Traffic Optimization to Control Epidemic Outbreaks in Metapopulation Models

We propose a novel framework to study viral spreading processes in metapopulation models. Large subpopulations (i.e., cities) are connected via metalinks (i.e., roads) according to a metagraph structure (i.e., the traffic infrastructure). The problem of containing the propagation of an epidemic outbreak in a metapopulation model by controlling the traffic between subpopulations is considered. Controlling the spread of an epidemic outbreak can be written as a spectral condition involving the eigenvalues of a matrix that depends on the network structure and the parameters of the model. Based on this spectral condition, we propose a convex optimization framework to find cost-optimal approaches to traffic control in epidemic outbreaks

Worst-Case Scenarios for Greedy, Centrality-Based Network Protection Strategies

The task of allocating preventative resources to a computer network in order to protect against the spread of viruses is addressed. Virus spreading dynamics are described by a linearized SIS model and protection is framed by an optimization problem which maximizes the rate at which a virus in the network is contained given finite resources. One approach to problems of this type involve greedy heuristics which allocate all resources to the nodes with large centrality measures. We address the worst case performance of such greedy algorithms be constructing networks for which these greedy allocations are arbitrarily inefficient. An example application is presented in which such a worst case network might arise naturally and our results are verified numerically by leveraging recent results which allow the exact optimal solution to be computed via geometric programming

Traffic Control for Network Protection Against Spreading Processes

Epidemic outbreaks in human populations are facilitated by the underlying transportation network. We consider strategies for containing a viral spreading process by optimally allocating a limited budget to three types of protection resources: (i) Traffic control resources, (ii), preventative resources and (iii) corrective resources. Traffic control resources are employed to impose restrictions on the traffic flowing across directed edges in the transportation network. Preventative resources are allocated to nodes to reduce the probability of infection at that node (e.g. vaccines), and corrective resources are allocated to nodes to increase the recovery rate at that node (e.g. antidotes). We assume these resources have monetary costs associated with them, from which we formalize an optimal budget allocation problem which maximizes containment of the infection. We present a polynomial time solution to the optimal budget allocation problem using Geometric Programming (GP) for an arbitrary weighted and directed contact network and a large class of resource cost functions. We illustrate our approach by designing optimal traffic control strategies to contain an epidemic outbreak that propagates through a real-world air transportation network.Comment: arXiv admin note: text overlap with arXiv:1309.627

Optimal Vaccine Allocation to Control Epidemic Outbreaks in Arbitrary Networks

We consider the problem of controlling the propagation of an epidemic outbreak in an arbitrary contact network by distributing vaccination resources throughout the network. We analyze a networked version of the Susceptible-Infected-Susceptible (SIS) epidemic model when individuals in the network present different levels of susceptibility to the epidemic. In this context, controlling the spread of an epidemic outbreak can be written as a spectral condition involving the eigenvalues of a matrix that depends on the network structure and the parameters of the model. We study the problem of finding the optimal distribution of vaccines throughout the network to control the spread of an epidemic outbreak. We propose a convex framework to find cost-optimal distribution of vaccination resources when different levels of vaccination are allowed. We also propose a greedy approach with quality guarantees for the case of all-or-nothing vaccination. We illustrate our approaches with numerical simulations in a real social network

Optimal Resource Allocation for Network Protection Against Spreading Processes

We study the problem of containing spreading processes in arbitrary directed networks by distributing protection resources throughout the nodes of the network. We consider two types of protection resources are available: (i) Preventive resources able to defend nodes against the spreading (such as vaccines in a viral infection process), and (ii) corrective resources able to neutralize the spreading after it has reached a node (such as antidotes). We assume that both preventive and corrective resources have an associated cost and study the problem of finding the cost-optimal distribution of resources throughout the nodes of the network. We analyze these questions in the context of viral spreading processes in directed networks. We study the following two problems: (i) Given a fixed budget, find the optimal allocation of preventive and corrective resources in the network to achieve the highest level of containment, and (ii) when a budget is not specified, find the minimum budget required to control the spreading process. We show that both resource allocation problems can be solved in polynomial time using Geometric Programming (GP) for arbitrary directed graphs of nonidentical nodes and a wide class of cost functions. Furthermore, our approach allows to optimize simultaneously over both preventive and corrective resources, even in the case of cost functions being node-dependent. We illustrate our approach by designing optimal protection strategies to contain an epidemic outbreak that propagates through an air transportation network

A Parallel Fuzzy C-Mean algorithm for Image Segmentation

This paper proposes a parallel Fuzzy C-Mean (FCM) algorithm for image segmentation. The sequential FCM algorithm is computationally intensive and has significant memory requirements. For many applications such as medical image segmentation and geographical image analysis that deal with large size images, sequential FCM is very slow. In our parallel FCM algorithm, dividing the computations among the processors and minimizing the need for accessing secondary storage, enhance the performance and efficiency of image segmentation task as compared to the sequential algorithm. such as medical image segmentation and geographical image analysis that deal with large size images, sequenrial FCM is very slow. In our parallel FCM algorithm, dividing the computations among the processors and minimizing the need for accessing secondary storage, enhance the performance and efficiency of image segmentation task as compared to the sequential algorith

Investigation of anesthetizing effect of tobacco (Nicotiana tabacum) aqueous and alcoholic extract on rainbow trout (Oncorhynchus mykiss)

The main goal of anesthesia in rainbow trout is decrease mobility and reduce stress of fish while breeding, and also facilitate operation such as manipulation to Tagging, Biometry, Sorting, Inspection and Sampling for pathology test. In the present study, Aqueous and alcoholic extract of tobacco plant (Nicotiana tabacum) for Anaesthetizing Rainbow trout (Oncohrynchus mykiss) in 3 weight groups: fry (less than 80 g), growing(about 200 g) and breeding(larger than 1500g) were used. The effective dose of tobacco in treatments 200, 400, 800 and 1600mg/lit in aqueous and 0.7, 1, 1.7 and 3.5mg/lit in alcoholic extract were tested. Totally, we can say that tobacco extract Anaesthetizes the rainbow trout in less than 3 minutes and recovery time is less that 5 minutes. Regarding to anesthetizing time, recovery time and fish relaxing during anesthesia, aqueous extract applies more gentle anesthesia in rainbow trout, and dose 200 to 800 mg/lit of aqueous extract were more successful. Also Regarding to significant correlation between doze and Anesthetics extremity and recovery time (P<0.05), lower dose of tobacco extract can be used to making fish calm for transportation

Pathological Effects of Aflatoxin and Their Amelioration by Vitamin E in White Leghorn Layers

White Leghorn layer breeder hens, 30 weeks of age, were divided into 12 groups (A-L). Group A was kept on basal feed and served as control, while group B was offered feed supplemented with vitamin E (100 mg/Kg). Groups C-G were offered feed containing 100, 500, 2,500, 5,000 and 10,000 µg/Kg aflatoxin B1 (AFB1), respectively, whereas groups H-L were offered same dietary levels of AFB1 along with vitamin E (100 mg/Kg). The experimental feeds were offered for three weeks and afterward all the groups were switched over to basal feed for next two weeks. Body weight, absolute and relative weights of liver and kidneys of AF fed birds were significantly higher than control group. Pathological lesions in aflatoxin (AF) fed birds included enlarged, pale and friable liver, swollen kidneys and hemorrhages on different organs. Histopathological lesions in liver included fatty change, congestion and hemorrhages, while in kidneys tubular necrosis, cellular infiltration, congestion and hemorrhages were found in groups fed AFB1 at 500 μg/Kg and higher doses. In AF fed hens, no significant ameliorative effects of vitamin E could be observed upon AF induced decrease in feed intake, gross pathology and histopathological alterations and organ weight except body weights. It was concluded that the vitamin E ameliorated the AFB1 induced toxic effects in some of parameters studied