Cattle Movement Network Research Articles

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.

Caracterização da rede de trânsito de bovinos no estado de Goiás, Brasil

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.

Quantifying changes in the British cattle movement network

The modelling of disease spread is crucial to the farming industry and policy makers. In some of these industries, excellent data exist on animal movements, along with the networks that these movements create, and allow researchers to model spread of disease (both epidemic and endemic). The Cattle Tracing System is an online recording system for cattle births, deaths and between-herd movements in the United Kingdom and is an excellent resource for any researchers interested in networks or modelling infectious disease spread through the UK cattle system. Data exist that cover many years, and it can be useful to know how much change is occurring in a network, to help judge the merit of using historical data within a modelling context. This article uses the data to construct weighted directed monthly movement networks for two distinct periods of time, 2004–2006 and 2015–2017, to quantify by how much the underlying structure of the network has changed. Substantial changes in network structure may influence policy-makers directly or may influence models built upon the network data, and these in turn could impact policy-makers and their assessment of risk. We examined 13 network metrics, ranging from general descriptive metrics such as total number of nodes with movements and total movements, through to metrics to describe the network (e.g., Giant weakly and strongly connected components) and metrics calculated per node (betweenness, degree and strength). Mixed effect models show that there is a statistically significant effect of the period (2004–2006 vs 2015–2017) in the values of nine of the 13 network metrics. For example median total degree decreased by 19%. In addition to examining networks for two time periods, two updates of the data were examined to determine by how much the movement data stored for 2004–2006 had been cleansed between updates. Examination of these updates shows that there are small decreases in problem movements (such as animals leaving slaughterhouses) and therefore evidence of historical data being improved between updates. In combination with the significant effect of period on many of the network metrics, the modification of data between updates provides further evidence that the most recent available data should be used for network modelling. This will ensure that the most representative descriptions of the network are available to provide accurate modelling results to best inform policy makers.

Assessing potential routes of Streptococcus agalactiae transmission between dairy herds using national surveillance, animal movement data and molecular typing

Streptococcus agalactiae, also known as group B Streptococcus (GBS), is a pathogen of humans and animals. It is an important cause of mastitis in dairy cattle, causing decreased milk quality and quantity. Denmark is the only country to have implemented a national surveillance and control campaign for GBS in dairy cattle. After a significant decline in the 20th century, prevalence has increased in the 21st century. Using a unique combination of national surveillance, cattle movement data and molecular typing, we tested the hypothesis that transmission mechanisms differ between GBS strains that are almost exclusive to cattle and those that affect humans as well as cattle, which would have implications for control recommendations. Three types of S. agalactiae, sequence type (ST) 1, ST23 and ST103 were consistently the most frequent strains among isolates obtained through the national surveillance programme from 2009 to 2011. Herds infected with ST103, which is common in cattle but rarely found in people in Europe, were spatially clustered throughout the study period and across spatial scales. By contrast, strains that are also commonly found in humans, ST1 and ST23, showed no spatial clustering in most or any years of the study, respectively. Introduction of cattle from a positive herd was associated with increased risk of infection by S. agalactiae in the next year (risk ratio of 2.9 and 4.7 for 2009–2010 and 2010–2011, respectively). Moreover, mean exposure to infection was significantly higher for newly infected herds and significantly lower for persistently susceptible herds, as compared to random simulated networks with the same properties, which suggests strong association between the cattle movement network and new infections. At strain-level, new infections with ST1 between 2009 and 2010 were significantly associated with cattle movements, while other strains showed only some degree of association. Sharing of veterinary services, which may serve as proxy for local or regional contacts at a range of scales, was not significantly associated with increased risk of introduction of S. agalactiae or one of the three predominant strains on a farm. Our findings support the reinstatement of restrictions on cattle movements from S. agalactiae positive herds, which came into effect in 2018, but provide insufficient evidence to support strain-specific control recommendations.

Analysis of the cattle trade network in the state of Mato Grosso, Brazil

We characterized the network of cattle movements in Mato Grosso, Brazil—a state responsible for a significant amount of Brazilian beef exports. In this analysis, we were particularly interested in the importance of slaughterhouses in the network of cattle movements, in the characteristics of the trade for different purposes (especially for reproduction, fattening, and slaughter), and about the importance of the main traders. We found that the median distance of all movements was 79.0 km, with an interquartile range between 37.2 and 163.2 km, which suggests a relatively short-range trade pattern; roughly 20% of the premises purchasing more animals were responsible for 95% of the animals purchased and 20% of the premises selling more animals were responsible for 86% of the animals sold, indicating the importance of the cattle trade hubs. Fitting the degree distribution using a power-law distribution, we obtained a scaling parameter of 2.59, indicating that the cattle movement network of Mato Grosso is less dense than other observed cattle movement networks. The networks for fattening, reproduction, and slaughtering only comprised 73%, 56%, and 25% of all nodes and 52%, 30%, and 17% of all edges, respectively; and slaughterhouses had the highest in-degrees and total degrees, followed by markets and farms, playing the role of main cattle trade hubs. Therefore, slaughterhouses are potentially useful as surveillance points to detect cases, and controlling important trader premises may optimize the use of resources for controlling diseases.

Network analysis of dairy cattle movement and associations with bovine tuberculosis spread and control in emerging dairy belts of Ethiopia

BackgroundDairy cattle movement could be a major risk factor for the spread of bovine tuberculosis (BTB) in emerging dairy belts of Ethiopia. Dairy cattle may be moved between farms over long distances, and hence understanding the route and frequency of the movements is essential to establish the pattern of spread of BTB between farms, which could ultimately help to inform policy makers to design cost effective control strategies. The objective of this study was, therefore, to investigate the network structure of dairy cattle movement and its influence on the transmission and prevalence of BTB in three emerging areas among the Ethiopian dairy belts, namely the cities of Hawassa, Gondar and Mekelle.MethodsA questionnaire survey was conducted in 278 farms to collect data on the pattern of dairy cattle movement for the last 5 years (September 2013 to August 2018). Visualization of the network structure and analysis of the relationship between the network patterns and the prevalence of BTB in these regions were made using social network analysis.ResultsThe cattle movement network structure display both scale free and small world properties implying local clustering with fewer farms being highly connected, at higher risk of infection, with the potential to act as super spreaders of BTB if infected. Farms having a history of cattle movements onto the herds were more likely to be affected by BTB (OR: 2.2) compared to farms not having a link history. Euclidean distance between farms and the batch size of animals moved on were positively correlated with prevalence of BTB. On the other hand, farms having one or more outgoing cattle showed a decrease on the likelihood of BTB infection (OR = 0.57) compared to farms which maintained their cattle.ConclusionThis study showed that the patterns of cattle movement and size of animal moved between farms contributed to the potential for BTB transmission. The few farms with the bulk of transmission potential could be efficiently targeted by control measures aimed at reducing the spread of BTB. The network structure described can also provide the starting point to build and estimate dynamic transmission models for BTB, and other infectious diseases.

Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies.

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a 'hurdle model' approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic 'complete' networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of 'fast' ( R0 = 3) and 'slow' ( R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

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

Between-farm transmission of bovine tuberculosis (bTB) occurs mostly through fence-to-fence contact between neighbouring farms, endemic infected wildlife or movement of infected animals. Unfortunately, bTB detection is frequently delayed and identification of the source of introduction is often difficult, particularly in endemic regions. Here, we characterized the cattle movement network of Castilla y Leon, a high bTB-prevalence (1.9% at the farm level in 2015) region in Spain, over six years and analysed the distribution of bTB to ultimately assess the likelihood of spatial and movement-mediated transmission. We analysed movement and bTB data from 27,633 units located in the region, of which 87% were involved in ~1.4million movements of ~8.8million animals. Network-level connectivity was low, although a few highly connected units were identified. Up to 15% of the herds became bTB-positive at some point during the study, with the highest percentage found in bullfighting and beef herds. Although bTB-positive herds had a significantly higher degree and moved more cattle than negative herds. Results of the k-test, a permutation-based procedure, suggested that positive farms were not significantly clustered in the movement network. Location was a likely risk factor as bTB-positive farms tended to be located within 5km from each other. Results suggested that movements may be a source of bTB in cattle in Castilla y Leon, although local factors may be more influential in determining risk of disease at the farm level. The description of the movement network in Castilla y Leon may be valuable for bTB surveillance in Spain. Moreover, results are useful to assess the movement-associated risk for multiple diseases.

The implementation of cattle market closure strategies to mitigate the foot-and-mouth disease epidemics: A contact modeling approach

Foot-and-mouth disease (FMD) is one of the most endemic diseases in livestock worldwide. The disease occurrence generally results in a huge economic impact. The virus may distribute across countries or even continents along the contact network of animal movements. The present study, therefore, aimed to explore a cattle movement network originated in Tak, a Thailand-Myanmar bordered province and to demonstrate how FMDV spread among the nodes of market, source and destination. Subsequently, we examined the effectiveness of market closure intervention. The market-market (M-M) network was constructed to highlight the inter-market connections and the FMDV was modeled to spread along the trade chain. Four market closure scenarios based on rapidness and duration of implementation were examined. Our results indicate that two of the three major markets located in the province were highly connected and a strongly connected component was identified. The intra-provincial animal movements, which were currently overlooked, should be moved into sights as most of the high-risk sources for FMD epidemics were recognized in a close proximity to the cattle markets. Simultaneously, remote destinations across the country were identified. The inter-provincial animal movement control must be strengthened once FMD outbreak is notified. Based on our simulations, closing markets with low inter-market connectivity may not prevent the spread of FMDV. A selective market closure strategy targeting highly connected markets together with cattle trader tracking system was an alternative approach. However, socio-economic consequences regarding this intervention must be considered.

Livestock markets play an important role in the cattle movement network in Pernambuco, Brazil

O comércio de animais é um importante fator de risco para a disseminação de doenças entre animais e rebanhos. O objetivo do presente estudo foi caracterizar a rede de movimentação de bovinos em Pernambuco, com base nas guias de trânsito animal (GTAs) de 2012 a 2013, e identificar a intensidade da relação comercial entre os estabelecimentos. Foram analisadas 737.950 GTAs emitidas, e as movimentações de bovinos totalizaram 3.481.185 animais (1.688.585 em 2012 e 1.792.600 em 2013). Um percentual de 52,57% (1.829.907/3.481.185) das movimentações de bovinos envolveu entrada ou saída de feiras de gado, indicando que as feiras desempenharam um papel importante na rede. Aproximadamente 20% dos estabelecimentos mais conectados foram responsáveis por aproximadamente 87% das movimentações relacionadas às vendas, e 95% das movimentações, relacionadas às compras. Considerando o importante papel das feiras e o intenso comércio de bovinos entre as propriedades, medidas de vigilância e controle podem ser mais eficientes se aplicadas em feiras e estabelecimentos altamente conectados, para prevenir a propagação de doenças infecciosas.

Cattle movement network, herd size, and bovine brucellosis in the State of Mato Grosso, Brazil

The aims of the present study were to compare centrality and other measures of bovine movement networks in the state of Mato Grosso, Brazil, in 2007, by examination of the positive and negative herds identified in the brucellosis survey conducted in 2003. In addition, we aimed to examine the association between herd size (measured by the number of females older than 24 months, denoted FEM24+) and animal trade between herds. We found a statistical association between brucellosis occurrence and both the total degree (number of traded animals) and out-degree (for the number of animals sold and the number of neighboring herds). This finding suggests that positive herds in the 2003 survey were trading (particularly selling) more frequently in 2007 than negative herds, thereby presumably increasing the risk of disease spread. Statistical differences observed (p-values) in the network of movements for reproduction purposes were more significant than those observed in other networks for average herd size; degrees of animals, batches, and neighbors; outdegree of neighbors; and betweenness. We found positive associations (p < 0.001) between the following variables: number of traded animals and the FEM24+ herd size; FEM24+ herd size and FEM24+ herd size of neighbors within the network of cattle movement; FEM24+ herd size of neighbors and number of traded animals; and weighted degree (animals) of neighbors and degree (animals) of the herd of origin. A comparison of positive and negative herds stratified by herd size (? 10 FEM24+; 11-50 FEM24+; and > 50 FEM24+), within the network of movements for reproduction purposes, revealed significant differences (p < 0.05) in the > 50 FEM24+ category only, for average herd size, total degree (animals, batches, and neighbors), outdegree (animals, batches, and neighbors), and outcloseness. Logistic regression analysis, in which average herd size and degree (animals) were both factors, showed that a tenfold increase in herd size and the number of animals traded would lead to an increased chance of a herd being positive by 41% (OR = 1.41 [1.01; 1.99]) and 39% (OR = 1.39 [1.05; 1.85]), respectively. We concluded that the presence of bovine brucellosis is associated with a larger herd size, which is consistent with other reports. We also found an association between the increased trade of bovines and the presence of bovine brucellosis. Furthermore, an association was noted between herd size and animal trade, both of which could contribute to the spread of brucellosis.

The application of social network mining to cattle movement analysis: introducing the predictive trend mining framework

This paper describes a predictive social network mining framework which is demonstrated using the Great Britain cattle movement datasets. The proposed framework, the predictive trend mining framework (PTMF), is used to analyse episodes of time-stamped social network data. The PTMF has two main components (1) a frequent pattern trend analysis component that efficiently identifies temporal frequent patterns and trends and also provides a mechanism for clustering and analysing these patterns and trends so as to detect dynamic changes within the cattle movement network, and (2) the predictive modelling component for forecasting the percolation of information or data across the network. The PTMF incorporates a number of novel elements including mechanisms to: (1) identify temporal frequent patterns and trends, (2) cluster large sets of trends, (3) analyse temporal clusters for pattern trend change detection, (4) visualise these changes using pattern migration network maps and (5) predict the paths whereby information moves across the network over time.

Infectious disease surveillance in animal movement networks: An approach based on the friendship paradox

The network of animal movements among livestock premises is an important topological structure for the spread of infectious diseases. The central focus of this study was to analyze strategies for selecting premises based on the friendship paradox (“your friends have more friends than you do”) – in which premises that neighbor randomly selected premises are sampled for surveillance or control – to determine whether these strategies are viable alternatives for the surveillance and control of diseases in scenarios with insufficient data on animal movement. To test the effectiveness of these strategies, we performed three sets of simulations. In the first set, we examined the risk of spreading an infectious disease using the cattle movement network of the state of Mato Grosso, Brazil. All tested strategies based on the friendship paradox have comparable performance to the hub control strategy (controlling premises that sold more animals) and superior performance to random sampling in terms of both reducing the risk of purchasing infected animals and the number of premises that need to be controlled. In the second and third sets of simulations, we observed that the friendship paradox strategies were more sensitive than the random sampling strategy to detect cases and disease, respectively. The survey of the entire animal movement network to identify animal premises with a key role in trade is not always possible, either because the data are insufficient or because informal trade is significant. If surveying the network is not possible, all approaches based on knowledge of the network become useless. As an alternative, knowing that there is a hidden movement network that follows rules inherent to all networks, such as the friendship paradox, can be used to our advantage. Strategies based on the friendship paradox do not assume knowledge of the animal movement network and therefore may be viable alternatives for the surveillance or control of infectious diseases in the absence of network information, thus optimizing the use of human and financial resources.

Characteristics of the spatio-temporal network of cattle movements in France over a 5-year period

A good knowledge of the specificities of the animal trade network is highly valuable to better control pathogen spread on a large regional to transnational scale. Because of their temporal dynamical nature, studying multi-annual datasets is particularly needed to investigate whether structural patterns are stable over the years. In this study, we analysed the French cattle movement network from 2005 to 2009 for different spatial granularities and temporal windows, with the three-fold objective of exploring temporal variations of the main network characteristics, computing proxies for pathogen spread on this network, which accounts for its time-varying properties and identifying specificities related to the main types of animals and farms (dairy versus beef). Network properties did not qualitatively vary among different temporal and spatial granularities. About 40% of the holdings and 80% of the communes were directly interconnected. The width of the aggregation time window barely impacted normalised distributions of indicators. A period of 8–16 weeks would suffice for robust estimation of their main trends, whereas longer periods would provide more details on tails. The dynamic nature of the network could be seen through the small overlap between consecutive networks with 65% of common active nodes for only 3% of common links over 2005–2009. To control pathogen spread on such a network, by reducing the largest strongly connected component by more than 80%, movements should be prevented from 1 to 5% of the holdings with the highest centrality in the previous year network. The analysis of breed-wise and herd-wise subnetworks, dairy, beef and mixed, reveals similar trends in temporal variation of average indicators and their distributions. The link-based backbones of beef subnetworks seem to be more stable over time than those of other subnetworks. At a regional scale, node reachability accounting for time-respecting paths, as proxy of epidemic burden, is greater for a dairy region than for a beef region. This highlights the importance of considering local specificities and temporal dynamics of animal trade networks when evaluating control measures of pathogen spread.

Local cattle movements in response to ongoing bovine tuberculosis zonation and regulations in Michigan, USA

Bovine tuberculosis (Mycobacterium bovis) is an ongoing management issue in the state of Michigan with eradication from livestock as the ultimate goal. Eradication has been a challenge owing to the presence of a wildlife reservoir; competing interests in managing the livestock and wildlife hosts; and many uncertainties in transmission dynamics of M. bovis. One of the cornerstones of the eradication effort has been to stop movement of infected cattle among farms by imposing strict pre-movement testing on cattle being moved within, into and out of the Modified Accredited Zone (MAZ) in northeastern Michigan. In addition to pre-movement tuberculosis testing, detailed information about the origin and destination premises of all movements within the MAZ has been recorded in Michigan. The aim of this study was to describe the farm-to-farm movements of cattle within the MAZ, report changes in the network of movements during a 6-year period when the MAZ was a constant size (2004–2009), and examine changes in cattle movement patterns when the MAZ was reduced from 11 to 5 counties in 2010. Non-slaughter cattle movement within the MAZ was characterized by predominantly local movements at a sub-county scale. Premises that shipped cattle were primarily senders or receivers, but rarely both. From 2004 to 2009, the number of cattle shipped, size of shipments, number of shipments and distance of shipments decreased; there was little change in the network patterns of interaction among individual premises; and interactions among all premises became more disconnected. After accounting for MAZ size, there were also no changes in cattle movement network patterns following the reduction of the MAZ in 2010. The movement of cattle was likely not a key risk factor in bTB spread among premises in the MAZ during the study period and the effect of zonation and movement regulations appeared to further reduce the risk of tuberculosis spread via cattle movements among farms in Michigan's MAZ.

A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network

Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.