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.

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.

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.

Abstract-Access Control is crucial for security management, but in the context of the Internet of Things it cannot be implemented the same way as traditional systems do. Indeed, devices that make the Internet of Things impose some constraints that encourage the design of new access control mechanisms, which should provide flexibility of configuration, as well as support several authorization scopes at the same time, yet being computationally light, dynamic and scalable in order to be ready for the forthcoming Cloud Computing paradigm. In this paper we propose an authorization model that is based on the OAuth 2.0 protocol. From the point of view of the identity provider, this model allows managing roles and permissions for an application-scoped authorization, to enable more flexible scenarios in which multiple tenants take part. With regard to devices, the OAuth 2.0 makes authorization extremely light, because all the required information is provided with a token. Considering all this, authorization management is completely delegated to an external system, so that an as-a-service access control mechanism is provided. The proposed model complies with the security, flexibility and performance requirements that are needed in the Internet of Things paradigm.

access control is a key element when guaranteeing the security of online services. However, devices that make the Internet of Things have some special requirements that foster new approaches to access control mechanisms. Their low computing capabilities impose limitations that make traditional paradigms not directly applicable to sensors and actuators. In this paper, we propose a dynamic, scalable, IoT-ready model that is based on the OAuth 2.0 protocol and that allows the complete delegation of authorization, so that an as a service access control mechanism is provided. Multiple tenants are also supported by means of application-scoped authorization policies, whose roles and permissions are fine-grained enough to provide the desired flexibility of configuration. Besides, OAuth 2.0 ensures interoperability with the rest of the Internet, yet preserving the computing constraints of IoT devices, because its tokens provide all the necessary information to perform authorization. The proposed model has been fully implemented in an open-source solution and also deeply validated in the scope of FIWARE, a European project with thousands of users, the goal of which is to provide a framework for developing smart applications and services for the future Internet. We provide the details of the deployed infrastructure and offer the analysis of a sample smart city setup that takes advantage of the model. We conclude that the proposed solution enables a new access control as a service paradigm that satisfies the special requirements of IoT devices in terms of performance, scalability and interoperability.