Auswahl der wissenschaftlichen Literatur zum Thema „Networked Epidemic Model“
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Zeitschriftenartikel zum Thema "Networked Epidemic Model"
Liu, Zuhan, und Canrong Tian. „A weighted networked SIRS epidemic model“. Journal of Differential Equations 269, Nr. 12 (Dezember 2020): 10995–1019. http://dx.doi.org/10.1016/j.jde.2020.07.038.
Der volle Inhalt der QuelleTian, Canrong, Qunying Zhang und 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.
Der volle Inhalt der QuelleШеншин, Александр Игоревич, Евгения Андреевна Шварцкопф und Константин Александрович Разинкин. „MATHEMATICAL PROVISION OF TWO-STAGE MODEL OF EPIDEMIC PROCESSES OF NETWORKED AUTOMATED STRUCTURES“. ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, Nr. 3(-) (19.10.2021): 431–52. http://dx.doi.org/10.36622/vstu.2021.24.3.010.
Der volle Inhalt der QuelleÁLVAREZ, E., J. DONADO-CAMPOS und F. MORILLA. „New coronavirus outbreak. Lessons learned from the severe acute respiratory syndrome epidemic“. Epidemiology and Infection 143, Nr. 13 (16.01.2015): 2882–93. http://dx.doi.org/10.1017/s095026881400377x.
Der volle Inhalt der QuelleLiu, Fangzhou, Shaoxuan CUI, Xianwei Li und Martin Buss. „On the Stability of the Endemic Equilibrium of A Discrete-Time Networked Epidemic Model“. IFAC-PapersOnLine 53, Nr. 2 (2020): 2576–81. http://dx.doi.org/10.1016/j.ifacol.2020.12.304.
Der volle Inhalt der QuelleAnderson, Brian D. O., und Mengbin Ye. „Equilibria Analysis of a Networked Bivirus Epidemic Model Using Poincaré–Hopf and Manifold Theory“. SIAM Journal on Applied Dynamical Systems 22, Nr. 4 (12.10.2023): 2856–89. http://dx.doi.org/10.1137/22m1529981.
Der volle Inhalt der QuelleLiu, Fangzhou, Zengjie Zhang und Martin Buss. „Optimal filtering and control of network information epidemics“. at - Automatisierungstechnik 69, Nr. 2 (30.01.2021): 122–30. http://dx.doi.org/10.1515/auto-2020-0096.
Der volle Inhalt der QuelleBellocchio, Francesco, Paola Carioni, Caterina Lonati, Mario Garbelli, Francisco Martínez-Martínez, Stefano Stuard und Luca Neri. „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, Nr. 18 (16.09.2021): 9739. http://dx.doi.org/10.3390/ijerph18189739.
Der volle Inhalt der QuelleChwat, Olivia. „Social Solidarity during the Pandemic: The “Visible Hand” and Networked Social Movements“. Kultura i Społeczeństwo 65, Nr. 1 (22.03.2021): 87–104. http://dx.doi.org/10.35757/kis.2021.65.1.3.
Der volle Inhalt der QuelleSiettos, Constantinos I., Cleo Anastassopoulou, Lucia Russo, Christos Grigoras und Eleftherios Mylonakis. „Forecasting and control policy assessment for the Ebola virus disease (EVD) epidemic in Sierra Leone using small-world networked model simulations“. BMJ Open 6, Nr. 1 (Januar 2016): e008649. http://dx.doi.org/10.1136/bmjopen-2015-008649.
Der volle Inhalt der QuelleDissertationen zum Thema "Networked Epidemic Model"
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.
Der volle Inhalt der QuelleThis 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
Tunc, Ilker. „Epidemic models on adaptive networks with network structure constraints“. W&M ScholarWorks, 2013. https://scholarworks.wm.edu/etd/1539623618.
Der volle Inhalt der QuelleBurch, Mark G. „Statistical Methods for Network Epidemic Models“. The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471613656.
Der volle Inhalt der QuelleMarques, 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/.
Der volle Inhalt der QuelleObjective. 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
Livingston, Samantha 1980. „Stochastic models for epidemics on networks“. Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28437.
Der volle Inhalt der QuelleIncludes 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.
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.
Der volle Inhalt der QuelleSensi, Mattia. „A Geometric Singular Perturbation approach to epidemic compartmental models“. Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/286191.
Der volle Inhalt der QuelleRiad, 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.
Der volle Inhalt der QuelleDepartment 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.
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/.
Der volle Inhalt der QuelleDavis, Ben. „Stochastic epidemic models on random networks : casual contacts, clustering and vaccination“. Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/47272/.
Der volle Inhalt der QuelleBücher zum Thema "Networked Epidemic Model"
Kiss, Istvan Z. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Cham: Springer International Publishing, 2017.
Den vollen Inhalt der Quelle findenBianconi, Ginestra. Epidemic Spreading. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0013.
Der volle Inhalt der QuelleNewman, Mark. Epidemics on networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0016.
Der volle Inhalt der QuelleKiss, István Z., Joel C. Miller und Péter L. Simon. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer, 2018.
Den vollen Inhalt der Quelle findenKiss, István Z., Joel C. Miller und Péter L. Simon. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer, 2017.
Den vollen Inhalt der Quelle findenRocha, Luis E. C., Fredrik Liljeros und Petter Holme. Sexual and Communication Networks of Internet-Mediated Prostitution. Herausgegeben von Scott Cunningham und Manisha Shah. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199915248.013.3.
Der volle Inhalt der QuelleKucharski, Adam. Les lois de la contagion: Fake news, virus, tendances... DUNOD, 2021.
Den vollen Inhalt der Quelle findenKucharski, Adam. Rules of Contagion: Why Things Spread--And Why They Stop. Basic Books, 2021.
Den vollen Inhalt der Quelle findenKucharski, Adam. Les lois de la contagion: Fake news, virus, tendances... : comment tout commence, pourquoi tout s'arrête. DUNOD, 2021.
Den vollen Inhalt der Quelle findenKucharski, Adam, und Francesca Barrie. Rules of Contagion: Why Things Spread - and Why They Stop. Welcome Books, 2020.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Networked Epidemic Model"
Boccara, Nino, und Kyeong Cheong. „Automata Network Epidemic Models“. In Cellular Automata and Cooperative Systems, 29–44. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1691-6_4.
Der volle Inhalt der QuelleWang, Huijuan. „Epidemic Spreading on Interconnected Networks“. In Multilevel Strategic Interaction Game Models for Complex Networks, 131–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24455-2_7.
Der volle Inhalt der QuelleIshida, Yoshiteru. „Self-Repair Networks as an Epidemic Model“. In Self-Repair Networks, 123–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26447-9_10.
Der volle Inhalt der QuellePomorski, Krzysztof. „Equivalence Between Classical Epidemic Model and Quantum Tight-Binding Model“. In Lecture Notes in Networks and Systems, 477–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18461-1_31.
Der volle Inhalt der QuelleWalter, Gilbert G., und Martha Contreras. „Models for the Spread of Epidemics“. In Compartmental Modeling with Networks, 125–29. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1590-5_14.
Der volle Inhalt der QuelleZhang, Yecheng, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu und Hao Zheng. „Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective“. In Computational Design and Robotic Fabrication, 209–22. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_18.
Der volle Inhalt der QuelleZhang, Yi. „An Epidemic Spreading Model Based on Dynamical Network“. In Proceedings of the Eleventh International Conference on Management Science and Engineering Management, 868–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59280-0_71.
Der volle Inhalt der QuelleSimoes, Joana A. „An Agent-Based/Network Approach to Spatial Epidemics“. In Agent-Based Models of Geographical Systems, 591–610. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-8927-4_29.
Der volle Inhalt der QuelleMeena, Rakesh Kumar, und Sushil Kumar. „A Study on Fractional SIS Epidemic Model Using RPS Method“. In Lecture Notes in Networks and Systems, 293–309. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3080-7_22.
Der volle Inhalt der QuelleLiu, Ming, Jie Cao, Jing Liang und MingJun Chen. „Integrated Optimization Model for Two-Level Epidemic-Logistics Network“. In Epidemic-logistics Modeling: A New Perspective on Operations Research, 109–28. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9353-2_6.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Networked Epidemic Model"
Souza, Ronald, und 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.
Der volle Inhalt der QuelleSchumm, P., C. Scoglio, D. Gruenbacher und 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.
Der volle Inhalt der QuelleSchumm, P., C. Scoglio, D. Gruenbacher und 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.
Der volle Inhalt der QuelleYunli Zhang, Maoxing Liu und 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.
Der volle Inhalt der QuelleAhi, Emrah, Mine Cavlar und 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.
Der volle Inhalt der QuelleAndroulidakis, Iosif, Sergio Huerta, Vasileios Vlachos und Igor Santos. „Epidemic model for malware targeting telephony networks“. In 2016 23rd International Conference on Telecommunications (ICT). IEEE, 2016. http://dx.doi.org/10.1109/ict.2016.7500450.
Der volle Inhalt der QuelleZhang, Li, und Aifeng Jin. „Two delayed SEIRS epidemic model in networks“. In 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324654.
Der volle Inhalt der QuelleNewton, Matthew, und Antonis Papachristodoulou. „Network Lyapunov Functions for Epidemic Models“. In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9304021.
Der volle Inhalt der QuelleZhang, Jin-Zhu, Jian-Jun Wang, Tie-Xiong Su und Zhen Jin. „Analysis of a Delayed SIR Epidemic Model“. In 2010 International Conference on Computational Aspects of Social Networks (CASoN 2010). IEEE, 2010. http://dx.doi.org/10.1109/cason.2010.50.
Der volle Inhalt der QuelleHan, Lansheng, Shuxia Han und Min Yang. „The Epidemic Threshold of a More General Epidemic Spreading Model for Network Viruses“. In 2007 3rd International Conference on Natural Computation. IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.724.
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