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1
Lindamulage, de Silva Olivier. "On the Efficiency of Decentralized Epidemic Management and Competitive Viral Marketing." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0145.
Abstract:
Cette thèse explore la prise de décision décentralisée dans les dynamiques épidémiques et de marketing viral en utilisant la théorie des jeux afin d'évaluer son efficacité. La thèse commence par une revue des outils mathématiques, mettant l'accent sur la théorie des graphes/jeux. Dans la suite de ce manuscrit, l'analyse de jeu épidémiologique et de compétition en marketing viral est établie. Notamment, dans le chapitre 2 où il est présenté un jeu épidémique en réseau dans lequel chaque joueur (région ou pays) cherche à trouver un compromis entre les pertes socio-économiques et sanitaires, tout en prenant en compte des contraintes telles que la disponibilité des unités de soins intensifs (USI). L'équilibre de Nash et l'équilibre de Nash généralisé sont analysés, et l'impact de la décentralisation sur l'efficacité est mesuré à l'aide de paramètres tels que le prix de l'anarchie (PoA) et le prix de la connectivité (PoC). Une application pratique du jeu à un scénario de Covid-19 est également illustrée. Le chapitre 3 étend l'analyse du chapitre 2 en incorporant la dynamique des opinions dans le contrôle décentralisé d'une épidémie en réseau. L'analyse se concentre sur l'existence et l'unicité de l'équilibre de Nash généralisé (GNE), et un algorithme pour atteindre le GNE est proposé. Les simulations identifient les scénarios où la décentralisation est acceptable en termes d'efficacité globale et soulignent l'importance de la dynamique des opinions dans les processus de prise de décision. Finalement, le chapitre 4 explore un modèle de duopole de Stackelberg dans le contexte des campagnes de marketing viral. L'objectif est de caractériser la stratégie d'allocation optimale des budgets publicitaires entre les régions pour maximiser la part de marché. Des stratégies d'équilibre sont déduites et des conditions pour un résultat de type "le gagnant rafle tout" sont établies. Les résultats théoriques sont complétés par des simulations numériques et un exemple illustrant la caractérisation de l'équilibre. Cette thèse offre des perspectives précieuses sur l'efficacité de la prise de décision décentralisée dans les dynamiques épidémiques et de marketing viral. Les résultats ont des implications pour la gestion des soins de santé, la concurrence commerciale et d'autres domaines connexesThis thesis investigates decentralized decision-making in epidemic and viral marketing dynamics. The mathematical framework of game theory is exploited to design and assess the effectiveness of decentralized strategies. The thesis begins with a review of mathematical tools, emphasizing graph theory and game theory. Chapter 2 presents a networked epidemic game where each player (region or country) seeks to implement a tradeoff between socio-economic and health looses, incorporating constraints such as intensive care unit (ICU) availability. Nash equilibrium and Generalized Nash equilibrium are analyzed, and the influence of decentralization on global efficiency is measured using metrics like the Price of Anarchy (PoA) and the Price of Connectedness (PoC). The practical application of the game to a Covid-19 scenario is illustrated. Chapter 3 extends the analysis of Chapter 2 by incorporating opinion dynamics into the decentralized control of a networked epidemic. A new game model is introduced, where players represent geographical aera balancing socio-economic and health losses; the game is built to implement features of practical interests and to possess some mathematical properties (e.g., posynomiality) which makes its analysis tractable. The analysis focuses on the existence and uniqueness of the Generalized Nash Equilibrium (GNE), and an algorithm for computing the GNE is proposed. Numerical simulations quantify the efficiency loss induced by decentralization in the presence and absence of opinion dynamics. The results identify scenarios where decentralization is acceptable in terms of global efficiency measures and highlight the importance of opinion dynamics in decision-making processes. Chapter 4 explores a Stackelberg duopoly model in the context of viral marketing campaigns. The objective is to characterize the optimal allocation strategy of advertising budgets across regions to maximize market share. A relatively simple Equilibrium strategies are derived, and conditions for a "winner takes all" outcome are established. Theoretical findings are complemented by numerical simulations and an example illustrating equilibrium characterization.This thesis offers valuable insights into the effectiveness of decentralized decision-making in the context of epidemic and viral marketing dynamics. The findings have implications for healthcare management, business competition, and related fields
2
Tunc, Ilker. "Epidemic models on adaptive networks with network structure constraints." W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623618.
Abstract:
Spread of infectious diseases progresses as a result of contacts between the individuals in a population. Therefore, it is crucial to gain insight into the pattern of connections to better understand and possibly control the spread of infectious diseases. Moreover, people may respond to an epidemic by changing their social behaviors to prevent infection. as a result, the structure of the network of social contacts evolves adaptively as a function of the disease status of the nodes. Recently, the dynamic relationships between different network topologies and adaptation mechanisms have attracted great attention in modeling epidemic spread. However, in most of these models, the original network structure is not preserved due to the adaptation mechanisms involving random changes in the links. In this dissertation, we study more realistic models with network structure constraints to retain aspects of the original network structure.;We study a susceptible-infected-susceptible (SIS) disease model on an adaptive network with two communities. Different levels of heterogeneity in terms of average connectivity and connection strength are considered. We study the effects of a disease avoidance adaptation mechanism based on the rewiring of susceptible-infected links through which the disease could spread. We choose the rewiring rules so that the network structure with two communities would be preserved when the rewiring links occur uniformly. The high dimensional network system is approximated with a lower dimensional mean field description based on a moment closure approximation. Good agreement between the solutions of the mean field equations and the results of the simulations are obtained at the steady state. In contrast to the non-adaptive case, similar infection levels in both of the communities are observed even when they are weakly coupled. We show that the adaptation mechanism tends to bring both the infection level and the average degree of the communities closer to each other.;In this rewiring mechanism, the local neighborhood of a node changes and is never restored to its previous state. However, in real life people tend to preserve their neighborhood of friends. We propose a more realistic adaptation mechanism, where susceptible nodes temporarily deactivate their links to infected neighbors and reactivate the links to those neighbors after they recover. Although the original network is static, the subnetwork of active links is evolving.;We drive mean field equations that predict the behavior of the system at the steady state. Two different regimes are observed. In the slow network dynamics regime, the adaptation simply reduces the effective average degree of the network. However, in the fast network dynamics regime, the adaptation further suppresses the infection level by reducing the dangerous links. In addition, non-monotonic dependence of the active degree on the deactivation rate is observed.;We extend the temporary deactivation adaptation mechanism to a scale-free network, where the degree distribution shows heavy tails. It is observed that the tail of the degree distribution of the active subnetwork has a different exponent than that of the original network. We present a heuristic explanation supporting that observation. We derive improved mean field equations based on a new moment closure approximation which is derived by considering the active degree distribution conditioned on the total degree. These improved mean field equations show better agreement with the simulation results than standard mean field analysis based on homogeneity assumptions.
3
Burch, Mark G. "Statistical Methods for Network Epidemic Models." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471613656.
4
Marques, Fernando Silveira. "Modelo híbrido estocástico aplicado no estudo de espalhamento de doenças infecciosas em redes dinâmicas de movimentação de animais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/10/10134/tde-16112015-110234/.
Abstract:
Objetivo. Desenvolvimento de uma estrutura para aplicação de simulação numérica estocástica no estudo de espalhamento de doenças em metapopulações de maneira que esta incorpore a topologia dinâmica de contatos entre as subpopulações, verificando as peculiaridades do modelo e aplicando este modelo às redes de movimentação de animais de Pernambuco para estudar o papel das feiras de animais. Método. Foi utilizado o paradigma de modelos híbridos para tratar do espalhamento de doenças nas metapopulações que, das nossas aplicações, resultou na união de duas estratégias de modelagem: Modelos Baseados no Indivíduo e o Algorítimo de Simulação Estocástica. Aplicamos os modelos híbridos em redes de movimentação de animais reais e fictícias para destacar as diferenças dos modelos híbridos com diferentes abordagens de migração (pendular e definitiva) e comparamos estes modelos com modelos clássicos de equações diferenciais. Ainda, através do pacote hybridModels, estudamos o papel das feiras de animais em cenários de epidemia de febre aftosa na rede de movimentação de animais de Pernambuco, introduzindo a doença numa feira de animais contida numa amostra da base de Guia de Trânsito Animal e calculamos a cadeia de infecção dos estabelecimentos. Resultados. Constatamos que no estudo de epidemias com o uso de modelo híbrido, a migração pendular, na média, subestima o número de animais infectados no cenário de comercialização de animais (migração defi nitiva), além de traduzir uma dinâmica de espalhamento enganosa, ignorando cenários mais complexo oferecido pela migração definitiva. Criamos o pacote hybridModels que generaliza os modelos híbridos com migração definitiva e com ele aplicamos um modelo híbrido SIR na rede de Pernambuco e verificamos que as feiras de animais de Pernambuco são potentes disseminadores de doenças transmissíveis. Conclusão. Apesar de custo computacional maior no estudo de espalhamento de doenças, a migração definitiva é o mais adequado tipo de conexão entre as subpopulações de animais de produção. Ainda, de acordo com as nossas analises, as feiras de animais estão entre os mais importantes nós na rede de movimentação de Pernambuco e devem ter lugar de destaque nas estratégias de controle e vigilância epidemiológicaObjective. Development of framework applied to stochastic numerical simulation for the study of disease spreading in metapopulations, in a way that it incorporates the dynamic topology of contacts between subpopulations, checking the framework peculiarities and applying it to the animal movement network of Pernambuco to study the role of animal markets. Method. We used hybrid models paradigm to treat disease spread in metapopulations. From our applications it has resulted in the union of two modeling strategies: Individual-based model and the Algorithm for Stochastic Simulation. We applied hybrid models in real and fictitious networks to highlight the differences between different animal movement approaches (commuting and migration) and we compared these models with classic models of differential equations. Furthermore, through the hybridModels package, we studied the role of animal markets in epidemic scenarios of Foot and Mouth Disease (FMD) in animal movement networks of Pernambuco, introducing the disease in an animal market of a sample from the Animal Transit Record of Pernambuco’s database and calculating the contact infection chain of premises. Results. We noted that in the study of epidemics using a hybrid model, commuting can underestimates the number of infected animals in the animal trade scenario (migration), and resulting in a misleading spreading dynamic by ignoring a more complex scenario that occurs with migration. We created the hybridModels package that generalizes the hybrid models with migration, applied a SIR hybrid model to the animal movement network of Pernambuco and verified that animal markets are important disease spreaders. Conclusion. Despite its higher computational cost in the study of epidemics in animal movement networks, migration is the most suitable type of connection between subpopulations. Furthermore, animal markets of Pernambuco are among the most important nodes for disease transmission and should be considered in strategies of surveillance and disease control
5
Livingston, Samantha 1980. "Stochastic models for epidemics on networks." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28437.
Abstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 37).
In this thesis, I looked at an extension of the Reed-Frost epidemic model which had two-sub-populations. By setting up a Markov chain to model the epidemic and finding the transition probabilities of that chain, MATLAB could be used to solve for the expected number of susceptibles and the expected duration. I simulated the model with more tan two sub-populations to find the average number of susceptibles and reviewed previously solved stochastic spatial models to understand how to solve the multiple-population Reed-Frost model on a network.
by Samantha Livingston.
M.Eng.
6
Sensi, Mattia. "A Geometric Singular Perturbation approach to epidemic compartmental models." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/286191.
Abstract:
We study fast-slow versions of the SIR, SIRS and SIRWS epidemiological models, and of the SIRS epidemiological model on homogeneous graphs, obtained through the application of the moment closure method. The multiple time scale behavior is introduced to account for large differences between some of the rates of the epidemiological pathways. Our main purpose is to show that the fast-slow models, even though in nonstandard form, can be studied by means of Geometric Singular Perturbation Theory (GSPT). In particular, without using Lyapunov's method, we are able to not only analyze the stability of the endemic equilibria of the SIR and SIRS models, but also to show that in the remaining models limit cycles arise. We show that the proposed approach is particularly useful in more complicated (higher dimensional) models such as the SIRWS model and the SIRS on homogeneous graphs, for which we provide a detailed description of their dynamics by combining analytic and numerical techniques. In particular, for the latter we show that the model can give rise to periodic solutions, differently from the corresponding model based on homogeneous mixing.
7
Sensi, Mattia. "A Geometric Singular Perturbation approach to epidemic compartmental models." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/286191.
Abstract:
We study fast-slow versions of the SIR, SIRS and SIRWS epidemiological models, and of the SIRS epidemiological model on homogeneous graphs, obtained through the application of the moment closure method. The multiple time scale behavior is introduced to account for large differences between some of the rates of the epidemiological pathways. Our main purpose is to show that the fast-slow models, even though in nonstandard form, can be studied by means of Geometric Singular Perturbation Theory (GSPT). In particular, without using Lyapunov's method, we are able to not only analyze the stability of the endemic equilibria of the SIR and SIRS models, but also to show that in the remaining models limit cycles arise. We show that the proposed approach is particularly useful in more complicated (higher dimensional) models such as the SIRWS model and the SIRS on homogeneous graphs, for which we provide a detailed description of their dynamics by combining analytic and numerical techniques. In particular, for the latter we show that the model can give rise to periodic solutions, differently from the corresponding model based on homogeneous mixing.
8
Riad, Md Mahbubul Huq. "Modeling Japanese Encephalitis using interconnected networks for a hypothetical outbreak in the USA." Kansas State University, 2017. http://hdl.handle.net/2097/35379.
Abstract:
Master of ScienceDepartment of Electrical and Computer Engineering
Caterina Maria Scoglio
Japanese Encephalitis (JE) is a vector-borne disease transmitted by mosquitoes and maintained in birds and pigs. An interconnected network model is proposed to examine the possible epidemiology of JE in the USA. Proposed JE model is an individual-level network model that explicitly considers the feral pig population and implicitly considers mosquitoes and birds in specific areas of Florida, North Carolina, and South Carolina. The virus transmission among feral pigs within a small geographic area (<60 sq mi areas) are modeled using two network topologies— fully connected and Erdos-Renyi networks. Connections between locations situated in different states (interstate links) are created with limited probability and based on fall and spring bird migration patterns. Simulation results obtained from the network models support the use of the Erdos-Renyi network because maximum incidence occurs during the fall migration period which is similar to the peak incidence of the closely related West Nile virus (WNV), another virus in the Japanese Encephalitis group (Flaviviridae) that is transmitted by both birds and mosquitoes. Simulation analysis suggested two important mitigation strategies: for low mosquito vectorial capacity, insecticidal spraying of infected areas reduces transmission and limits the outbreak to a single geographic area. Alternatively, in high mosquito vectorial capacity areas, birds rather than mosquitoes need to be removed/controlled.
9
Taylor, Michael. "Exact and approximate epidemic models on networks : theory and applications." Thesis, University of Sussex, 2013. http://sro.sussex.ac.uk/id/eprint/45258/.
Abstract:
This thesis is concerned with modelling the spread of diseases amongst host populations and the epidemics that result from this process. We are primarily interested in how networks can be used to model the various heterogeneities observable in real-world populations. Firstly, we start with the full system of Kolmogorov/master equations for a simple Susceptible-Infected-Susceptible (SIS) type epidemic on an arbitrary contact network. From this general framework, we rigorously derive sets of ODEs that describe the exact dynamics of the expected number of individuals and pairs of individuals. We proceed to use moment closure techniques to close these hierarchical systems of ODEs, by approximating higher order moments in terms of lower order moments. We prove that the simple first order mean-field approximation becomes exact in the limit of a large, fully-connected network. We then investigate how well two different pairwise approximations capture the topological features of theoretical networks generated using different algorithms. We then introduce the effective degree modelling framework and propose a model for SIS epidemics on dynamic contact networks by accounting for random link activation and deletion. We show that results from the resulting set of ODEs agrees well with results from stochastic simulations, both in describing the evolution of the network and the disease. Furthermore, we derive an analytic calculation of the stability of the disease-free steady state and explore the validity of such a measure in the context of a dynamically evolving contact network. Finally, we move on to derive a system of ODEs that describes the interacting dynamics of a disease and information relating to the disease. We allow individuals to become responsive in light of received information and, thus, reduce the rate at which they become infected. We consider the effectiveness of different routes of information transmission (such as peer-to-peer communication or mass media campaigns) in slowing or preventing the spread of a disease. Finally, we use a range of modelling techniques to investigate the spread of disease within sheep flocks. We use field data to construct weighted contact networks for flocks of sheep to account for seasonal changes of the flock structure as lambs are born and eventually become weaned. We construct a range of network and ODE models that are designed to investigate the effect of link-weight heterogeneity on the spread of disease.
10
Davis, Ben. "Stochastic epidemic models on random networks : casual contacts, clustering and vaccination." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/47272/.
Abstract:
There has been considerable recent interest in models for epidemics on networks describing social contacts. This thesis considers a stochastic SIR (Susceptible - Infective - Removed) model for the spread of an epidemic among a population of individuals, with a random network of social contacts, that is partitioned into households and in which individuals also make casual contacts, i.e. with people chosen uniformly at random from the population. The behaviour of the model as the population tends to infinity is investigated. A threshold parameter that governs whether or not the epidemic with an initial infective can become established is obtained, as is the probability that such an outbreak occurs and, if so, how large it will become. The behaviour of this model is then compared to that of a finite population using Monte Carlo simulations. The effect of the different transmission routes on the final outcome of an epidemic and the effect of introducing social contacts and clustering to the network on the performance of various vaccination strategies are also investigated.
11
Pachas, Manrique Anna Patricia. "Modelos epidemiológicos em redes." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/18662.
Abstract:
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The speed and comprehensiveness global level with the pathogen has spread in recent years has drawn attention to the importance of the contact’s social network structure. In fact, the topology of the networks in which members of society interact has influenced the dynamics of epidemics. Studies have shown that pathogens when disiparem in scale-free networks have different effects when compared broadcast in random networks, such as the classic models. In these there were epidemic threshold, may somehow the health ministry have a control on the dissipation of diseases by applying certain measures such as vaccines. Already in models in which are considered the networks, specifically the free network scale, the threshold disappears. Thus, the epidemic threshold depends on the topology is required to include within this structure models Because of the importance of these networks, random networks and scalefree have been implemented along the epidemics of propagation models to check the epidemic threshold and the characteristic time, noting that the epidemic threshold disappears
A velocidade e a abrangência a nível mundial com que os agentes patogênicos tem se disseminado nos últimos anos tem chamado a atenção para a importância da estrutura da rede social de contato . De fato, a topologia das redes na qual os membros da sociedade interagem têm influenciado na dinâmica das epidemias.Estudos têm demostrado que os agentes patogênicos ao se dissiparem em redes livres de escala tem efeitos diferentes se comparado quando difundidos em redes aleatórias, como nos modelos clássicos. Nestes existiam limiar de epidemia ,podendo de alguma forma as entidades de saúde ter um controle sobre a dissipação das enfermidades , aplicando certas medidas como as vacinas por exemplo. Já nos modelos nos quais são consideradas as redes , especificamente a rede livre de escala,este limiar desaparece. Desta forma, o limiar de epidemia ao depender da topologia se faz necessário incluir esta estrutura dentro dos modelos epidemiológicos. Devido a importância destas redes , redes aleatórias e principalmente redes livres de escala foram implementadas junto a modelos de propagação de epidemias para verificar o limiar de epidemia e o tempo característico , verificando que o limiar de epidemia desaparece.
12
Nsoesie, Elaine O. "Sensitivity Analysis and Forecasting in Network Epidemiology Models." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/37620.
Abstract:
In recent years, several methods have been proposed for real-time modeling and forecasting of the epidemic curve. These methods range from simple compartmental models to complex agent-based models. In this dissertation, we present a model-based reasoning approach to forecasting the epidemic curve and estimating underlying disease model parameters. The method is based on building an epidemic library consisting of past and simulated influenza outbreaks. During an influenza epidemic, we use a combination of statistical, optimization and modeling techniques to either assign the epidemic to one of the cases in the library or propose parameters for modeling the epidemic. The method is presented in four steps. First, we discuss a sensitivity analysis study evaluating how minute changes in the disease model parameters influence the dynamics of simulated epidemics. Next, we present a supervised classification method for predicting the epidemic curve. The epidemic curve is forecasted by matching the partial surveillance curve to at least one of the epidemics in the library. We expand on the classification approach by presenting a method which identifies epidemics similar or different from those in the library. Lastly, we discuss a simulation optimization method for estimating model parameters to forecast the epidemic curve of an epidemic distinct from those in the library.Ph. D.
13
Schumm, Phillip Raymond Brooke. "Characterizing epidemics in metapopulation cattle systems through analytic models and estimation methods for data-driven model inputs." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16897.
Abstract:
Doctor of PhilosophyDepartment of Electrical and Computer Engineering
Caterina Maria Scoglio
We have analytically discovered the existence of two global epidemic invasion thresholds in a directed meta-population network model of the United States cattle industry. The first threshold describes the outbreak of disease first within the core of the livestock system while the second threshold describes the invasion of the epidemic into a second class of locations where the disease would pose a risk for contamination of meat production. Both thresholds have been verified through extensive numerical simulations. We have further derived the relationship between the pair of thresholds and discovered a unique dependence on the network topology through the fractional compositions and the in-degree distributions of the transit and sink nodes. We then addressed a major challenge for epidemiologists and their efforts to model disease outbreaks in cattle. There is a critical shortfall in the availability of large-scale livestock movement data for the United States. We meet this challenge by developing a method to estimate cattle movement parameters from publicly available data. Across 10 Central States of the US, we formulated a large, convex optimization problem to predict the cattle movement parameters which, having minimal assumptions, provide the best fit to the US Department of Agriculture's Census database and follow constraints defined by scientists and cattle experts. Our estimated parameters can produce distributions of cattle shipments by head which compare well with shipment distributions also provided by the US Department of Agriculture. This dissertation concludes with a brief incorporation of the analytic models and the parameter estimation. We approximated the critical movement rates defined by the global invasion thresholds and compared them with the average estimated cattle movement rates to find a significant opportunity for epidemics to spread through US cattle populations.
14
Sutrave, Sweta. "Dynamic network models of a continental epidemic: soybean rust in the USA." Thesis, Kansas State University, 2010. http://hdl.handle.net/2097/4588.
Abstract:
Master of ScienceDepartment of Electrical and Computer Engineering
Karen A. Garrett
Caterina M. Scoglio
With rapid global movement of epidemics, research efforts to characterize dynamics of epidemics have gained much focus. Traditional epidemiological models have focused on only temporal components of epidemics. Development of spatio-temporal models proved to be a notable achievement in epidemiology. Network-based epidemiological models enable better handling of spatial and temporal components of an epidemic. Early network models considered a binary level of contact between infected entities, which is an idealistic approach. A realistic approach would use weighted edges which signify the level of interaction between the nodes where the edge-weights change over time as a function of environmental factors. Estimation of edge weights from observed time series is a relatively less explored area for network modeling. Dynamic networks make the problem more complicated as edge weights change over time. Estimation of parameters for models describing the edge weights as a function of variables that change in time has the potential to provide better general models. Soybean rust (caused by Phakopsora pachyrhizi) is an important disease globally and its occurrence in the US has been studied extensively since its introduction in 2004. Rust is a fungal disease which propagates as a result of the fungal spores being carried by the wind. In this thesis, a network network based model is proposed to predict the intensity of spread of the disease in space and time. This model uses the host abundance and wind data and the observed rust incidence time series to compute the edge-weights. Also, the edge-weights in the model change over time thus following a dynamic approach. In order to cut costs involved with the establishment and maintenance of infection monitoring sites, the effect of removal of monitoring nodes using various strategies has also been analyzed in this thesis. The model has been tested with observed soybean rust data from sentinel plot network from across the United States.
15
Kolgushev, Oleg. "Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955128/.
Abstract:
Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
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Ferreira, Jackson Andrade. "Um modelo multiescalas de autômatos celulares para pandemia da dengue." Universidade Federal de Viçosa, 2009. http://locus.ufv.br/handle/123456789/4233.
Abstract:
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Previous issue date: 2009-02-09Fundação de Amparo a Pesquisa do Estado de Minas Gerais
The dramatic resurgence and emergence of epidemic dengue and dengue hemorragic fever in the last two decades neatly define a global pandemic. The dispersion of dengue viruses combines local infections of humans bited by infective mosquitoes inside a city with long-range transmissions to non-infective vectors that feed the blood of infected people arriving from other urban areas. In the present work a cellular automata model for dengue epidemic is proposed and investigated through large-scale computer simulations. The model takes into account the main features concerning the population dynamics of mosquitoes and humans and the disease transmission cycle. Furthermore, the model is defined on a scale-free network in which each node is a square lattice in order to properly describe the environment as urban centers interconnected through a national transportation system. A nonzero epidemic threshold is found and it is approached with a power law behavior by the density of infected individuals, as observed in the small-world network of Watts and Strogatz. Also, it is studied the importance of three parameters for the dengue spreading: the diffusivity of the mosquitoes, the probability of a mosquito bites humans, and the travel's probability of people between two interconnected cities. Finally, maps of infected individuals are obtained in order to caracterise the epidemic spreading.
O dramático ressurgimento e a emergência da epidemia de dengue e dengue hemorrágica nas últimas duas décadas claramente definem uma pandemia global. A dispersão do vírus da dengue combina infecções locais dos seres humanos picados por mosquitos infectados dentro de uma cidade com transmissões de longo alcance por vetores não-infecciosos que se alimentam do sangue de pessoas infectadas provenientes de outras zonas urbanas. No presente trabalho um modelo de autômatos celulares para epidemias de dengue é proposto e investigado através de siulação por computador, em larga escala. O modelo leva em conta as principais características relativas à dinâmica das populações de mosquitos e seres humanos e o ciclo de transmissão da doença. Além disso, o modelo é definido em uma rede livre de escala, em que cada nó é uma rede quadrada, a fim de descrever adequadamente o meio ambiente como os centros urbanos interligados através do sistema de transporte nacional. Um limiar epidêmico diferente de zero é encontrado e é aproximado com um comportamento tipo lei de potência pela densidade de indivíduos infectados, como observado na rede mundo-pequeno de Watts-Strogatz. Também, é estudada a importância de três parâmetros na dispersão da dengue: a difusividade do mosquito, a probabilidade do mosquito picar um ser humano, e a probabilidade de viagem de pessoas entre duas cidades conectadas. Por fim, mapas de indivíduos infectados são obtidos a fim de caracterizar a difusão da epidemia.
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Oleś, Katarzyna A. "Searching for the optimal control strategy of epidemics spreading on different types of networks." Thesis, University of Stirling, 2014. http://hdl.handle.net/1893/21199.
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The main goal of my studies has been to search for the optimal control strategy of controlling epidemics when taking into account both economical and social costs of the disease. Three control scenarios emerge with treating the whole population (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy, LS) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. Although the properties of the pathogen might not be known in advance for emerging diseases, the prediction of the optimal strategy can be made based on economic analysis only. The details of the local strategy and in particular the size of the optimal treatment neighbourhood weakly depends on disease infectivity but strongly depends on other epidemiological factors (rate of occurring the symptoms, spontaneously recovery). The required extent of prevention is proportional to the size of the infection neighbourhood, but this relationship depends on time till detection and time till treatment in a non-nonlinear (power) law. The spontaneous recovery also affects the choice of the control strategy. I have extended my results to two contrasting and yet complementary models, in which individuals that have been through the disease can either be treated or not. Whether the removed individuals (i.e., those who have been through the disease but then spontaneously recover or die) are part of the treatment plan depends on the type of the disease agent. The key factor in choosing the right model is whether it is possible - and desirable - to distinguish such individuals from those who are susceptible. If the removed class is identified with dead individuals, the distinction is very clear. However, if the removal means recovery and immunity, it might not be possible to identify those who are immune. The models are similar in their epidemiological part, but differ in how the removed/recovered individuals are treated. The differences in models affect choice of the strategy only for very cheap treatment and slow spreading disease. However for the combinations of parameters that are important from the epidemiological perspective (high infectiousness and expensive treatment) the models give similar results. Moreover, even where the choice of the strategy is different, the total cost spent on controlling the epidemic is very similar for both models. Although regular and small-world networks capture some aspects of the structure of real networks of contacts between people, animals or plants, they do not include the effect of clustering noted in many real-life applications. The use of random clustered networks in epidemiological modelling takes an impor- tant step towards application of the modelling framework to realistic systems. Network topology and in particular clustering also affects the applicability of the control strategy.
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Trimarchi, Biagio. "Distributed Identification of a Network Model for Pandemic Spreading." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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The recent outburst of the Covid-19 epidemic has made clear the necessity of developing dynamical models for the prediction and the control of large scale epidemic systems. Due to the large impact they have on the life of people and on the economy of the countries, it is extremely important to design models that are able to predict the evolution of such complex phenomena. In the thesis a compartmental model for epidemics is developed and implemented in a Python toolbox suited for distributed computation. Based on the proposed model and on available data, an identification algorithm, based on a gradient free descent method, is proposed to find model parameters that best fit the data. The distributed nature of the system allows for the implementation of a scheme in which computation is distributed among different spatial regions.
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Pinto, Eduardo Ribeiro. "Estudo da dinâmica de epidemias em Redes Complexas." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/153846.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Os Modelos Baseados em Indivíduos (MBI’s) têm sido crescentemente empregados na modelagem de processos infecciosos. Um MBI consiste de uma estrutura na qual ocorrem interações entre um certo número de indivíduos, cujo comportamento é determinado por um conjunto de características que evoluem estocasticamente no tempo. Estudos recentes têm mostrado que as redes complexas constituem um suporte natural para o estudo da propagação de uma doença. Redes complexas são descritas por um conjunto de vértices (nós), arestas (conexões, ligações ou links) e algum tipo de interação entre os mesmos. Na formulação original do MBI e em modelos SIR (Suscetível, Infectado e Recuperado) e SEI (Suscetível, Exposto e Infectado), as relações entre os indivíduos são representadas por grafos completos, ou seja, todos os indivíduos estão conectados entre si. Como a topologia de uma rede real não pode ser descrita por uma rede puramente aleatória, nesse trabalho o MBI foi implementado de forma a incorporar modelos mais realísticos de redes de contato na propagação de uma doença infecciosa. De maneira geral, observou-se que redes complexas com diferentes topologias resultam em curvas de indivíduos suscetíveis, infectados e recuperados (ou suscetíveis, expostos e infectados) com diferentes comportamentos, e desta forma, que a evolução de uma dada doença, em particular a tuberculose, é altamente sensível à topologia de rede utilizada. Mais especificamente, observou-se que quanto maior o valor do comprimento do salto médio, mais rápida será a propagação da doença e, consequentemente, maior será o número de indivíduos infectados.
Individual-Based Models have been increasingly employed in the modeling of an infectious process. An IBM consists of a structure in which interactions occur between a certain number of individuals, whose behavior is determined by a set of characteristics that evolve stochastically in time. Recent studies have shown that complex networks are a natural framework for the study of a disease spread. Complex networks are described by a set of vertices (or nodes), edges (connections or links) and some type of interactions between them. In the original IBM approach and in SIR (Susceptible, Infected, Recovered) and SEI (Susceptible, Exposed and Infected) models, the relations between individuals are represented by complete graphs, that is, all individuals are connected to each other. Since the topology of a real network can not be described by a purely random network, in this work an IBM has been implemented in order to incorporate some realistic contact networks xvii models. In general, it was observed that complex networks with different topologies correspond to curves of susceptible, infected and recovered individuals (or susceptible, exposed and infected) with different behaviors, and thus, that the evolution of a given disease, in particular tuberculosis, is highly sensitive to a network topology. More specifically, it was observed that the higher the value of the mean jump length is, the faster the disease spreads and consequently, the higher is the number of infected individuals.
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Ismailov, Alexej. "Network Monitoring in Delay Tolerant Network." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174053.
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A Disruption Tolerant Network (DTN) is a sparse network where connectivity is regulated by the proximity of mobile nodes. Connections are sporadic and the delivery rate is closely related to node movement. As network resources often are limited in such settings, it is useful to monitor the network in order to make more efficient communication decisions. This study investigates existing routing protocols and monitoring tools for DTN that best cope with the requirements of a tactical military network. A model is proposed to estimate source to destination delay in DTN. This model is evaluated in a Java-based software simulator called The ONE. In order to match the tactical military environment, two scenarios are constructed. The squad scenario simulates the formation movement pattern of several squads and the hierarchical communication scheme that is maintained in a military context. The other scenario simulates a convoy line movement of a military group during transportation. The results of this study show that the proposed mechanism can improve delivery rate and reduce network overhead in settings with strict buffer limitations. The estimation worked best in scenarios that contained some patterns of movement or communication. These patterns are resembled in the model's collected data and the model can provide the user with rough estimates of end-to-end delays in the network. Primary use of this model has been to reduce number of old messages in the network, but other applications like anomaly detection are also discussed in this work.Ett avbrottstolerant nätverk (DTN) är ett glest nät där konnektiviteten avgörs av närheten bland de rörliga noderna i nätverket. Avbrotten i ett sådant nät förekommer ofta och sporadiskt. Eftersom nätverksresurserna oftast är begränsade i sådana sammanhang, så är det lämpligt att övervaka nätverket för att göra det möjligt att fatta mer effektiva kommunikationsbeslut. Det här arbetet undersöker olika routingalgoritmer och övervakningsvektyg för DTN med hänsyn till de krav som ställs av ett taktiskt nät. En modell för att uppskatta fördröjningen från källa till destination är framtagen i arbetet. Modellen är utvärderad med hjälp av en Javabaserad mjukvarusimulator som heter The ONE. För att bäst representera den miljö som uppstår i militära sammanhang är två scenarion framtagna. Det första är ett truppscenario där nodernar rör sig i fromationer och nättrafiken följer den hierarkiska modellen som används i militär kommunikation. Det andra scenariot är ett konvojscenario där enheter marcherar på led. Resultaten från denna studie visar att den föreslagna modellen kan öka andelen levererade meddelanden och minska nätverksbelastningen i en miljö där bufferstorleken hos noderna är begränsad. Uppskattningen visade sig fungera bäst i scenarion som innehöll någon form av mönster bland nodernas rörelse eller deras kommunikation. Dessa mönster återspeglas i modellens insamlade data och modellen kan förse användaren med en grov estimering av slutfördröjningen till alla destinationer i nätet. Modellen har i huvudsak använts till att minska antalet gamla meddelanden i nätet, men arbetet berör även andra användningsområden som anomalidetektion.
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Nassani, Sararose. "An Application of Statistics and Random Graphs to Analyze Local Heroin Markets." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case155440032815001.
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Corley, Courtney David. "Social Network Simulation and Mining Social Media to Advance Epidemiology." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc11053/.
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Traditional Public Health decision-support can benefit from the Web and social media revolution. This dissertation presents approaches to mining social media benefiting public health epidemiology. Through discovery and analysis of trends in Influenza related blogs, a correlation to Centers for Disease Control and Prevention (CDC) influenza-like-illness patient reporting at sentinel health-care providers is verified. A second approach considers personal beliefs of vaccination in social media. A vaccine for human papillomavirus (HPV) was approved by the Food and Drug Administration in May 2006. The virus is present in nearly all cervical cancers and implicated in many throat and oral cancers. Results from automatic sentiment classification of HPV vaccination beliefs are presented which will enable more accurate prediction of the vaccine's population-level impact. Two epidemic models are introduced that embody the intimate social networks related to HPV transmission. Ultimately, aggregating these methodologies with epidemic and social network modeling facilitate effective development of strategies for targeted interventions.
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Schimit, Pedro Henrique Triguis. "Modelagem e controle de propagação de epidemias usando autômatos celulares e teoria de jogos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-05122011-153541/.
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Estuda-se o espalhamento de doenças contagiosas utilizando modelos suscetível-infectado-recuperado (SIR) representados por equações diferenciais ordinárias (EDOs) e por autômatos celulares probabilistas (ACPs) conectados por redes aleatórias. Cada indivíduo (célula) do reticulado do ACP sofre a influência de outros, sendo que a probabilidade de ocorrer interação com os mais próximos é maior. Efetuam-se simulações para investigar como a propagação da doença é afetada pela topologia de acoplamento da população. Comparam-se os resultados numéricos obtidos com o modelo baseado em ACPs aleatoriamente conectados com os resultados obtidos com o modelo descrito por EDOs. Conclui-se que considerar a estrutura topológica da população pode dificultar a caracterização da doença, a partir da observação da evolução temporal do número de infectados. Conclui-se também que isolar alguns infectados causa o mesmo efeito do que isolar muitos suscetíveis. Além disso, analisa-se uma estratégia de vacinação com base em teoria dos jogos. Nesse jogo, o governo tenta minimizar os gastos para controlar a epidemia. Como resultado, o governo realiza campanhas quase-periódicas de vacinação.The spreading of contagious diseases is studied by using susceptible-infected-recovered (SIR) models represented by ordinary differential equations (ODE) and by probabilistic cellular automata (PCA) connected by random networks. Each individual (cell) of the PCA lattice experiences the influence of others, where the probability of occurring interaction with the nearest ones is higher. Simulations for investigating how the disease propagation is affected by the coupling topology of the population are performed. The numerical results obtained with the model based on randomly connected PCA are compared to the results obtained with the model described by ODE. It is concluded that considering the topological structure of the population can pose difficulties for characterizing the disease, from the observation of the time evolution of the number of infected individuals. It is also concluded that isolating a few infected subjects can cause the same effect than isolating many susceptible individuals. Furthermore, a vaccination strategy based on game theory is analyzed. In this game, the government tries to minimize the expenses for controlling the epidemic. As consequence, the government implements quasi-periodic vaccination campaigns.
24
Segovia, Silvero Juan. "Robustness against large-scale failures in communications networks." Doctoral thesis, Universitat de Girona, 2011. http://hdl.handle.net/10803/70008.
Abstract:
This thesis studies robustness against large-scale failures in communications networks. If failures are isolated, they usually go unnoticed by users thanks to recovery mechanisms. However, such mechanisms are not effective against large-scale multiple failures. Large-scale failures may cause huge economic loss. A key requirement towards devising mechanisms to lessen their impact is the ability to evaluate network robustness. This thesis focuses on multilayer networks featuring separated control and data planes. The majority of the existing measures of robustness are unable to capture the true service degradation in such a setting, because they rely on purely topological features. One of the major contributions of this thesis is a new measure of functional robustness. The failure dynamics is modeled from the perspective of epidemic spreading, for which a new epidemic model is proposed. Another contribution is a taxonomy of multiple, large-scale failures, adapted to the needs and usage of the field of networking.Esta tesis estudia la robustez contra fallos de gran escala en redes de comunicaciones. Si los fallos son aislados, usualmente pasan inadvertidos para los usuarios gracias al uso de mecanismos de recuperación. Sin embargo, tales mecanismos no son efectivos contra fallos múltiples de gran escala. Los fallos de gran escala pueden causar grandes pérdidas económicas. Un requisito clave a la hora de diseñar mecanismos efectivos para reducir los efectos negativos es la habilidad de evaluar la robustez de la red. Esta tesis se centra en redes multinivel que poseen planos de control y de datos separados. La mayoría de las medidas de robustez existentes no capturan correctamente la verdadera degradación de los servicios en tales escenarios porque basan la evaluación en propiedades puramente topológicas. Una de las contribuciones de esta tesis es una nueva métrica de robustez funcional. La dinámica de los fallos se modela desde la perspectiva de la propagación de epidemias, para lo cual un nuevo modelo epidémico es propuesto. Otra contribución es una taxonomía de los fallos múltiples de gran escala, adaptado a las necesidades y uso del campo de las redes de comunicaciones.
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Gallois, Passat Isabelle. "Analyse de modèles d'évolution sur un réseau, cas d'un système épidémique avec diffusion non locale." Thesis, Cergy-Pontoise, 2015. http://www.theses.fr/2015CERG0786.
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Cette thèse porte sur l'analyse mathématique de modèles d'évolution sur des réseaux. La thèse se compose de trois chapitres. Les deux premiers portent sur un modèle de propagation d'épidémies dans des réseaux et le troisième porte sur l'équation de Price, qui intervient dans la modélisation de la croissance des réseaux complexes.L'essentiel de la thèse est constituée des deux premiers chapitres, où nous proposons et analysons un modèle épidémique de type SIS avec diffusion non locale. Ce modèle est obtenu à partir d'un modèle discret, en supposant que le degré des noeuds du réseau est ici une variable continue à valeurs positives. Le réseau est ainsi modélisé par la distribution de probabilité des degrés des noeuds du réseau, où a lieu la transmission épidémique. La migration le long des arêtes du réseau correspond à une diffusion non locale. Le système d'évolution en temps pour les densités d'individus sains et infectés se ramène ainsi à un système couplé d'équations différentielles non linéaires avec des termes non locaux, qui sont des moyennes sur le réseau de ces densités. Nous analysons ce système d'évolution, en étudiant successivement le cas d'une transmission limitée (Chapitre 1) et non limitée (Chapitre 2).Nous prouvons tout d'abord rigoureusement l'existence d'une unique solution, locale ou globale, par une méthode de point fixe. Nous établissons ensuite des conditions de seuils nécessaires et suffisantes pour l'existence d'un équilibre endémique. Puis nous étudions la stabilité linéaire des équilibres sain et endémique et comparons nos résultats à ceux obtenus sur le modèle discret de départ. Dans le cas de la transmission limitée et de coefficients de diffusion égaux, on se ramène à une équation de type Fisher avec diffusion non locale, pour laquelle on établit un principe de comparaison. Ceci nous permet d'étudier le comportement asymptotique en temps pour des données initiales quelconques.Le dernier chapitre porte sur l'équation de Price, qui est un modèle de croissance des réseaux.Il se présente sous la forme d'une relation de récurrence donnant l'évolution de la distribution des degrés dans un réseau de taille croissante. Nous montrons rigoureusement la convergence de la solution du modèle de Price vers la solution stationnaire et nous montrons que celle-ci est équivalente à une loi puissance, dont nous précisons l'exposantThis thesis is devoted to the mathematic analysis of time-dependent models on complex networks. There are three chapters. The first two chapters concern a model for the spread of epidemics on networks while the third chapter concerns Price equation, which arises as a model for the growth of complex networks.Most part of this thesis is concentrated in the first two chapters, in which we propose and analyze a SIS-type epidemic model with nonlocal diffusion. This model is derived from a discrete model, by considering here the degree as a continuous variable taking nonnegative values. Hence the network is described by the degree distribution of its nodes, where the epidemic transmission takes place. Migration occurs along the edges of the network and corresponds to nonlocal diffusion. The evolution system for the density of susceptible and infected individuals reads as a coupled system of nonlinear equations with nonlocal terms, which are given by the mean values of these densities on the network. We provide the analysis of this time-dependent system, distinguishing the cases of limited transmision (chapter 1) and illimited transmission (chapter 2).We first rigorously prove the existence of a unique solution to the system, either locally or globally in time, using a fixed point method. Next we establish necessary and sufficient threshold conditions for the existence of an endemic equilibrium. We then investigate the linear stability of both the disease-free and the endemic equilibrium and compare our results to the ones obtained for the discrete system. In the case of equal diffusivities and illimited transmission, we reduce the system to a Fisher-type equation with nonlocal diffusion, for which we prove a comparison principle. This allows us to study the large-time asymptotics of the solution for arbitrary initial data.The last chapter deals with Price equation, which is a model for the growth of networks. The model reads as a discrete recursive equation that provides the time-evolution of the probability distribution of the degrees in a growing network. We show rigorously that the solution converges to a stationary state exhibiting a power-law tail, whose exponent is explicitly given
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Meharouech, Ali Amira. "Wireless body-to-body sensor networks : optimization models and algorithms." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB122/document.
Abstract:
Motivés par la demande croissante de services de santé améliorés et à distance, qui tend à augmenter notamment avec une population de plus en plus âgée, et la réduction du coût de l'utilisation des infrastructures réseaux, afin d'assurer des applications de santé temps-réel et à faible débit de données, les réseaux de capteurs médicaux sans fil (WBANs) forment encore un domaine de recherche en forte croissance, notamment avec le développement de WBANs coopératifs. Dans ce contexte, en utilisant les utilisateurs du réseau eux-mêmes en tant que relais on pourrait étendre les infrastructures réseaux existantes, tout en améliorant la capacité du réseau et optimisant l'utilisation du spectre radio. Ainsi, les opérateurs réseaux, qui planifient déjà pour l'intégration de l'internet des objets (IoT) et l'informatique en nuage (cloud), devraient aussi penser à créer un nouveau type de réseau ad hoc mobile, où les utilisateurs du réseau sont utilisés comme des stations de base ad hoc simplifiées, afin de partager l'information en temps-réel entre des personnes colocalisées portant des capteurs corporels. Ce nouveau type de réseau est appelé réseau corporel sans fil (BBN: Body-to-Body Network). Dans un BBN, un appareil radio, collecte les données des nœuds capteurs attachés ou portés par une personne, et les transmet à un appareil récepteur situé sur une autre personne du voisinage, afin d'être traitées ou retransmises à d'autres utilisateurs du BBN. le BBN peut trouver des applications dans divers domaines tels que la santé, les sports d'équipe, le militaire, les divertissements, ainsi que des expériences passionnantes des réseaux sociaux. Fonctionnant dans la bande Industrielle, Scientifique et Médicale (ISM), les liaisons de communication dans un BBN seront très sensibles aux interférences entre les différentes technologies qui partagent le spectre radio limité. Ainsi, l'interférence entre ces technologies devient une préoccupation importante pour la conception de protocoles pour l'utilisateur final du BBN. À ce jour, très peu d'études existent, qui effectuent une analyse en profondeur de ce type de scénario implicant le corps humain dans des communications radio. Le problème d'interférence dans un tel système distribué, doit être abordé avec des mécanismes distribués, tels que la théorie des jeux. Les décideurs dans le jeu sont soit les WBANs formant le BBN ou les opérateurs de réseaux qui contrôlent les dispositifs de communication inter-WBAN. Ces dispositifs doivent faire face à des ressources de transmission limitées (bande ISM) ce qui donne lieu à des conflits d'intérêts. Cette thèse vise à explorer les opportunités pour permettre des communications inter-WBANs en assurant le partage du spectre radio par le biais de deux approches. D'abord, l'atténuation des interférences mutuelles et croisées, et par la conception d'un protocole de routage spécifique BBN utilisé dans une application de contrôle de l'expansion d'une épidémie dans les zones de rassemblement de masse, tels que les aéroports. Dans un premier volet, une approche basée sur la théorie des jeux est proposée pour résoudre le problème d'interférence distribué dans les BBNs. Le jeu d'atténuation des interférences socialement conscient des intérêts de la collectivité (SIM) a une double tâche: à l'échelle WBAN, il alloue des canaux ZigBee aux capteurs corporels pour la collecte intra-WBAN des données, et à l'échelle BBN, il alloue les canaux WiFi aux appareils mobiles pour la transmission et le relais des données inter-WBANs. Deux algorithmes, BR-SIM et SORT-SIM, ont été développés pour rechercher les points d'équilibre de Nash du jeu SIM. Le premier (BR-SIM) assure les solutions de meilleure réponse (Best-response) tandis que le second (SORT-SIM) tente d'obtenir un compromis entre des solutions quasi-optimales et un temps de convergence réduit. (...)Motivated by the rising demand for remote and improved healthcare, while decreasing the cost of using network infrastructures to ensure time and data rate-constrained applications, Wireless Body Area Networks (WBANs) still form a strongly growing research field. Besides, engineers and researchers are investigating new solutions to supplement mobile communications through developing opportunities for cooperative WBANs. In this context, using network users themselves as relays could complement and extend existing infrastructure networks, while improving network capacity and promoting radio spectrum usage. Yet, network operators, that are already planning for the Internet of Things (IoT) and cloud computing technologies integration, should also think about this new possibility of creating a new type of mobile ad hoc network, where network users themselves are used as simplified ad hoc base stations, to fulfill the desire of sharing real-time information between colocated persons carrying body sensors. This emerging type of network is called Body-to-Body Network (BBN). In a BBN, a radio device situated on one person gathers the sensor data from the sensor nodes worn by that person, and transmit them to a transceiver situated on another person in the nearby area, in order to be processed or relayed to other BBN users. BBNs can find applications in a range of areas such as healthcare, team sports, military, entertainment, as well as exciting social networking experiences. Operating in the popular Industrial, Scientific and Medical (ISM) band, the communication links in a BBN will be heavily susceptible to interference between the different radio technologies sharing the limited radio spectrum. Thus, inter-body interference become an important concern for protocol design and quality of service for the BBN end user. Yet, higher layer MAC and networking mechanisms need to be in place to overcome this interference problem. To date, very few studies, that perform in-depth analysis of this type of body-centric scenario, exist. The interference problem in such distributed system, should be tackeled with distributed mechanisms, such as Game Theory. The decision makers in the game are either the WBANs/people forming the BBN or the network operators who control the inter-WBAN communicating devices. These devices have to cope with a limited transmission resource (ISM band) that gives rise to a conflict of interests. This thesis aims at exploring the opportunities to enable inter-WBAN communications by ensuring feasible sharing of the radio spectrum through two challenging research issues. First, mutual and cross-technology interference mitigation, and second, the design of a BBN specific routing protocol applied to an epidemic control application within mass gathering areas, such as the airport, as use case in this thesis. In a first phase, a game theoretical approach is proposed to resolve the distributed interference problem in BBNs. The Socially-aware Interference Mitigation (SIM) game performs twofold: at the WBAN stage, it allocates ZigBee channels to body sensors for intra-WBAN data sensing, and at the BBN stage, it allocates WiFi channels to mobile devices for inter-WBAN data transmitting and relaying. Two algorithms, BR-SIM and SORT-SIM, were developed to search for Nash equilibra to the SIM game. The first (BR-SIM) ensures best response solutions while the second (SORT-SIM) attempts to achieve tradeoff between sub-optimal solutions and short convergence time. Then, in order to highlight the social role of BBNs, the second part of this thesis is devoted to propose an epidemic control application tailored to BBNs, in indoor environment. This application implements a geographic routing protocol, that differentiates WBANs traffic and ensures real-time quarantine strategies. (...)
27
Meharouech, Ali Amira. "Wireless body-to-body sensor networks : optimization models and algorithms." Electronic Thesis or Diss., Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB122.
Abstract:
Motivés par la demande croissante de services de santé améliorés et à distance, qui tend à augmenter notamment avec une population de plus en plus âgée, et la réduction du coût de l'utilisation des infrastructures réseaux, afin d'assurer des applications de santé temps-réel et à faible débit de données, les réseaux de capteurs médicaux sans fil (WBANs) forment encore un domaine de recherche en forte croissance, notamment avec le développement de WBANs coopératifs. Dans ce contexte, en utilisant les utilisateurs du réseau eux-mêmes en tant que relais on pourrait étendre les infrastructures réseaux existantes, tout en améliorant la capacité du réseau et optimisant l'utilisation du spectre radio. Ainsi, les opérateurs réseaux, qui planifient déjà pour l'intégration de l'internet des objets (IoT) et l'informatique en nuage (cloud), devraient aussi penser à créer un nouveau type de réseau ad hoc mobile, où les utilisateurs du réseau sont utilisés comme des stations de base ad hoc simplifiées, afin de partager l'information en temps-réel entre des personnes colocalisées portant des capteurs corporels. Ce nouveau type de réseau est appelé réseau corporel sans fil (BBN: Body-to-Body Network). Dans un BBN, un appareil radio, collecte les données des nœuds capteurs attachés ou portés par une personne, et les transmet à un appareil récepteur situé sur une autre personne du voisinage, afin d'être traitées ou retransmises à d'autres utilisateurs du BBN. le BBN peut trouver des applications dans divers domaines tels que la santé, les sports d'équipe, le militaire, les divertissements, ainsi que des expériences passionnantes des réseaux sociaux. Fonctionnant dans la bande Industrielle, Scientifique et Médicale (ISM), les liaisons de communication dans un BBN seront très sensibles aux interférences entre les différentes technologies qui partagent le spectre radio limité. Ainsi, l'interférence entre ces technologies devient une préoccupation importante pour la conception de protocoles pour l'utilisateur final du BBN. À ce jour, très peu d'études existent, qui effectuent une analyse en profondeur de ce type de scénario implicant le corps humain dans des communications radio. Le problème d'interférence dans un tel système distribué, doit être abordé avec des mécanismes distribués, tels que la théorie des jeux. Les décideurs dans le jeu sont soit les WBANs formant le BBN ou les opérateurs de réseaux qui contrôlent les dispositifs de communication inter-WBAN. Ces dispositifs doivent faire face à des ressources de transmission limitées (bande ISM) ce qui donne lieu à des conflits d'intérêts. Cette thèse vise à explorer les opportunités pour permettre des communications inter-WBANs en assurant le partage du spectre radio par le biais de deux approches. D'abord, l'atténuation des interférences mutuelles et croisées, et par la conception d'un protocole de routage spécifique BBN utilisé dans une application de contrôle de l'expansion d'une épidémie dans les zones de rassemblement de masse, tels que les aéroports. Dans un premier volet, une approche basée sur la théorie des jeux est proposée pour résoudre le problème d'interférence distribué dans les BBNs. Le jeu d'atténuation des interférences socialement conscient des intérêts de la collectivité (SIM) a une double tâche: à l'échelle WBAN, il alloue des canaux ZigBee aux capteurs corporels pour la collecte intra-WBAN des données, et à l'échelle BBN, il alloue les canaux WiFi aux appareils mobiles pour la transmission et le relais des données inter-WBANs. Deux algorithmes, BR-SIM et SORT-SIM, ont été développés pour rechercher les points d'équilibre de Nash du jeu SIM. Le premier (BR-SIM) assure les solutions de meilleure réponse (Best-response) tandis que le second (SORT-SIM) tente d'obtenir un compromis entre des solutions quasi-optimales et un temps de convergence réduit. (...)Motivated by the rising demand for remote and improved healthcare, while decreasing the cost of using network infrastructures to ensure time and data rate-constrained applications, Wireless Body Area Networks (WBANs) still form a strongly growing research field. Besides, engineers and researchers are investigating new solutions to supplement mobile communications through developing opportunities for cooperative WBANs. In this context, using network users themselves as relays could complement and extend existing infrastructure networks, while improving network capacity and promoting radio spectrum usage. Yet, network operators, that are already planning for the Internet of Things (IoT) and cloud computing technologies integration, should also think about this new possibility of creating a new type of mobile ad hoc network, where network users themselves are used as simplified ad hoc base stations, to fulfill the desire of sharing real-time information between colocated persons carrying body sensors. This emerging type of network is called Body-to-Body Network (BBN). In a BBN, a radio device situated on one person gathers the sensor data from the sensor nodes worn by that person, and transmit them to a transceiver situated on another person in the nearby area, in order to be processed or relayed to other BBN users. BBNs can find applications in a range of areas such as healthcare, team sports, military, entertainment, as well as exciting social networking experiences. Operating in the popular Industrial, Scientific and Medical (ISM) band, the communication links in a BBN will be heavily susceptible to interference between the different radio technologies sharing the limited radio spectrum. Thus, inter-body interference become an important concern for protocol design and quality of service for the BBN end user. Yet, higher layer MAC and networking mechanisms need to be in place to overcome this interference problem. To date, very few studies, that perform in-depth analysis of this type of body-centric scenario, exist. The interference problem in such distributed system, should be tackeled with distributed mechanisms, such as Game Theory. The decision makers in the game are either the WBANs/people forming the BBN or the network operators who control the inter-WBAN communicating devices. These devices have to cope with a limited transmission resource (ISM band) that gives rise to a conflict of interests. This thesis aims at exploring the opportunities to enable inter-WBAN communications by ensuring feasible sharing of the radio spectrum through two challenging research issues. First, mutual and cross-technology interference mitigation, and second, the design of a BBN specific routing protocol applied to an epidemic control application within mass gathering areas, such as the airport, as use case in this thesis. In a first phase, a game theoretical approach is proposed to resolve the distributed interference problem in BBNs. The Socially-aware Interference Mitigation (SIM) game performs twofold: at the WBAN stage, it allocates ZigBee channels to body sensors for intra-WBAN data sensing, and at the BBN stage, it allocates WiFi channels to mobile devices for inter-WBAN data transmitting and relaying. Two algorithms, BR-SIM and SORT-SIM, were developed to search for Nash equilibra to the SIM game. The first (BR-SIM) ensures best response solutions while the second (SORT-SIM) attempts to achieve tradeoff between sub-optimal solutions and short convergence time. Then, in order to highlight the social role of BBNs, the second part of this thesis is devoted to propose an epidemic control application tailored to BBNs, in indoor environment. This application implements a geographic routing protocol, that differentiates WBANs traffic and ensures real-time quarantine strategies. (...)
28
Hui, Zi. "Spatial structure of complex network and diffusion dynamics." Thesis, Le Mans, 2013. http://www.theses.fr/2013LEMA1005/document.
Abstract:
Dans le développement récent des sciences de réseau, réseaux contraints spatiales sont devenues un objet d'une enquête approfondie. Spatiales des réseaux de contraintes sont intégrées dans l'espace de configuration. Leurs structures et les dynamiques sont influencées par la distance spatiale. Ceci est prouvé par les données empiriques de plus en plus sur des systèmes réels montrant des lois exponentielles ou de distribution d'énergie distance spatiale de liens. Dans cette thèse, nous nous concentrons sur la structure de réseau spatial avec une distribution en loi de puissance spatiale. Plusieurs mécanismes de formation de la structure et de la dynamique de diffusion sur ces réseaux sont pris en considération. D'abord, nous proposons un réseau évolutif construit en l'espace de configuration d'un mécanisme de concurrence entre le degré et les préférences de distance spatiale. Ce mécanisme est décrit par un a^'fc- + (1 — a)^'lL_,1, où ki est le degré du noeud i et rni est la distance spatiale entre les noeuds n et i. En réglant le paramètre a, le réseau peut être fait pour changer en continu à partir du réseau spatiale entraînée (a = 0) pour le réseau sans échelle (a = 1). La structure topologique de notre modèle est comparé aux données empiriques de réseau de courrier électronique avec un bon accord. Sur cette base, nous nous concentrons sur la dynamique de diffusion sur le réseau axé sur spatiale (a — 0). Le premier modèle, nous avons utilisé est fréquemment employée dans l'étude de la propagation de l'épidémie: ['spatiale susceptible-infecté-susceptible (SIS) modèle. Ici, le taux de propagation entre deux noeuds connectés est inversement proportionnelle à leur distance spatiale. Le résultat montre que la diffusion efficace de temps augmente avec l'augmentation de a. L'existence d'seuil épidémique générique est observée, dont la valeur dépend du paramètre a Le seuil épidémique maximum et le ratio minimum fixe de noeuds infectés localiser simultanément dans le intervalle 1.5 < a < 2.Puisque le réseau spatiale axée a bien défini la distance spatiale, ce modèle offre une occasion d'étudier la dynamique de diffusion en utilisant les techniques habituelles de la mécanique statistique. Tout d'abord, compte tenu du fait que la diffusion est anormale en général en raison de l'importante long plage de propagation, nous introduisons un coefficient de diffusion composite qui est la somme de la diffusion d'habitude constante D des lois de la Fick appliqué sur différentes distances de transfert possibles sur le réseau. Comme prévu, ce coefficient composite diminue avec l'augmentation de a. et est une bonne mesure de l'efficacité de la diffusion. Notre seconde approche pour cette diffusion anormale est de calculer le déplacement quadratique moyen (l²) à identifier une constante de diffusion D' et le degré de la anomalousness y avec l'aide de la loi de puissance (l²) = 4D'ty. D' comportements de la même manière que D, i.e.. elle diminue avec l'augmentation de a. y est inférieur à l'unité (subdiffusion) et tend à un (diffusion normale) que a augmenteIn the recent development of network sciences, spatial constrained networks have become an object of extensive investigation. Spatial constrained networks are embedded in configuration space. Their structures and dynamics are influenced by spatial distance. This is proved by more and more empirical data on real Systems showing exponential or power laws spatial distance distribution of links. In this dissertation, we focus on the structure of spatial network with power law spatial distribution. Several mechanisms of structure formation and diffusion dynamics on these networks are considered. First we propose an evolutionary network constructed in the configuration space with a competing mechanism between the degree and the spatial distance preferences. This mechanism is described by a ki + (1 — a), where ki is the degree of node i and rni is the spatial distance between nodes n and i. By adjusting parameter a, the network can be made to change continuously from the spatial driven network (a = 0) to the scale-free network (a = 1). The topological structure of our model is compared to the empirical data from email network with good agreement. On this basis, we focus on the diffusion dynamics on spatial driven network (a = 0). The first model we used is frequently employed in the study of epidemie spreading : the spatial susceptible-infected-susceptible (SIS) model. Here the spreading rate between two connected nodes is inversely proportional to their spatial distance. The result shows that the effective spreading time increases with increasing a. The existence of generic epidemic threshold is observed, whose value dépends on parameter a. The maximum épidemic threshold and the minimum stationary ratio of infected nodes simultaneously locate in the interval 1.5 < a < 2. Since the spatial driven network has well defined spatial distance, this model offers an occasion to study the diffusion dynamics by using the usual techniques of statistical mechanics. First, considering the fact that the diffusion is anomalous in general due to the important long-range spreading, we introduce a composite diffusion coefficient which is the sum of the usual diffusion constant D of the Fick's laws applied over different possible transfer distances on the network. As expected, this composite coefficient decreases with increasing a and is a good measure of the efficiency of the diffusion. Our second approach to this anomalous diffusion is to calculate the mean square displacement (l²) to identify a diffusion constant D' and the degree of thé anomalousness y with the help of the power law {l²} = 4D'ty. D' behaviors in the same way as D, i.e., it decreases with increasing a. y is smaller than unity (subdiffusion) and tends to one (normal diffusion) as a increases
29
Kandhway, Kundan. "Optimal Control of Information Epidemics in Homogeneously And Heterogeneously Mixed Populations." Thesis, 2016. http://etd.iisc.ac.in/handle/2005/2670.
Abstract:
Social networks play an important role in disseminating a piece of information in a population. Companies advertising a newly launched product, movie promotion, political campaigns, social awareness campaigns by governments, charity campaigns by NGOs and crowd funding campaigns by entrepreneurs are a few examples where an entity is interested in disseminating a piece of information in a target population, possibly under resource constraints.
In this thesis we model information diffusion in a population using various epidemic models and study optimal campaigning strategies to maximize the reach of information. In the different problems considered in this thesis, information epidemics are modeled as the Susceptible-Infected, Susceptible-Infected-Susceptible, Susceptible-Infected-Recovered and Maki Thompson epidemic processes; however, we modify the models to incorporate the intervention made by the campaigner to enhance information propagation. Direct recruitment of individuals as spreaders and providing word-of-mouth incentives to the spreaders are considered as two intervention strategies (controls) to enhance the speed of information propagation. These controls can be implemented by placing advertisements in the mass media, announcing referral/cash back rewards for introducing friends to a product or service being advertised etc.
In the different problems considered in this thesis, social contacts are modeled with varying levels of complexity---population is homogeneously mixed or follows heterogeneous mixing. The solutions to the problems which consider homogeneous mixing of individuals identify the most important periods in the campaign duration which should be allocated more resources to maximize the reach of the message, depending on the system parameters of the epidemic model (e.g., epidemics with high and low virulence). When a heterogeneous model is considered, apart from this, the solution identifies the important classes of individuals which should be allocated more resources depending upon the network considered (e.g. Erdos-Renyi, scale-free) and model parameters. These classes may be carved out based on various centrality measures in the network. If multiple strategies are available for campaigning, the solution also identifies the relative importance of the strategies depending on the network type.
We study variants of the optimal campaigning problem where we optimize different objective functions. For some of the formulated problems, we discuss the existence and uniqueness of the solution. Sometimes our formulations call for novel techniques to prove the existence of a solution.
30
Kandhway, Kundan. "Optimal Control of Information Epidemics in Homogeneously And Heterogeneously Mixed Populations." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2670.
Abstract:
Social networks play an important role in disseminating a piece of information in a population. Companies advertising a newly launched product, movie promotion, political campaigns, social awareness campaigns by governments, charity campaigns by NGOs and crowd funding campaigns by entrepreneurs are a few examples where an entity is interested in disseminating a piece of information in a target population, possibly under resource constraints.
In this thesis we model information diffusion in a population using various epidemic models and study optimal campaigning strategies to maximize the reach of information. In the different problems considered in this thesis, information epidemics are modeled as the Susceptible-Infected, Susceptible-Infected-Susceptible, Susceptible-Infected-Recovered and Maki Thompson epidemic processes; however, we modify the models to incorporate the intervention made by the campaigner to enhance information propagation. Direct recruitment of individuals as spreaders and providing word-of-mouth incentives to the spreaders are considered as two intervention strategies (controls) to enhance the speed of information propagation. These controls can be implemented by placing advertisements in the mass media, announcing referral/cash back rewards for introducing friends to a product or service being advertised etc.
In the different problems considered in this thesis, social contacts are modeled with varying levels of complexity---population is homogeneously mixed or follows heterogeneous mixing. The solutions to the problems which consider homogeneous mixing of individuals identify the most important periods in the campaign duration which should be allocated more resources to maximize the reach of the message, depending on the system parameters of the epidemic model (e.g., epidemics with high and low virulence). When a heterogeneous model is considered, apart from this, the solution identifies the important classes of individuals which should be allocated more resources depending upon the network considered (e.g. Erdos-Renyi, scale-free) and model parameters. These classes may be carved out based on various centrality measures in the network. If multiple strategies are available for campaigning, the solution also identifies the relative importance of the strategies depending on the network type.
We study variants of the optimal campaigning problem where we optimize different objective functions. For some of the formulated problems, we discuss the existence and uniqueness of the solution. Sometimes our formulations call for novel techniques to prove the existence of a solution.
31
Lin, Yu-Chen, and 林煜程. "Epidemic Model in Well-Mixed Multiplex Network with Distributed Time Delay." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5bvh2e.
Abstract:
碩士國立交通大學
應用數學系數學建模與科學計算碩士班
105
In this paper, we consider an epidemic model in well-mixed multiplex networks with distributed time delay. Specifically, the model consists of two layers of well-mixed networks, where two diffusive processes on the same individual interacting and affecting each other. We assume that there is a distributed time delay for an individual getting infected and no delay for an individual changing one’s status from unawareness to awareness. There are three possible equilibria E1, E2 and E3, called disease and information free equilibrium, disease free and information saturated equilibrium and endemic and information saturated equilibrium, in this model. Our main result contain the following. First, we prove the uniform persistence of the model for parameter region yielding E3. Second, it is shown that E1 is globally stable for any time delay. Third, we prove that E2 is globally stable for any time delay. Finally, With help of the uniform persistence, we shoe that there exists a H*>0 such that E3 is globally stable for time delay less than H*.
32
Huang, Yi-Jie, and 黃義傑. "A study of a network-based SIS epidemic model with saturated treatment rate." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/w43b3u.
Abstract:
碩士國立高雄師範大學
數學系
105
In recent years, infectious diseases have become a serious problem and have increased morbidity and mortality of individuals around the world. Thus, studying the spreading dynamics of the infectious diseases is a relevant issue. In this thesis, we will investigate an epidemic model which is composed of a system of ordinary differential equations. To fit for reality, complex network topology and saturated incidence rate are incorporated into the model. Besides, we know that the resources of treatment may be limited, and this implies that the recovery from infective individuals will reach a maximum. Therefore, we will study a network-based SIS epidemic model with saturated incidence and treatment rates. Existence and stability of the equilibria of the epidemic model will be analyzed. Interestingly, we find that incorporating the saturated treatment rate may cause the existence of two endemic equilibria. Numerical simulations will be given to demonstrate the theoretical results.
33
Shiller, Elisabeth. "EVOLVING CONTACT NETWORKS TO ANALYZE EPIDEMIC BEHAVIOUR AND STUDYING THE EFFECTS OF VACCINATION." Thesis, 2012. http://hdl.handle.net/10214/5266.
Abstract:
Epidemic models help researchers understand and predict the nature of a potential epidemic. This study analyzes and improves network evolution technology that evolves contact networks so that simulated epidemics on the network mimic a specified epidemic pattern. The evolutionary algorithm incorporates the novel recentering-restarting algorithm, which is adopted into the optimizer to allow for efficient search of the space of networks. It also implements the toggle-delete representation which allows for broader search of solution space. Then, a diffusion character based method is used for analyzing the contact networks. A comparison of simulated epidemics that result from changing patient zero for a single contact network is performed. It is found that the location of patient zero is important for the behaviour of an epidemic. The social fabric representation is invented and then tested for parameter choices. The response to vaccination strategies (including ring vaccination) is then tested by incorporating them into the epidemic simulations.Ontario Graduate Scholarship (OGS), Natural Sciences and Engineering Research Council of Canada (NSERC)
34
"Study of an Epidemic Multiple Behavior Diffusion Model in a Resource Constrained Social Network." Master's thesis, 2013. http://hdl.handle.net/2286/R.I.20967.
Abstract:
abstract: In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.Dissertation/Thesis
M.S. Computer Science 2013
35
Moraes, Alvaro. "Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models." Diss., 2015. http://hdl.handle.net/10754/344375.
Abstract:
Epidemics have shaped, sometimes more than wars and natural disasters, demo- graphic aspects of human populations around the world, their health habits and their economies. Ebola and the Middle East Respiratory Syndrome (MERS) are clear and current examples of potential hazards at planetary scale.
During the spread of an epidemic disease, there are phenomena, like the sudden extinction of the epidemic, that can not be captured by deterministic models. As a consequence, stochastic models have been proposed during the last decades. A typical forward problem in the stochastic setting could be the approximation of the expected number of infected individuals found in one month from now. On the other hand, a typical inverse problem could be, given a discretely observed set of epidemiological data, infer the transmission rate of the epidemic or its basic reproduction number.
Markovian epidemic models are stochastic models belonging to a wide class of pure jump processes known as Stochastic Reaction Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions but they also have applications in neural networks, virus kinetics, and dynamics of social networks, among others.
4
This PhD thesis is focused on novel fast simulation algorithms and statistical
inference methods for SRNs.
Our novel Multi-level Monte Carlo (MLMC) hybrid simulation algorithms provide
accurate estimates of expected values of a given observable of SRNs at a prescribed final time. They are designed to control the global approximation error up to a user-selected accuracy and up to a certain confidence level, and with near optimal computational work. We also present novel dual-weighted residual expansions for fast estimation of weak and strong errors arising from the MLMC methodology.
Regarding the statistical inference aspect, we first mention an innovative multi- scale approach, where we introduce a deterministic systematic way of using up-scaled likelihoods for parameter estimation while the statistical fittings are done in the base model through the use of the Master Equation. In a di↵erent approach, we derive a new forward-reverse representation for simulating stochastic bridges between con- secutive observations. This allows us to use the well-known EM Algorithm to infer the reaction rates. The forward-reverse methodology is boosted by an initial phase where, using multi-scale approximation techniques, we provide initial values for the EM Algorithm.
36
Chen, Chi-Hweng, and 陳祈宏. "A distribution model of epidemic prevention goods in epidemic prevention network of the infectious diseases – a case study of Severe Acute Respiratory Syndrome." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/24103452916490972473.
Abstract:
碩士國立雲林科技大學
工業工程與管理研究所碩士班
93
When the infectious diseases outbreak, it always be endemic in a short time because people don’t pay attention to epidemic prevention goods. For example, Severe Acute Respiratory Syndrome causes about eight thousands and four hundreds suspect cases and eight hundreds people death in the world from World Health Organization identified the infectious diseases. In the same time everyone has live in the menace of infectious diseases and no people know what is the best methods of control infectious diseases. And hospitals of medical treatment of infectious diseases don’t have enough epidemic prevention goods and the vendor of epidemic prevention goods also can’t provide good service of epidemic prevention goods distribution. So our research plan focus on “ inventory routing problem “ of epidemic prevention goods. We introduce “ Vendor Management Inventory “ , ”Epidemic Propagation Model” and the “ Fuzzy Theory ” to develop a distribution model of epidemic prevention goods. And we can make sure epidemic prevention goods could be supply enough and management health of medical professional. Through the research outcome, we can find that in the situation of taking total logistic operation cost as master achievement index, we provide distribution model of epidemic prevention goods created by our research is obviously better than the method at present. We also can find that our distribution model of epidemic prevention goods has superior expression in the combinations of different hospitals of medical treatment of infectious diseases quantities and items of epidemic prevention goods quantities scale.
37
Hsu, Hui-Yu, and 許卉瑀. "Analysis of Information Delivery Dynamics in Cognitive Radio Ad Hoc Networks Using Epidemic Models." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/34067141204704547126.
Abstract:
碩士國立臺灣科技大學
資訊工程系
103
Over the past decade cognitive radio (CR) is introduced to increase spectrum efficiency, however it on the other hand burdens the interference control between unlicensed secondary users (SUs) and primary users (PUs). In CR ad hoc networks (CRAHNs), such considerations in interference control become more complicated, where SUs adopt dynamic spectrum access and power adjustment to ensure sufficient operation of PUs, and the inevitably increasing latency poses new challenges on reliability of end-to-end communications. To guarantee operations of primary systems while fully optimizing system performance in CRAHNs, this thesis proposes interference-aware flooding schemes exploiting global timeout and vaccine recovery schemes to control the heavy buffer occupancy induced by packet replications. The information delivery dynamics of SUs under the proposed interference-aware recovery-assisted flooding schemes is analyzed via epidemic models and stochastic geometry from a macroscopic view of the entire system. The simulation results show that our model can efficiently capture the complicated data delivery dynamics in CRAHN in terms of end-to-end transmission reliability and buffer occupancy. Consequently this thesis sheds new light on analysis of recovery-assisted flooding schemes in CRAHN.
38
Bansal, Khandelwal Shweta 1980. "Ecology of infectious diseases with contact networks and percolation theory." Thesis, 2008. http://hdl.handle.net/2152/3910.
39
Παπαφράγκος, Κωνσταντίνος. "Αναπαράσταση και προσομοίωση σύνθετων δικτύων για ανάλυση χαρακτηριστικών ασφαλείας." Thesis, 2013. http://hdl.handle.net/10889/6411.
Abstract:
Βασικό χαρακτηριστικό της σύγχρονης εποχής αποτελεί η ραγδαία αύξηση του
Διαδικτύου τόσο σε επίπεδο χρηστών όσο και σε επίπεδο παρεχόμενων υπηρεσιών.
Συνεπώς, είναι επιτακτική η ανάγκη της προστασίας των δικτυακών και
υπολογιστικών συστημάτων από διάφορες απειλές οι οποίες μπορούν να τα
καταστήσουν τρωτά. Για την πλήρη προστασία όμως αυτών των συστημάτων,
απαιτείται πρώτα η κατανόηση του είδους, της ταυτότητας και του τρόπου διάδοσης
της απειλής. Ιδιαίτερη χρήσιμη έχει αποδειχθεί η ανάπτυξη και αναζήτηση
αξιόπιστων μοντέλων ικανών να περιγράψουν αρκετά αποτελεσματικά τον τρόπο
διάδοσης μιας απειλής. Η αναζήτηση τέτοιων μοντέλων αποτελεί πλέον ένα
σημαντικό τομέα έρευνας στην ακαδημαϊκή και όχι μόνο κοινότητα.
Σκοπός της παρούσας διπλωματικής εργασίας είναι η προσομοίωση και μελέτη
των βασικών επιδημιολογικών μοντέλων SI, SIR, SIS και SIRS. Τα μοντέλα αυτά
είναι εμπνευσμένα από την επιστήμη της Βιολογίας, και πλέον τη σημερινή εποχή
χρησιμοποιούνται ευρέως για τη μοντελοποίηση της διάδοσης αρκετών απειλών στα
δίκτυα υπολογιστών, όπως πχ. οι ιοί και τα σκουλήκια (viruses and worms). Η
εργασία αποτελείται από πέντε κεφάλαια.
Στο πρώτο κεφάλαιο, γίνεται και η παρουσίαση των ασυρμάτων δικτύων
αισθητήρων περιγράφοντας επίσης τόσο τη δομή όσο και τα βασικά χαρακτηριστικά
αυτών.
Στο δεύτερο κεφάλαιο γίνεται μια παρουσίαση των βασικών ειδών του
κακόβουλου λογισμικού που μπορούν να πλήξουν ένα υπολογιστικό σύστημα.
Γίνεται επίσης αναφορά στα χαρακτηριστικά των κακόβουλων λογισμικών τα οποία
επηρεάζουν την εξάπλωσή του.
Το τρίτο κεφάλαιο επιχειρεί να εισάγει την έννοια της επιδημιολογίας στα
συστήματα υπολογιστών με την ανάλυση κυρίως των ιδιαιτεροτήτων οι οποίες την
χαρακτηρίζουν. Επίσης αυτό το κεφάλαιο παρουσιάζει κάποια βασικά
επιδημιολογικά μοντέλα κάνοντας μια αναφορά τόσο στα βασικά χαρακτηριστικά
αυτών, όσο επίσης και στον τρόπο λειτουργίας τους.
Το τέταρτο κεφάλαιο το οποίο είναι και το πιο σημαντικό της εργασίας αυτής,
αφιερώνεται στην παρουσίαση του εργαλείου OPNET Modeler που χρησιμοποιήσαμε
και στην εκτενή περιγραφή της προσομοίωσης των μοντέλων SI, SIS, SIR και SIRS
που διεξήγαμε για ένα ασύρματο δίκτυο αισθητήρων. Γίνεται παρουσίαση της
λειτουργίας του δικτύου με ταυτόχρονη επεξήγηση του κώδικα που αναπτύχθηκε.
Επιπλέον παρουσιάζονται και αναλύονται τα αποτελέσματα της προσομοίωσης ενώ
παράλληλα περιγράφονται και τα συμπεράσματα στα οποία μας οδήγησε η εν λόγω
προσομοίωση.
Τέλος, στο πέμπτο κεφάλαιο, γίνεται μια αναφορά σε κάποια βασικά
συμπεράσματα στα οποία οδηγηθήκαμε, ενώ περιγράφονται και πεδία πάνω στη
μελέτη της διάδοσης ενός κακόβουλου λογισμικού σε ένα υπολογιστικό δίκτυο, τα
οποία μπορούν να μελετηθούν εκτενέστερα μελλοντικά.A basic characteristic of contemporary days is the boom of the Internet either in terms of users or in terms of services rendered. Therefore, there is an imperative need to protect the network and computational systems from various threats which can render them vulnerable. However, for the full protection of these systems, it is required in the first place to get to know the type, the identity and the propagation mode of the threat. Of significant use has proved to be the development and the pursuit of models capable of describing quite effectively the way a threat is spread. The pursuit of such models constitutes nowadays a significant sector of research, including, but not limited to the academic community. The intention of the present diploma thesis is the simulation and study of the basic epidemic models SI, SIR, SIS and SIRS. These models are inspired from the science of Biology, and they are widely used nowadays for the modeling of the spread of various threats in computer networks such as viruses and worms. This dissertation consists of five chapters. In the first chapter, there is taking place the presentation of wireless sensor networks and there is also a description of their structure and their basic characteristics. In the second chapter there is a presentation of the basic types of malicious software that can hit a computational system. There is also reference to the characteristics of malicious software that affect their propagation. The third chapter attempts to introduce the concept on epidemiology in computer systems, analyzing mainly the particularities characterizing her. In addition, this chapter presents some basic epidemic models, referring both to their basic characteristics and their mode of operation. The fourth chapter, which is also the most significant one of the present thesis, is dedicated to the presentation of the tool OPNET Modeler that we used too in the thorough description of the simulation of the models SI, SIR, SIS and SIRS that we carried out for a wireless sensor network. It is taking place the presentation of the network’s operation mode with a simultaneous explanation of the code that was developed. Moreover, there are presented and analyzed the results of the simulation when at the same time are also described the conclusions that were derived from the present simulation. Finally, in the fifth chapter, there is a reference to some basic conclusions in which we were led, where there are also described fields concerning the study of malicious software propagation in a computational network, which can studied further in the future.
40
Venkataramanan, Srinivasan. "Influence Dynamics on Social Networks." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/3114.
Abstract:
With online social networks such as Facebook and Twitter becoming globally popular, there is renewed interest in understanding the structural and dynamical properties of social networks. In this thesis we study several stochastic models arising in the context of the spread of influence or information in social networks. Our objective is to provide compact and accurate quantitative descriptions of the spread processes, to understand the effects of various system parameters, and to design policies for the control of such diffusions.
One of the well established models for influence spread in social networks is the threshold model. An individual’s threshold indicates the minimum level of “influence” that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. We begin with the well-known Linear Threshold (LT) model introduced by Kempe et al. [1]. We analytically characterize the expected influence for a given initial set under the LT model, and provide an equivalent interpretation in terms of acyclic path probabilities in a Markov chain. We derive explicit optimal initial sets for some simple networks and also study the effectiveness of the Pagerank [2] algorithm for the problem of influence maximization. Using insights from our analytical characterization, we then propose a computationally efficient G1-sieving algorithm for influence maximization and show that it performs on par with the greedy algorithm, through experiments on a coauthorship dataset.
The Markov chain characterisation gives only limited insights into the dynamics of influence spread and the effects of the various parameters. We next provide such insights in a restricted setting, namely that of a homogeneous version of the LT model but with a general threshold distribution, by taking the fluid limit of a probabilistically scaled version of the spread Markov process. We observe that the threshold distribution features in the fluid limit via its hazard function. We study the effect of various threshold distributions and show that the influence evolution can exhibit qualitatively different behaviors, depending on the threshold distribution, even in a homogeneous setting. We show that under the exponential threshold distribution, the LT model becomes equivalent to the SIR (Susceptible-Infected-Recovered) epidemic model [3]. We also show how our approach is easily amenable to networks with heterogeneous community structures.
Hundreds of millions of people today interact with social networks via their mobile devices. If the peer-to-peer radios on such devices are used, then influence spread and information spread can take place opportunistically when pairs of such devices come in proximity. In this context, we develop a framework for content delivery in mobile opportunistic networks with joint evolution of content popularity and availability. We model the evolution of influence and content spread using a multi-layer controlled epidemic model, and, using the monotonicity properties of the o.d.e.s, prove that a time-threshold policy for copying to relay nodes is delay-cost optimal.
Information spread occurs seldom in isolation on online social networks. Several contents might spread simultaneously, competing for the common resource of user attention. Hence, we turn our attention to the study of competition between content creators for a common population, across multiple social networks, as a non-cooperative game. We characterize the best response function, and observe that it has a threshold structure. We obtain the Nash equilibria and study the effect of cost parameters on the equilibrium budget allocation by the content creators. Another key aspect to capturing competition between contents, is to understand how a single end-user receives and processes content. Most social networks’ interface involves a timeline, a reverse chronological list of contents displayed to the user, similar to an email inbox. We study competition between content creators for visibility on a social network user’s timeline. We study a non-cooperative game among content creators over timelines of fixed size, show that the equilibrium rate of operation under a symmetric setting, exhibits a non-monotonic behavior with increasing number of players. We then consider timelines of infinite size, along with a behavioral model for user’s scanning behavior, while also accounting for variability in quality (influence weight) among content creators. We obtain integral equations, that capture the evolution of average influence of competing contents on a social network user’s timeline, and study various content competition formulations involving quality and quantity.
41
Venkataramanan, Srinivasan. "Influence Dynamics on Social Networks." Thesis, 2014. http://hdl.handle.net/2005/3114.
Abstract:
With online social networks such as Facebook and Twitter becoming globally popular, there is renewed interest in understanding the structural and dynamical properties of social networks. In this thesis we study several stochastic models arising in the context of the spread of influence or information in social networks. Our objective is to provide compact and accurate quantitative descriptions of the spread processes, to understand the effects of various system parameters, and to design policies for the control of such diffusions.
One of the well established models for influence spread in social networks is the threshold model. An individual’s threshold indicates the minimum level of “influence” that must be exerted, by other members of the population engaged in some activity, before the individual will join the activity. We begin with the well-known Linear Threshold (LT) model introduced by Kempe et al. [1]. We analytically characterize the expected influence for a given initial set under the LT model, and provide an equivalent interpretation in terms of acyclic path probabilities in a Markov chain. We derive explicit optimal initial sets for some simple networks and also study the effectiveness of the Pagerank [2] algorithm for the problem of influence maximization. Using insights from our analytical characterization, we then propose a computationally efficient G1-sieving algorithm for influence maximization and show that it performs on par with the greedy algorithm, through experiments on a coauthorship dataset.
The Markov chain characterisation gives only limited insights into the dynamics of influence spread and the effects of the various parameters. We next provide such insights in a restricted setting, namely that of a homogeneous version of the LT model but with a general threshold distribution, by taking the fluid limit of a probabilistically scaled version of the spread Markov process. We observe that the threshold distribution features in the fluid limit via its hazard function. We study the effect of various threshold distributions and show that the influence evolution can exhibit qualitatively different behaviors, depending on the threshold distribution, even in a homogeneous setting. We show that under the exponential threshold distribution, the LT model becomes equivalent to the SIR (Susceptible-Infected-Recovered) epidemic model [3]. We also show how our approach is easily amenable to networks with heterogeneous community structures.
Hundreds of millions of people today interact with social networks via their mobile devices. If the peer-to-peer radios on such devices are used, then influence spread and information spread can take place opportunistically when pairs of such devices come in proximity. In this context, we develop a framework for content delivery in mobile opportunistic networks with joint evolution of content popularity and availability. We model the evolution of influence and content spread using a multi-layer controlled epidemic model, and, using the monotonicity properties of the o.d.e.s, prove that a time-threshold policy for copying to relay nodes is delay-cost optimal.
Information spread occurs seldom in isolation on online social networks. Several contents might spread simultaneously, competing for the common resource of user attention. Hence, we turn our attention to the study of competition between content creators for a common population, across multiple social networks, as a non-cooperative game. We characterize the best response function, and observe that it has a threshold structure. We obtain the Nash equilibria and study the effect of cost parameters on the equilibrium budget allocation by the content creators. Another key aspect to capturing competition between contents, is to understand how a single end-user receives and processes content. Most social networks’ interface involves a timeline, a reverse chronological list of contents displayed to the user, similar to an email inbox. We study competition between content creators for visibility on a social network user’s timeline. We study a non-cooperative game among content creators over timelines of fixed size, show that the equilibrium rate of operation under a symmetric setting, exhibits a non-monotonic behavior with increasing number of players. We then consider timelines of infinite size, along with a behavioral model for user’s scanning behavior, while also accounting for variability in quality (influence weight) among content creators. We obtain integral equations, that capture the evolution of average influence of competing contents on a social network user’s timeline, and study various content competition formulations involving quality and quantity.
42
Tasi, Ming Chi, and 蔡明其. "Using Network-Oriented Epidemic Model to Simulate the Transmission Dynamics of Novel Influenza and Assess the Efficacy of Prevention Intervention Strategies." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/47j2ec.
Abstract:
碩士長庚大學
資訊工程學研究所
96
WHO forecasts that there will be two billion people throughout the world catch the Novel Influenza when it becomes popular again, and the death rate will approach to one hundred percent. Although, we can not predict when and what kind of novel infection will invade Taiwan or how it threaten people’s lives, health seriously. Infection situation report from WHO and CDC shows that the novel infection is more and more close to us without warning. As a result, we need to set up a Novel Influenza Network-Oriented Epidemic Model before it become popular next time. We want to use this model to discuss the Efficacy of Prevention Intervention Strategies, and to know the better applying strategy, testee, and timing. We make use of Bipartite Graph, Social mirror identity in our research to show the relation between people and places; also try to set up the network we use in daily life for special usage on computer modeling. We integrate Daily Contact Network, Multi-agent System and SEIR infection condition model to make a Novel Influenza Network-Oriented Epidemic Model and a related Prevention Intervention Strategies. The research use the cases in the past three seasons in Taiwan as the proving document to prove the model is showing the correct Simulating Transmission Dynamics of seasoning influenza. At last, we discuss and compare the efficiency and effect on different Prevention Intervention Strategies to different novel influenza control by using this model.
43
Botha, Robert Anthony. "The James 1:27 trust programme : a case study of an information, communication and technology (ICT) response to orphans and vulnerable children in the context of an HIV and AIDS epidemic." Diss., 2010. http://hdl.handle.net/10500/3908.
Abstract:
This case study examines the James 1:27 Trust as an information, communication and
technology response to the plight of orphans and vulnerable children within the context of an
HIV and AIDS epidemic. The James 1:27 Trust demonstrates how social networks can be
mobilized in support of children at risk. The use of business information and management
systems to administer concepts such as “virtual adoption” is deemed an important innovative
contribution. The James 1:27 Trust and its model is studied as a contributor in finding solutions
to scale and multiply levels of care by community and faith-based organisations to orphans and
vulnerable children. The James 1:27 Trust is located at the Innovation Hub in Pretoria, Africa’s
first internationally accredited science park.Social Work
M.A. (Social Behaviour in HIV/AIDS))
44
(9741149), Lintao Ye. "Algorithmic and Graph-Theoretic Approaches for Optimal Sensor Selection in Large-Scale Systems." Thesis, 2020.
Abstract:
Using sensor measurements to estimate the states and parameters of a system is a fundamental task in understanding the behavior of the system. Moreover, as modern systems grow rapidly in scale and complexity, it is not always possible to deploy sensors to measure all of the states and parameters of the system, due to cost and physical constraints. Therefore, selecting an optimal subset of all the candidate sensors to deploy and gather measurements of the system is an important and challenging problem. In addition, the systems may be targeted by external attackers who attempt to remove or destroy the deployed sensors. This further motivates the formulation of resilient sensor selection strategies. In this thesis, we address the sensor selection problem under different settings as follows.
First, we consider the optimal sensor selection problem for linear dynamical systems with stochastic inputs, where the Kalman filter is applied based on the sensor measurements to give an estimate of the system states. The goal is to select a subset of sensors under certain budget constraints such that the trace of the steady-state error covariance of the Kalman filter with the selected sensors is minimized. We characterize the complexity of this problem by showing that the Kalman filtering sensor selection problem is NP-hard and cannot be approximated within any constant factor in polynomial time for general systems. We then consider the optimal sensor attack problem for Kalman filtering. The Kalman filtering sensor attack problem is to attack a subset of selected sensors under certain budget constraints in order to maximize the trace of the steady-state error covariance of the Kalman filter with sensors after the attack. We show that the same results as the Kalman filtering sensor selection problem also hold for the Kalman filtering sensor attack problem. Having shown that the general sensor selection and sensor attack problems for Kalman filtering are hard to solve, our next step is to consider special classes of the general problems. Specifically, we consider the underlying directed network corresponding to a linear dynamical system and investigate the case when there is a single node of the network that is affected by a stochastic input. In this setting, we show that the corresponding sensor selection and sensor attack problems for Kalman filtering can be solved in polynomial time. We further study the resilient sensor selection problem for Kalman filtering, where the problem is to find a sensor selection strategy under sensor selection budget constraints such that the trace of the steady-state error covariance of the Kalman filter is minimized after an adversary removes some of the deployed sensors. We show that the resilient sensor selection problem for Kalman filtering is NP-hard, and provide a pseudo-polynomial-time algorithm to solve it optimally.
Next, we consider the sensor selection problem for binary hypothesis testing. The problem is to select a subset of sensors under certain budget constraints such that a certain metric of the Neyman-Pearson (resp., Bayesian) detector corresponding to the selected sensors is optimized. We show that this problem is NP-hard if the objective is to minimize the miss probability (resp., error probability) of the Neyman-Pearson (resp., Bayesian) detector. We then consider three optimization objectives based on the Kullback-Leibler distance, J-Divergence and Bhattacharyya distance, respectively, in the hypothesis testing sensor selection problem, and provide performance bounds on greedy algorithms when applied to the sensor selection problem associated with these optimization objectives.
Moving beyond the binary hypothesis setting, we also consider the setting where the true state of the world comes from a set that can have cardinality greater than two. A Bayesian approach is then used to learn the true state of the world based on the data streams provided by the data sources. We formulate the Bayesian learning data source selection problem under this setting, where the goal is to minimize the cost spent on the data sources such that the learning error is within a certain range. We show that the Bayesian learning data source selection is also NP-hard, and provide greedy algorithms with performance guarantees.
Finally, in light of the COVID-19 pandemic, we study the parameter estimation measurement selection problem for epidemics spreading in networks. Here, the measurements (with certain costs) are collected by conducting virus and antibody tests on the individuals in the epidemic spread network. The goal of the problem is then to optimally estimate the parameters (i.e., the infection rate and the recovery rate of the virus) in the epidemic spread network, while satisfying the budget constraint on collecting the measurements. Again, we show that the measurement selection problem is NP-hard, and provide approximation algorithms with performance guarantees.