieve the officially tuberculosis-free (OTF) status. Here, we evaluated the impact of farm and breakdown characteristics on the probability of disease persistence in infected farms in Castilla y Leon, a bTB-endemic region of Spain, using survival and logistic regression models. Data from bTB breakdowns occurring in 3,550 bTB-positive herds detected in 2010–2017 were analyzed. A multivariable Cox proportional hazards model was fitted using time to recover OTF status as the response variable, and a multivariable logistic regression model using the chronic status (yes/no) for herds experiencing particularly long breakdowns as the outcome variable was also used. Both analyses revealed that county-level bTB herd prevalence, herd size, number of incoming animals in the previous 3 years, number of skin test reactors in the disclosing test, and number of days between the disclosing and follow-up tests were associated with increased breakdown duration. Production type was not consistently associated with chronic infection, suggesting that once infected, it is not a significant predictor of outbreak duration beyond the initial stages of the breakdown. Province-level location and number of animals that are bacteriology-positive also affected significantly the expected herd breakdown duration, but their effect became less significant over time. Risk factors identified in this study may help to identify herds more prone to suffer chronic bTB infection that may require additional control measures early on in a breakdown.

Implications of farmed-cervid movements on the transmission of chronic wasting disease

Chronic wasting disease is a transmissible spongiform encephalopathy that affects cervids with a clinical picture of muscle wasting in infected animals. The objective of this study was to quantify movement patterns of farmed cervids in the state of Minnesota as a model for identifying potential disease mitigation points. Time aggregated network analysis was performed on data consisting of 1221 intra-state cervid movements from farms located within Minnesota (n = 432 farms). Intra-state movements accounted for 48.2 % of all documented movements (2578) in Minnesota from 2013 to 2018; the remaining movements were inter-state. Annual networks were sparse in nature with low graph densities (6.9 × 10^-4 - 1.4 × 10^-3) and transitivity (0.06-0.12). Frequency of movements increased significantly (p < 0.05) in the months of September and October before decreasing in November, which coincided with the breeding and hunting seasons. Some of these contacts were as far as 500 km apart. The median length of infection chains for CWD positive farms was estimated to be 5.0 and 6.0 farms in-and out-going infection chains, respectively. A k-test analysis demonstrated that the observed median number of infected farms directly connected to other infected farms was 2.0, which was significantly higher than a fortuitous event (p = 0.002). Movements of cervids between farms were largely unpredictable with very low edge overlap (mean 0.02 %) from year to year, suggesting that persistent commercial relationships among farms were rare. In conclusion, long distance trade movements present a risk for spread of chronic wasting disease in Minnesota. The sparse networks and unpredictable farm contacts could be because cervid production is not as vertically integrated as other species-differentiated and established industries, such as swine or poultry. Our analytical approach can be used to understand chronic wasting disease in other states in the U.S. and North America in general.

Assessment of the sensitivity of the bovine tuberculosis eradication program in a high prevalence region of Spain using scenario tree modeling

In spite of the efforts invested to eradicate bovine tuberculosis (bTB) in cattle, the disease is still present in several developed countries, including Spain. Among the factors related with disease persistence in high prevalence areas, the lack of sensitivity of the screening test for detection of infected herds [single intradermal tuberculin (SIT) test] can play a major role. Here, a scenario tree model mimicking the diagnostic test scheme in place in the region of Castilla y Leon (Spain) was developed to estimate the probability of detecting bTB in an infected-non detected officially tuberculosis free (OTF) herd (herd sensitivity, HSe). In order to do so the probability of detecting at least one positive animal in the SIT test with/without post-mortem (detection of lesions and culture) confirmation in an infected herd was estimated using Monte Carlo simulation through @RISK (Palisade Co, NY, USA). Uncertainty on the accuracy of the diagnostic tests was introduced in the model using distributions based on the literature. The performance of the model was evaluated by comparing the predicted number of SIT/post-mortem positive animals in infected herds with those observed in newly detected bTB-infected herds in the region in 2011-2015. The estimated HSe of the SIT test was 76.2% (95% probability interval: 19.8-97.6). According to the model, bTB infection would be then confirmed through culture in 65.3% (95% PI: 50.0-82.3) of the herds detected through the SIT test, so that overall the proportion of infected-non detected OTF herds in which the infection could be confirmed after the initial SIT test was 49.6% (95% PI: 9.75-80.3). The predicted HSe of both SIT test and culture was directly correlated with herd size. Results from the model suggest a moderate but highly variable HSe of the current surveillance system in place for bTB detection in OTF herds located in high prevalence areas, that could be maximized by performing multiple tests within a year as indicated in the Spanish eradication program (with a median SIT HSe of 87% when two consecutive tests were considered). In addition, these results highlight the usefulness of performing subsequent SIT tests to rule out infection in SIT-positive herds even when the causative agent cannot be isolated.