Background. Using a novel combination of whole-genome sequencing (WGS) analysis and geographic metadata, we traced the origins of Salmonella Bareilly isolates collected in 2012 during a widespread food-borne outbreak in the United States associated with scraped tuna imported from India. Methods. Using next-generation sequencing, we sequenced the complete genome of 100 Salmonella Bareilly isolates obtained from patients who consumed contaminated product, from natural sources, and from unrelated historically and geographically disparate foods. Pathogen genomes were linked to geography by projecting the phylogeny on a virtual globe and produced a transmission network. Results. Phylogenetic analysis of WGS data revealed a common origin for outbreak strains, indicating that patients in Maryland and New York were infected from sources originating at a facility in India. Conclusions. These data represent the first report fully integrating WGS analysis with geographic mapping and a novel use of transmission networks. Results showed that WGS vastly improves our ability to delimit the scope and source of bacterial food-borne contamination events. Furthermore, these findings reinforce the extraordinary utility that WGS brings to global outbreak investigation as a greatly enhanced approach to protecting the human food supply chain as well as public health in general.

Due to limited resources and harsh environments, partitioning can be inevitable in wireless sensor networks (WSNs). To re-establish network connectivity, healthy mobile sensor nodes can be repositioned in an on-demand basis. However, the nodes are equipped with limited batteries and their movement requires excessive energy consumption. In this paper, we address the problem of minimising the movement cost when re-establishing network connectivity in delay tolerant WSNs by presenting a distributed heuristic approach based on Game Theory. Compared to centralised heuristics which assume complete knowledge of the network and failures, this approach is self-healing that can work with uncertain network/failure information. Under limited knowledge on partitions and failure locations, Game Theory is used to facilitate decision making on the selection of the nodes to be moved and their movement directions. Extensive simulations have revealed the efficiency of the proposed approach in terms of movement distance and network coverage.