Raul Ossada

Detecting livestock production zones.

Communities are sets of nodes that are related in an important way, most likely sharing common properties and/or playing similar roles within a network. Unraveling a network structure, and hence the trade preferences and pathways, could be useful to a researcher or a decision maker. We implemented a community detection algorithm to find livestock communities, which is consistent with the definition of a livestock production zone, assuming that a community is a group of farm premises in which an animal is more likely to stay during its lifetime than expected by chance. We applied this algorithm to the network of animal movements within the state of Mato Grosso for 2007. This database holds information concerning 87,899 premises and 521,431 movements throughout the year, totaling 15,844,779 animals moved. The community detection algorithm achieved a network partition that shows a clear geographical and commercial pattern, two crucial features for preventive veterinary medicine applications; this algorithm provides also a meaningful interpretation to trade networks where links emerge based on trader node choices.

Scale-Free Networks with the Same Degree Distribution: Different Structural Properties

We have analysed some structural properties of scale-free networks with the same degree distribution. Departing from a degree distribution obtained from the Barabasi-Albert (BA) algorithm, networks were generated using four additional different algorithms (Molloy-Reed, Kalisky, and two new models named A and B) besides the BA algorithm itself. For each network, we have calculated the following structural measures: average degree of the nearest neighbours, central point dominance, clustering coefficient, the Pearson correlation coefficient, and global efficiency. We found that different networks with the same degree distribution may have distinct structural properties. In particular, model B generates decentralized networks with a larger number of components, a smaller giant component size, and a low global efficiency when compared to the other algorithms, especially compared to the centralized BA networks that have all vertices in a single component, with a medium to high global efficiency. The other three models generate networks with intermediate characteristics between B and BA models. A consequence of this finding is that the dynamics of different phenomena on these networks may differ considerably.

The friendship paradox in scale-free networks

Our friends have more friends than we do. That is the basis of the friendship paradox. In mathematical terms, the mean number of friends of friends is higher than the mean number of friends. In the present study, we analyzed the relationship between the mean degree of vertices (individuals), , and the mean number of friends of friends, , in scale-free networks with degrees ranging from a minimum degree (k_min) to a maximum degree (k_max). We deduced an expression for - for scale-free networks following a power-law distribution with a given scaling parameter (alpha). Based on this expression, we can quantify how the degree distribution of a scale-free network affects the mean number of friends of friends.

On the risk of introduction of an emerging infectious disease

published in Revista Brasileira de Epidemiologia ad-dresses the threat of an influenza pandemic and mentions “the information technology that makes it possible to monitor the dis-tribution of new cases in real time”. We are aware that the risk of spread of an emerg-ing infectious disease from one country to another depends on the number of travelers and on the ability of the monitoring system to detect infected passengers. In this letter, we describe a quantitative methodology to estimate the risk of introduction of an emerging infectious disease into a disease-free country through the movement of persons between countries in international flights. Our aim is to contribute to the dis-cussion about the importance of factors that influence the spread of influenza and other emerging diseases.Let us suppose that