A micro-epidemic of hantavirus infections occurred in Lower Bavaria, South-East Germany, starting in April 2004. While only three cases were registered from 2001 to 2003, a dramatically increased number of clinically apparent human hantavirus infections (n=38) was observed in 2004, plus seven additional cases by June 2005. To determine the reservoir responsible for the infections, a total of 43 rodents were trapped in Lower Bavaria. Serological and genetic investigations revealed that Puumala virus (PUUV) is dominant in the local population of bank voles. Partial PUUV S segment nucleotide sequences originating from bank voles at four different trapping sites in Lower Bavaria showed a low divergence (up to 3.1%). This is contrasted by a nucleotide sequence divergence of 14-16% to PUUV strains detected in Belgium, France, Slovakia or North-Western Germany. PUUV sequences from bank voles in Lower Bavaria represent a new PUUV subtype which seems to be responsible for the observed increase of human hantavirus infections in 2004-2005.

We propose a method for detecting large events based on the structure of temporal communication networks. Our method is motivated by findings that viral information spreading has distinct diffusion patterns with respect to community structure. Namely, we hypothesize that global events trigger viral information cascades that easily cross community boundaries and can thus be detected by monitoring intra- and inter-community communications. By comparing the amount of communication within and across communities, we show that it is possible to detect events, even when they do not trigger a significantly larger communication volume. We demonstrate the effectiveness of our method using two examples—the email communication network of Enron and the Twitter communication network during the Boston Marathon bombing.

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Abstract-We introduce a mathematical model for the study of cooperative control problems for multiple autonomous air vehicles (AAVs) connected via a communication network. We propose a cooperative control strategy based on task-load balancing that seeks to ensure that no vehicle is underutilized and we show how to characterize task-load balancing as a stability property. Then, using Lyapunov stability analysis, we provide conditions under which task-load balancing is achieved even in the presence of communication delays. Finally, we investigate performance properties of the cooperative controller using Monte Carlo simulations. This shows the benefits of cooperation and the effects of network delays and communication topology on performance.

Based on the formation of triad junctions, the proposed mechanism generates networks that exhibit extended rather than single power law behavior. Triad formation guarantees strong neighborhood clustering and community-level characteristics as the network size grows to infinity. The asymptotic behavior is of interest in the study of directed networks in which (i) the formation of links cannot be described according to the principle of preferential attachment; (ii) the in-degree distribution fits a power law for nodes with a high degree and an exponential form otherwise; (iii) clustering properties emerge at multiple scales and depend on both the number of links that newly added nodes establish and the probability of forming triads; and (iv) groups of nodes form modules that feature less links to the rest of the nodes.