Relevant bibliographies by topics / Networked Epidemic Model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Networked Epidemic Model'

Author: Grafiati
Published: 8 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Networked Epidemic Model"

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Liu, Zuhan, and Canrong Tian. "A weighted networked SIRS epidemic model." Journal of Differential Equations 269, no. 12 (2020): 10995–1019. http://dx.doi.org/10.1016/j.jde.2020.07.038.

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Tian, Canrong, Qunying Zhang, and Lai Zhang. "Global stability in a networked SIR epidemic model." Applied Mathematics Letters 107 (September 2020): 106444. http://dx.doi.org/10.1016/j.aml.2020.106444.

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ÁLVAREZ, E., J. DONADO-CAMPOS, and F. MORILLA. "New coronavirus outbreak. Lessons learned from the severe acute respiratory syndrome epidemic." Epidemiology and Infection 143, no. 13 (2015): 2882–93. http://dx.doi.org/10.1017/s095026881400377x.

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SUMMARYSystem dynamics approach offers great potential for addressing how intervention policies can affect the spread of emerging infectious diseases in complex and highly networked systems. Here, we develop a model that explains the severe acute respiratory syndrome coronavirus (SARS-CoV) epidemic that occurred in Hong Kong in 2003. The dynamic model developed with system dynamics methodology included 23 variables (five states, four flows, eight auxiliary variables, six parameters), five differential equations and 12 algebraic equations. The parameters were optimized following an iterative pr
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Шеншин, Александр Игоревич, Евгения Андреевна Шварцкопф, and Константин Александрович Разинкин. "MATHEMATICAL PROVISION OF TWO-STAGE MODEL OF EPIDEMIC PROCESSES OF NETWORKED AUTOMATED STRUCTURES." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 3(-) (October 19, 2021): 431–52. http://dx.doi.org/10.36622/vstu.2021.24.3.010.

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В последние годы отмечается стремительный рост количества атак на информационные системы и ресурсы с использованием вредоносного кода и контента. Наряду с этим, происходит непрерывное совершенствование функциональных возможностей вирусов, позволяющих скрывать своё присутствие в системе. К сожалению, существующий арсенал моделей эпидемических процессов не позволяет эффективно учитывать период скрытого распространения инфекции с последующим реагированием систем защиты при практическом моделировании сетевых эпидемий. В представленном исследовании проведён анализ существующего методического обеспе
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Na, Jihye, Youngeun Nam, Susik Yoon, Hwanjun Song, Byung Suk Lee, and Jae-Gil Lee. "Mobility Networked Time-Series Forecasting Benchmark Datasets." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 2539–49. https://doi.org/10.1609/icwsm.v19i1.35955.

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Human mobility is crucial for urban planning (e.g., public transportation) and epidemic response strategies. However, existing research often neglects integrating comprehensive perspectives on spatial dynamics, temporal trends, and other contextual views due to the limitations of existing mobility datasets. To bridge this gap, we introduce MOBINS (MOBIlity Networked time Series), a novel dataset collection designed for networked time-series forecasting of dynamic human movements. MOBINS features diverse and explainable datasets that capture various mobility patterns across different transporta
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Xue, Dong, Naichao Liu, Xinyi Chen, and Fangzhou Liu. "A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic." Entropy 26, no. 8 (2024): 654. http://dx.doi.org/10.3390/e26080654.

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This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements
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Liu, Fangzhou, Shaoxuan CUI, Xianwei Li, and Martin Buss. "On the Stability of the Endemic Equilibrium of A Discrete-Time Networked Epidemic Model." IFAC-PapersOnLine 53, no. 2 (2020): 2576–81. http://dx.doi.org/10.1016/j.ifacol.2020.12.304.

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Anderson, Brian D. O., and Mengbin Ye. "Equilibria Analysis of a Networked Bivirus Epidemic Model Using Poincaré–Hopf and Manifold Theory." SIAM Journal on Applied Dynamical Systems 22, no. 4 (2023): 2856–89. http://dx.doi.org/10.1137/22m1529981.

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Liu, Fangzhou, Zengjie Zhang, and Martin Buss. "Optimal filtering and control of network information epidemics." at - Automatisierungstechnik 69, no. 2 (2021): 122–30. http://dx.doi.org/10.1515/auto-2020-0096.

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Abstract In this article, we propose an optimal control scheme for information epidemics with stochastic uncertainties aiming at maximizing information diffusion and minimizing the control consumption. The information epidemic dynamics is represented by a network Susceptible-Infected-Susceptible (SIS) model contaminated by both process and observation noises to describe a perturbed disease-like information diffusion process. To reconstruct the contaminated system states, we design an optimal filter which ensures minimized estimation errors in a quadratic sense. The state estimation is then uti
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Bellocchio, Francesco, Paola Carioni, Caterina Lonati, et al. "Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network." International Journal of Environmental Research and Public Health 18, no. 18 (2021): 9739. http://dx.doi.org/10.3390/ijerph18189739.

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Accurate predictions of COVID-19 epidemic dynamics may enable timely organizational interventions in high-risk regions. We exploited the interconnection of the Fresenius Medical Care (FMC) European dialysis clinic network to develop a sentinel surveillance system for outbreak prediction. We developed an artificial intelligence-based model considering the information related to all clinics belonging to the European Nephrocare Network. The prediction tool provides risk scores of the occurrence of a COVID-19 outbreak in each dialysis center within a 2-week forecasting horizon. The model input var
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Dissertations / Theses on the topic "Networked Epidemic Model"

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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.

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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
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Tunc, Ilker. "Epidemic models on adaptive networks with network structure constraints." W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623618.

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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
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Burch, Mark G. "Statistical Methods for Network Epidemic Models." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471613656.

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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/.

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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
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Livingston, Samantha 1980. "Stochastic models for epidemics on networks." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28437.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (p. 37).<br>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
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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.

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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
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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.

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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
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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.

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Master of Science<br>Department of Electrical and Computer Engineering<br>Caterina Maria Scoglio<br>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
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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/.

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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
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Davis, Ben. "Stochastic epidemic models on random networks : casual contacts, clustering and vaccination." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/47272/.

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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
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Books on the topic "Networked Epidemic Model"

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Kiss, Istvan Z. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer International Publishing, 2017.

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Bianconi, Ginestra. Epidemic Spreading. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0013.

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Epidemic processes are relevant to studying the propagation of infectious diseases, but their current use extends also to the study of propagation of ideas in the society or memes and news in online social media. In most of the relevant applications epidemic spreading does not actually take place on a single network but propagates in a multilayer network where different types of interaction play different roles. This chapter provides a comprehensive view of the effect that multilayer network structures have on epidemic processes. The Susceptible–Infected–Susceptible (SIS) Model and the Suscept
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Newman, Mark. Epidemics on networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0016.

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This chapter discusses the spread of diseases over contact networks between individuals and the methods used to model this process. The chapter begins with an introduction to the classic models of mathematical epidemiology, including the SI model, the SIR model, and the SIS model. Models for coinfection and competition between diseases are also discussed, as well as “complex contagion” models used to represent the spread of information. The remainder of the chapter deals with the behavior of these models on networks, where the behavior of spreading diseases depends strongly on network structur
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Kiss, István Z., Joel C. Miller, and Péter L. Simon. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer, 2018.

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Kiss, István Z., Joel C. Miller, and Péter L. Simon. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer, 2017.

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Rocha, Luis E. C., Fredrik Liljeros, and Petter Holme. Sexual and Communication Networks of Internet-Mediated Prostitution. Edited by Scott Cunningham and Manisha Shah. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199915248.013.3.

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This chapter examines prostitution as a socioeconomic phenomenon and discusses its contribution to the spread of sexually transmitted infections. Using online network data from Internet-mediated prostitution in Brazil, it looks at the connectedness of individuals on a review website where clients record intimate details about encounters with sex workers. It begins with an overview of networks, including human sexual networks, along with network properties and measures and the dynamics and structure of a sexual network. It describes general models of disease spreading and introduces a specific
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Kucharski, Adam. Les lois de la contagion: Fake news, virus, tendances... DUNOD, 2021.

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The Rules of Contagion: Why Things Spread--And Why They Stop. Basic Books, 2020.

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Kucharski, Adam. Rules of Contagion: Why Things Spread--And Why They Stop. Basic Books, 2021.

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Kucharski, Adam. Les lois de la contagion: Fake news, virus, tendances... : comment tout commence, pourquoi tout s'arrête. DUNOD, 2021.

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Book chapters on the topic "Networked Epidemic Model"

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Boccara, Nino, and Kyeong Cheong. "Automata Network Epidemic Models." In Cellular Automata and Cooperative Systems. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1691-6_4.

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Wang, Huijuan. "Epidemic Spreading on Interconnected Networks." In Multilevel Strategic Interaction Game Models for Complex Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24455-2_7.

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Consoli, Sergio, Peter Markov, Nikolaos I. Stilianakis, Lorenzo Bertolini, Antonio Puertas Gallardo, and Mario Ceresa. "Epidemic Information Extraction for Event-Based Surveillance Using Large Language Models." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4581-4_17.

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AbstractThis paper presents a novel approach to epidemic surveillance, leveraging the power of artificial intelligence and large language models (LLMs) for effective interpretation of unstructured big data sources like the popular ProMED and WHO Disease Outbreak News. We explore several LLMs, evaluating their capabilities in extracting valuable epidemic information. We further enhance the capabilities of the LLMs using in-context learning and test the performance of an ensemble model incorporating multiple open-source LLMs. The findings indicate that LLMs can significantly enhance the accuracy
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Ishida, Yoshiteru. "Self-Repair Networks as an Epidemic Model." In Self-Repair Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26447-9_10.

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Pomorski, Krzysztof. "Equivalence Between Classical Epidemic Model and Quantum Tight-Binding Model." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18461-1_31.

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Walter, Gilbert G., and Martha Contreras. "Models for the Spread of Epidemics." In Compartmental Modeling with Networks. Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1590-5_14.

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Zhang, Yecheng, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, and Hao Zheng. "Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_18.

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AbstractFrom the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation
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Zhang, Yi. "An Epidemic Spreading Model Based on Dynamical Network." In Proceedings of the Eleventh International Conference on Management Science and Engineering Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59280-0_71.

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Simoes, Joana A. "An Agent-Based/Network Approach to Spatial Epidemics." In Agent-Based Models of Geographical Systems. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-8927-4_29.

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Liu, Ming, Jie Cao, Jing Liang, and MingJun Chen. "Integrated Optimization Model for Two-Level Epidemic-Logistics Network." In Epidemic-logistics Modeling: A New Perspective on Operations Research. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9353-2_6.

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Conference papers on the topic "Networked Epidemic Model"

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Chen, Bo, Baike She, Calvin Hawkins, et al. "Differentially Private Computation of Basic Reproduction Numbers in Networked Epidemic Models." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644264.

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Chang, Herng-Hua, Yu-Xuan Chou, Chi-Chao Chao, and Sung-Tsang Hsieh. "Automatic Epidermis Segmentation in Fluorescence Images Based on a U-Shaped Network Model." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650305.

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Zino, Lorenzo, and Alessando Rizzo. "On a Susceptible-Infected-Susceptible Epidemic Model with Reactive Behavioral Response on Higher-Order Temporal Networks." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886121.

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Zhou, Xuehe, Jingcheng Zhang, Yangyang Tian, Jiuwen Cao, Zichao Jin, and Lingbo Yang. "Study on Large-Scale Crop Disease Epidemic Forecasting Based on a Fusion Model Between SEIR and LSTM Networks." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10827251.

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Pinto, Conrado C., and Daniel R. Figueiredo. "Identifying Asymptomatic Nodes in Network Epidemics using Betweenness Centrality." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/wperformance.2024.2414.

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Epidemics of certain viruses in a population can have major impact effects, as is the case in the recent global pandemic caused by the COVID-19 virus. Identifying infected individuals during the course of an epidemic is extremely important for measuring spread and designing more effective control measures. However, in some epidemics infected individuals do not exhibit clear symptoms despite being infected and contributing to the contagion of others (called asymptomatic). This work addresses the problem of identifying asymptomatic individuals in network epidemics based on the observation of inf
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Souza, Ronald, and Daniel Figueiredo. "Characterizing Protection Effects on Network Epidemics driven by Random Walks." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wperformance.2020.11109.

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Protection effects (PFx) denote protective measures taken by individuals (such as to wear masks and wash hands) upon their risk-perception towards an ongoing epidemic outbreak. The holistic force produced may fundamentally change the course of a spreading, with respect to both its reach and duration. This work proposes a model for PFx on network epidemics where nodes are sites mobile-agents may visit. Risk aversion is encoded as random-walks biased to safe sites. Assuming the network is a complete graph, the model is analyzed and framed as a classical SIS. We find a regime under which PFx prec
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Schumm, P., C. Scoglio, D. Gruenbacher, and T. Easton. "Epidemic spreading on weighted contact networks." In 2007 2nd Bio-Inspired Models of Network, Information and Computing Systems (BIONETICS). IEEE, 2007. http://dx.doi.org/10.1109/bimnics.2007.4610111.

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Schumm, P., C. Scoglio, D. Gruenbacher, and T. Easton. "Epidemic Spreading on Weighted Contact Networks." In 2nd International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems. IEEE, 2007. http://dx.doi.org/10.4108/icst.bionetics2007.2435.

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Yunli Zhang, Maoxing Liu, and Youwen Li. "An epidemic model on evolving networks." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002636.

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Ahi, Emrah, Mine Cavlar, and Oznur Ozkasap. "Stepwise Probabilistic Buffering for Epidemic Information Dissemination." In 2006 1st Bio-Inspired Models of Network, Information and Computing Systems. IEEE, 2006. http://dx.doi.org/10.1109/bimnics.2006.361811.

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