Bibliografie tematiche / Networked Epidemic Model

Letteratura scientifica selezionata sul tema "Networked Epidemic Model"

Autore: Grafiati
Pubblicato: 8 giugno 2024

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Articoli di riviste sul tema "Networked Epidemic Model":

1

Liu, Zuhan, e Canrong Tian. "A weighted networked SIRS epidemic model". Journal of Differential Equations 269, n. 12 (dicembre 2020): 10995–1019. http://dx.doi.org/10.1016/j.jde.2020.07.038.

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2

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

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3

Шеншин, Александр Игоревич, Евгения Андреевна Шварцкопф e Константин Александрович Разинкин. "MATHEMATICAL PROVISION OF TWO-STAGE MODEL OF EPIDEMIC PROCESSES OF NETWORKED AUTOMATED STRUCTURES". ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, n. 3(-) (19 ottobre 2021): 431–52. http://dx.doi.org/10.36622/vstu.2021.24.3.010.

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Abstract (sommario):
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 обеспечения в области сетевой эпидемиологии и предложено описание (включая научно-методическое обоснование) дискретной двухэтапной модели эпидемического процесса, призванной разрешить указанное противоречие, а также - разработана методика построения этой модели, включающая соответствующие аналитические выражения для параметров моделирования. In recent years there is a rapid increase of number of attacks on information systems and resources using malicious code and content. Along with this, continuous improvement of self-presence hiding functionality of malware are taking place. Unfortunately, existing arsenal of epidemic process models does not provide an ability to effectively take into account a latent spread period of infection and following reaction of protection systems in cases of practical modeling of network epidemics. In presented research was carried out an analysis of the existing methodological works in the field of network epidemiology and was proposed a description (including scientific-methodological justification) of a discrete two-stage epidemic process model, which is designed to resolve said contradiction, and a methodology for constructing this model was developed, including the corresponding analytical expressions for the modeling parameters.
4

ÁLVAREZ, E., J. DONADO-CAMPOS e F. MORILLA. "New coronavirus outbreak. Lessons learned from the severe acute respiratory syndrome epidemic". Epidemiology and Infection 143, n. 13 (16 gennaio 2015): 2882–93. http://dx.doi.org/10.1017/s095026881400377x.

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Abstract (sommario):
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 process of simulation to fit the real data from the epidemics. Univariate and multivariate sensitivity analyses were performed to determine the reliability of the model. In addition, we discuss how further testing using this model can inform community interventions to reduce the risk in current and future outbreaks, such as the recently Middle East respiratory syndrome coronavirus (MERS-CoV) epidemic.
5

Liu, Fangzhou, Shaoxuan CUI, Xianwei Li e Martin Buss. "On the Stability of the Endemic Equilibrium of A Discrete-Time Networked Epidemic Model". IFAC-PapersOnLine 53, n. 2 (2020): 2576–81. http://dx.doi.org/10.1016/j.ifacol.2020.12.304.

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6

Anderson, Brian D. O., e Mengbin Ye. "Equilibria Analysis of a Networked Bivirus Epidemic Model Using Poincaré–Hopf and Manifold Theory". SIAM Journal on Applied Dynamical Systems 22, n. 4 (12 ottobre 2023): 2856–89. http://dx.doi.org/10.1137/22m1529981.

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7

Liu, Fangzhou, Zengjie Zhang e Martin Buss. "Optimal filtering and control of network information epidemics". at - Automatisierungstechnik 69, n. 2 (30 gennaio 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 utilized to develop the optimal controller, for which the optimality of the closed-loop system is guaranteed by a separation principle. The designed optimal filter and controller, together with the separation principle, form a complete solution for the optimal control of network information epidemics with stochastic perturbations. Such optimal-filtering-based control strategy is also generalizable to a wider range of networked nonlinear systems. In the numerical experiments on real network data, the effectiveness of the proposed optimal control is validated and confirmed.
8

Bellocchio, Francesco, Paola Carioni, Caterina Lonati, Mario Garbelli, Francisco Martínez-Martínez, Stefano Stuard e 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, n. 18 (16 settembre 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 variables include information related to the epidemic status and trends in clinical practice patterns of the target clinic, regional epidemic metrics, and the distance-weighted risk estimates of adjacent dialysis units. On the validation dates, there were 30 (5.09%), 39 (6.52%), and 218 (36.03%) clinics with two or more patients with COVID-19 infection during the 2-week prediction window. The performance of the model was suitable in all testing windows: AUC = 0.77, 0.80, and 0.81, respectively. The occurrence of new cases in a clinic propagates distance-weighted risk estimates to proximal dialysis units. Our machine learning sentinel surveillance system may allow for a prompt risk assessment and timely response to COVID-19 surges throughout networked European clinics.
9

Chwat, Olivia. "Social Solidarity during the Pandemic: The “Visible Hand” and Networked Social Movements". Kultura i Społeczeństwo 65, n. 1 (22 marzo 2021): 87–104. http://dx.doi.org/10.35757/kis.2021.65.1.3.

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The author poses the following questions: (1) What forms are social movements adopting today, particularly in response to the epidemic crisis? (2) Are we observing the practice of grassroots solidarity reaching beyond the charitable model of support? She seeks answers taking the Facebook group Visible Hand [Widzialna Ręka] as an example; it was established shortly after lockdown had been announced in the first quarter of 2020, as a form of social organisation aiming to provide mutual aid during the difficult time of the pandemic. She asserts that communities organising themselves in a manner similar to Visible Hand are an example of how external crises highlight problems existing within societies and contribute to their destabilisation. While deliberating over whether the initiative in question is one of ad-hoc episodes of non-organised collective activity, a discussion-and-contact forum, or perhaps a contemporary social movement, she reaches for Manuel Castells’ concept of networked social movements—and asserts that Visible Hand may be acknowledged as a social movement. In closing her paper, she considers the connections between moral bond and solidarity.
10

Siettos, Constantinos I., Cleo Anastassopoulou, Lucia Russo, Christos Grigoras e 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, n. 1 (gennaio 2016): e008649. http://dx.doi.org/10.1136/bmjopen-2015-008649.

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Ti possono interessare anche gli elenchi estesi di opere sul tema "Networked Epidemic Model":
Articoli di riviste Tesi

Tesi sul tema "Networked Epidemic Model":

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.

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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 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 connexes
This 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.

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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 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.

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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/.

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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 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 epidemiológica
Objective. 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.

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Abstract (sommario):
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.

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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 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.

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Abstract (sommario):
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.

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Master of Science
Department 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/.

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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 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/.

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Abstract (sommario):
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.
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Libri sul tema "Networked Epidemic Model":

1

Kiss, Istvan Z. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Cham: 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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Abstract (sommario):
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 Susceptible–Infected–Removed (SIR) Model are characterized on multilayer networks. Additionally, it is shown that the multilayer networks framework can also allow us to study interacting Awareness and epidemic spreading, competing networks and epidemics in temporal networks.
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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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Abstract (sommario):
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 structure. It is shown that the SIR model maps to a bond percolation process on networks, allowing us to solve for static properties such as the total number of individuals infected in a disease outbreak. The case of the configuration model is developed in detail and the calculations are extended to competing diseases, coinfection, and complex contagion. Time-dependent behavior of diseases on networks is also studied using various differential equation approximations, including pair approximations and degree-based approximations.
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Kiss, István Z., Joel C. Miller e 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 e Péter L. Simon. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Springer, 2017.

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6

Rocha, Luis E. C., Fredrik Liljeros e Petter Holme. Sexual and Communication Networks of Internet-Mediated Prostitution. A cura di Scott Cunningham e 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 methodology for temporal networks, where the infection coevolves with network structure. The chapter shows that the structure of the sexual network is highly clustered within cities but that minimal connections exist across cities. It also finds evidence for local bridges between cities: individual clients who frequent prostitutes nationally. Male tourists play important roles in a potential epidemic by linking otherwise distinct communities.
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Kucharski, Adam. Les lois de la contagion: Fake news, virus, tendances... DUNOD, 2021.

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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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Kucharski, Adam, e Francesca Barrie. Rules of Contagion: Why Things Spread - and Why They Stop. Welcome Books, 2020.

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Capitoli di libri sul tema "Networked Epidemic Model":

1

Boccara, Nino, e 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.

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Wang, 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.

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Ishida, 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.

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Pomorski, 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.

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Walter, Gilbert G., e 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.

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Zhang, Yecheng, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu e 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.

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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 technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
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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, 868–77. Cham: 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, 591–610. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-8927-4_29.

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Meena, Rakesh Kumar, e 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.

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Liu, Ming, Jie Cao, Jing Liang e 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.

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Atti di convegni sul tema "Networked Epidemic Model":

1

Souza, Ronald, e 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 preclude endemic steady-states upon arbitrarily large rates for both walk and transmissibility. Simulation results support our theoretical findings.
2

Schumm, P., C. Scoglio, D. Gruenbacher e 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 e 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 e 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 e 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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Androulidakis, Iosif, Sergio Huerta, Vasileios Vlachos e 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.

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Zhang, Li, e 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.

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Newton, Matthew, e 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.

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Zhang, Jin-Zhu, Jian-Jun Wang, Tie-Xiong Su e 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.

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Han, Lansheng, Shuxia Han e 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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