Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Cardenas NC

PROVIDER: S-EPMC7912437 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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

Cardenas Nicolas Cespedes NC Pozo Pilar P Lopes Francisco Paulo Nunes FPN Grisi-Filho José H H JHH Alvarez Julio J

Microorganisms 20210122 2

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 net ...[more]

PMID: 33499225

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Project description: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.

Project description:National border areas are special places for the spread of Mycobacterium tuberculosis (MTB). These regions concentrate vulnerable populations and constant population movements. Understanding the dynamics of the transmission of MTB is fundamental to propose control measures and to monitor drug resistance. We conducted a population-based prospective study of tuberculosis (TB) to evaluate molecular characteristics of MTB isolates circulating in Roraima, a state on the border of Venezuela and Guyana. Eighty isolates were genotyped by IS6110-RFLP (restriction fragment length polymorphism), spoligotyping, and 24-locus mycobacterial interspersed repetitive unit-variable number of repeats tandem (MIRU-VNTR). Drug susceptibility tests were performed by using the proportion method and GeneXpert® MTB/RIF (Cepheid, Sunnyvale, CA). Isolates showing a phenotypic resistance profile were submitted to polymerase chain reaction (PCR) and sequencing. Spoligotyping showed 40 distinct patterns with a high prevalence of Latin-American and Mediterranean (LAM), Haarlem (H), and the "ill-defined" T clades. Mycobacterial interspersed repetitive unit -VNTR and IS6110-RFLP showed clustering rates of 21.3% and 30%, respectively. Drug resistance was detected in 11 (15.1%) isolates, and all were found to have primary resistance; among these, six (8.2%) isolates were streptomycin mono-resistant, four (5.4%) isoniazid mono-resistant, and one (1.3%) multidrug resistant. This is the first study on the molecular epidemiology and drug resistance profile of MTB from Roraima. Herein, we describe high diversity of genetic profiles circulating in this region that may be driven by the introduction of new strain types because of large population flow in this region. In summary, our results showed that analyses of these circulating strains can contribute to a better understanding of TB epidemiology in the northern Brazilian border and be useful to establish public health policies on TB prevention.

Dataset Information

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

Publications

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

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