Modelling the effect of test-and-slaughter strategies to control bovine tuberculosis in endemic high prevalence herds.

Bovine tuberculosis (bTB) prevalence substantially increased over the past two decades with relatively high impact on large dairy herds, raising the concern of regulatory authorities and industry stakeholders, and threatening animal and public health. Lack of resources, together with the economic and social consequences of whole-herd stamping-out, makes depopulation an impractical disease control alternative in these herds. The increase in bTB prevalence was associated with demographic and management changes in the dairy industry in Uruguay, reducing the efficacy of the current control programme (i.e. status quo) based on intradermal serial testing with caudal fold- and comparative-cervical tuberculin test-and-slaughter of reactors (CFT-CCT). Here, we aimed to assess the epidemiological effectiveness of six alternative control scenarios based on test-and-slaughter of positive animals, using mathematical modelling to infer bTB-within-herd dynamics. We simulated six alternative control strategies consisting of testing adult cattle (>1 year) in the herd every 3 months using one test (in vivo or in vitro) or a combination in parallel of two tests (CFT, interferon-gamma release assay-IGRA- or enzyme-linked immunosorbent assay). Results showed no significant differences overall in the time needed to reach bTB eradication (median ranging between 61 and 82 months) or official bovine tuberculosis-free status (two consecutive negative herd tests) between any of the alternative strategies and the status quo (median ranging between 50 and 59 months). However, we demonstrate how alternative strategies can significantly reduce bTB prevalence when applied for restricted periods (6, 12 or 24 months), and in the case of IGRAc (IGRA using peptide-cocktail antigens), without incurring on higher unnecessary slaughter of animals (false positives) than the status quo in the first 6 months of the programme (p-value < .05). Enhanced understanding bTB-within-herd dynamics with the application of different control strategies help to identify optimal strategies to ultimately improve bTB control and bTB eradication from dairies in Uruguay and similar endemic settings.

Epidemiological investigation of bovine tuberculosis outbreaks in Uruguay (2011-2013).

Bovine tuberculosis (bTB) is a chronic disease of cattle caused by infection with the Mycobacterium bovis. While bTB prevalence in Uruguay has been low (<11 outbreaks/year) for the past 50 years as a consequence of a national control program, annual incidence increased in 2011 through 2013-15, 26 and 16 infected herds each year, raising concerns from livestock stakeholders and the government. The goal of this study was to assess the spatial dynamics of bTB in Uruguay from 2011 to 2013 and the association between bTB and potential demographic and movement risk factors at the herd level using data provided by the Uruguayan Ministry of Livestock, Agriculture, and Fisheries. Clustering of incident outbreaks was assessed using the Cuzick-Edwards' test and the Bernoulli model of the spatial scan statistic, and a conditional multivariable logistic regression model was used to assess risk factors associated with bTB in a subset of Uruguayan dairy farms. Significant (P<0.05) global clustering was detected in 2012, while high-risk local clusters were detected in southwestern (2011, 2012, 2013), northwestern (2012), and southeastern (2012) Uruguay. Increased risk of bTB in different regions of Uruguay suggests a potential role of animal movements in disease dissemination. Larger herds, higher numbers of animals purchased, and incoming steers to the farm were associated with increased odds of breaking with bTB, in agreement with previous studies but also suggesting other additional sources of risk. These results will contribute to enhanced effectiveness of bTB control programs in Uruguay with the ultimate objective of preventing or mitigating the impact of the disease in the human and animal populations of the country.

Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control.

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.