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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="PmVRrVG1vvC7vB6kddcEis_ZlTramj4WXXADxZqJJ60kvEm-THxF1sKBIGsCoiFOF2CAM4-cBMzEGqc7Wlkkxw" /> <meta name="citation_title" content="A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks" /> <meta name="citation_author" content="Davide Cassi" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/118041862/A_comparison_of_node_vaccination_strategies_to_halt_SIR_epidemic_spreading_in_real_world_complex_networks" /> <meta name="twitter:title" content="A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks" /> <meta name="twitter:description" content="We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial" /> <meta name="twitter:image" content="https://0.academia-photos.com/34786075/10144815/11320048/s200_davide.cassi.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/118041862/A_comparison_of_node_vaccination_strategies_to_halt_SIR_epidemic_spreading_in_real_world_complex_networks" /> <meta property="og:title" content="A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial" /> <meta property="article:author" content="https://independent.academia.edu/DavideCassi" /> <meta name="description" content="We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial" /> <title>(PDF) A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks</title> <link rel="canonical" href="https://www.academia.edu/118041862/A_comparison_of_node_vaccination_strategies_to_halt_SIR_epidemic_spreading_in_real_world_complex_networks" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'b092bf3a3df71cf13feee7c143e83a57eb6b94fb'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1739830076000); window.Aedu.timeDifference = new Date().getTime() - 1739830076000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial network structure, i.e., non-adaptive approach, and performing partial node rank recalculation after node removal, i.e., semi-adaptive approach. To quantify the efficacy of each vaccination strategy, we used three epidemic spread indicators: the size of the largest connected component, the total number of infected at the end of the epidemic, and the maximum number of simultaneously infected individuals. We show that the best vaccination strategies in the non-adaptive and semi-adaptive approaches are different and that the best strategy also depends on the number of available vaccines. Furthermore, a partial recalculation of the node centrality increases the efficacy of the vaccination strategies up to 80%.","author":[{"@context":"https://schema.org","@type":"Person","name":"Davide Cassi","url":"https://independent.academia.edu/DavideCassi"}],"contributor":[],"dateCreated":"2024-04-25","headline":"A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks","image":"https://attachments.academia-assets.com/113759551/thumbnails/1.jpg","inLanguage":"en","keywords":["Vaccination","Root-Mean Square Error","Centrality"],"publisher":{"@context":"https://schema.org","@type":"Organization","name":"Research Square Platform 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window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":118041862,"created_at":"2024-04-25T03:11:58.838-07:00","from_world_paper_id":253425623,"updated_at":"2024-04-25T03:36:04.186-07:00","_data":{"abstract":"We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial network structure, i.e., non-adaptive approach, and performing partial node rank recalculation after node removal, i.e., semi-adaptive approach. To quantify the efficacy of each vaccination strategy, we used three epidemic spread indicators: the size of the largest connected component, the total number of infected at the end of the epidemic, and the maximum number of simultaneously infected individuals. We show that the best vaccination strategies in the non-adaptive and semi-adaptive approaches are different and that the best strategy also depends on the number of available vaccines. Furthermore, a partial recalculation of the node centrality increases the efficacy of the vaccination strategies up to 80%.","publisher":"Research Square Platform LLC"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [34786075]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":113759551,"attachmentType":"pdf"}"><img alt="First page of “A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/113759551/mini_magick20240802-1-vusi79.png?1722614501" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="34786075" href="https://independent.academia.edu/DavideCassi"><img alt="Profile image of Davide Cassi" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/34786075/10144815/11320048/s65_davide.cassi.jpg" />Davide Cassi</a></div><div class="ds-work-card--detail"><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">13 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 118041862; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">We compared seven node vaccination strategies in twelve real-world complex networks. The node vaccination strategies are modeled as node removal on networks. We performed node vaccination strategies both removing nodes according to the initial network structure, i.e., non-adaptive approach, and performing partial node rank recalculation after node removal, i.e., semi-adaptive approach. To quantify the efficacy of each vaccination strategy, we used three epidemic spread indicators: the size of the largest connected component, the total number of infected at the end of the epidemic, and the maximum number of simultaneously infected individuals. We show that the best vaccination strategies in the non-adaptive and semi-adaptive approaches are different and that the best strategy also depends on the number of available vaccines. Furthermore, a partial recalculation of the node centrality increases the efficacy of the vaccination strategies up to 80%.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":113759551,"attachmentType":"pdf","workUrl":"https://www.academia.edu/118041862/A_comparison_of_node_vaccination_strategies_to_halt_SIR_epidemic_spreading_in_real_world_complex_networks"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":113759551,"attachmentType":"pdf","workUrl":"https://www.academia.edu/118041862/A_comparison_of_node_vaccination_strategies_to_halt_SIR_epidemic_spreading_in_real_world_complex_networks"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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This structure can be captured by multilayer networks and can have major effects on the spreading of process that occurs over them, such as epidemics. In this Letter we study a targeted immunization strategy for epidemic spreading over a multilayer network. We apply the strategy in one of the layers and study its effect in all layers of the network disregarding degree-degree correlation among layers. We found that the targeted strategy is not as efficient as in isolated networks, due to the fact that in order to stop the spreading of the disease it is necessary to immunize more than the 80% of the individuals. However, the size of the epidemic is drastically reduced in the layer where the immunization strategy is applied compared to the case with no mitigation strategy. Thus, the immunization strategy has a major effect on the layer were it is applied, but does not efficiently protect the individuals of other layers.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Immunization strategy for epidemic spreading on multilayer networks","attachmentId":41395900,"attachmentType":"pdf","work_url":"https://www.academia.edu/20480051/Immunization_strategy_for_epidemic_spreading_on_multilayer_networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/20480051/Immunization_strategy_for_epidemic_spreading_on_multilayer_networks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="86669198" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/86669198/Suppression_of_epidemic_spreading_process_on_multiplex_networks_via_active_immunization">Suppression of epidemic spreading process on multiplex networks via active immunization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="169788029" href="https://independent.academia.edu/ChintyaHia">Chintya Hia</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Spatial epidemic spreading, a fundamental dynamical process upon complex networks, attracts huge research interest during the past few decades. To suppress the spreading of epidemic, a couple of e ective methods have been proposed, including node vaccination. Under such a scenario, nodes are immunized passively and fail to reveal the mechanisms of active activity. Here, we suggest one novel model of an observer node, which can identify infection through interacting with infected neighbors and inform the other neighbors for vaccination, on multiplex networks, consisting of epidemic spreading layer and information spreading layer. In detail, the epidemic spreading layer supports susceptibleinfected-recovered process, while observer nodes will be selected according to several algorithms derived from percolation theory. Numerical simulation results show that the algorithm based on large degree performs better than random placement, while the algorithm based on nodes' degree in the information spreading layer performs the best (i.e., the best suppression e cacy is guaranteed when placing observer nodes based on nodes' degree in the information spreading layer). With the help of state probability transition equation, the above phenomena can be validated accurately. Our work thus may shed new light into understanding control of empirical epidemic control.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Suppression of epidemic spreading process on multiplex networks via active immunization","attachmentId":91068143,"attachmentType":"pdf","work_url":"https://www.academia.edu/86669198/Suppression_of_epidemic_spreading_process_on_multiplex_networks_via_active_immunization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/86669198/Suppression_of_epidemic_spreading_process_on_multiplex_networks_via_active_immunization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="28986224" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/28986224/Immunization_against_Infection_Propagation_in_Heterogeneous_Networks">Immunization against Infection Propagation in Heterogeneous Networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="54466675" href="https://fordham.academia.edu/SajalBhatia">Sajal Bhatia</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2014 IEEE 13th International Symposium on Network Computing and Applications, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">Modeling spreading processes for infections has been a widely researched area owing to its application in variety of domains especially epidemic spread and worm propagation. Until recently, infection propagation models usually inspired by epidemic spreading, solely relied upon the underlying network properties without taking into account the variation in node specific properties, such as its ability to spread infection or recover from an infection. Owing to this fact, these models have been agnostic to the effects such node heterogeneity might have in the overall infection (or immunization) process. In this paper, we incorporate node properties in a well-known Susceptible Infected Recovered Susceptible (SIRS) model for infection propagation, and propose new heuristics to curb the spread of infection in heterogeneous networks. The proposed heuristics are validated against various network topologies, including a real-world example of an email exchange network.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Immunization against Infection Propagation in Heterogeneous Networks","attachmentId":49431336,"attachmentType":"pdf","work_url":"https://www.academia.edu/28986224/Immunization_against_Infection_Propagation_in_Heterogeneous_Networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/28986224/Immunization_against_Infection_Propagation_in_Heterogeneous_Networks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="123263246" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/123263246/Analysis_of_Epidemic_Models_in_Complex_Networks_and_Node_Isolation_Strategie_Proposal_for_Reducing_Virus_Propagation">Analysis of Epidemic Models in Complex Networks and Node Isolation Strategie Proposal for Reducing Virus Propagation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="255323247" href="https://independent.academia.edu/CarlosRodr%C3%ADguezLucatero">Carlos Rodríguez Lucatero</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Axioms, 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">Many models of virus propagation in Computer Networks inspired by epidemic disease propagation mathematical models that can be found in the epidemiology field (SIS, SIR, SIRS, etc.) have been proposed in the last two decades. The purpose of these models has been to determine the conditions under which a virus becomes rapidly extinct in a network. The most common models of virus propagation in networks are SIS-type models or their variants. In such models, the conditions that lead to a rapid extinction of the spread of a computer virus have been calculated and its dependence on some parameters inherent to the mathematical model has been observed. In this article, we will try to analyze a particular SIS-type model proposed in the past by Chakrabarti as well as an SIRS-type variation of this model proposed in the past by myself. I will show through simulations the influence that the topology of a network has on the dynamics of the spread of a virus in different network types. In the recent past, there have been interesting articles that demonstrate the relationship between the eigenvalue λ 1 of the adjacency matrix and the reduction in the spread of a virus in a network. From this, the minimization of the spectral radius strategies by edge suppression has been proposed. This problem is NP-complete in its general case and for this reason, heuristic algorithms have been proposed. In this article, I will perform simulations of an SIS-type model in topologies with the same number of nodes but with different structures to compare their epidemic behavior. The simulations will show that regular topologies with small node degrees, i.e., of degree 4, as is the case of the topology that I call Lattice4, have favorable behavior in terms of the fast extinction property, with respect to other denser and less regular topologies such as the binomial topologies as well as Power law topologies. Based on the results of the simulations, my contribution will consist of proposing, as a node isolation strategy, a transformation of the original topology into an approximately regular topology by edge elimination. Although such a transformed topology is not optimal in terms of reducing the propagation of a virus, it induces the rapid extinction of the virus in the network.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analysis of Epidemic Models in Complex Networks and Node Isolation Strategie Proposal for Reducing Virus Propagation","attachmentId":117736149,"attachmentType":"pdf","work_url":"https://www.academia.edu/123263246/Analysis_of_Epidemic_Models_in_Complex_Networks_and_Node_Isolation_Strategie_Proposal_for_Reducing_Virus_Propagation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/123263246/Analysis_of_Epidemic_Models_in_Complex_Networks_and_Node_Isolation_Strategie_Proposal_for_Reducing_Virus_Propagation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="44971194" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/44971194/Influence_of_dynamic_immunization_on_epidemic_spreading_in_networks">Influence of dynamic immunization on epidemic spreading in networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="169737752" href="https://independent.academia.edu/zhenjin3">zhen jin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Physica A: Statistical Mechanics and its Applications, 2015</p><p class="ds-related-work--abstract ds2-5-body-sm">• A new dynamic immunization strategy in networks is proposed.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Influence of dynamic immunization on epidemic spreading in networks","attachmentId":65506697,"attachmentType":"pdf","work_url":"https://www.academia.edu/44971194/Influence_of_dynamic_immunization_on_epidemic_spreading_in_networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/44971194/Influence_of_dynamic_immunization_on_epidemic_spreading_in_networks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="78578909" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/78578909/An_Efficient_Immunization_Strategy_Using_Overlapping_Nodes_and_Its_Neighborhoods">An Efficient Immunization Strategy Using Overlapping Nodes and Its Neighborhoods</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="621317" href="https://u-bourgogne.academia.edu/hocinecherifi">Hocine Cherifi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">When an epidemic occurs, it is often impossible to vaccinate the entire population due to limited amount of resources. Therefore, it is of prime interest to identify the set of influential spreaders to immunize, in order to minimize both the cost of vaccine resource and the disease spreading. While various strategies based on the network topology have been introduced, few works consider the influence of the community structure in the epidemic spreading process. Nowadays, it is clear that many real-world networks exhibit an overlapping community structure, in which nodes are allowed to belong to more than one community. Previous work shows that the numbers of communities to which a node belongs is a good measure of its epidemic influence. In this work, we address the effect of nodes in the neighborhood of the overlapping nodes on epidemics spreading. The proposed immunization strategy provides highly connected neighbors of overlapping nodes in the network to immunize. The whole process requires information only at the node level and is well suited to large-scale networks. Extensive experiments on four real-world networks of diverse nature have been performed. Comparisons with alternative local immunization strategies using the fraction of the Largest Connected Component (LCC) after immunization,show that the proposed method is much more efficient. Additionally, it compares favorably to global measures such as degree and betweenness centrality.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Efficient Immunization Strategy Using Overlapping Nodes and Its Neighborhoods","attachmentId":85578281,"attachmentType":"pdf","work_url":"https://www.academia.edu/78578909/An_Efficient_Immunization_Strategy_Using_Overlapping_Nodes_and_Its_Neighborhoods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/78578909/An_Efficient_Immunization_Strategy_Using_Overlapping_Nodes_and_Its_Neighborhoods"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="11471715" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/11471715/Hub_nodes_inhibit_the_outbreak_of_epidemic_under_voluntary_vaccination">Hub nodes inhibit the outbreak of epidemic under voluntary vaccination</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="26643484" href="https://independent.academia.edu/MichaelSmall1">Michael Small</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2010</p><p class="ds-related-work--abstract ds2-5-body-sm">It is commonly believed that epidemic spreading on scale-free networks is difficult to control and that the disease can spread even with a low infection rate, lacking an epidemic threshold. In this paper, we study epidemic spreading on complex networks under the framework of game theory, in which a voluntary vaccination strategy is incorporated. In particular, individuals face the 'dilemma' of vaccination: they have to decide whether or not to vaccinate according to the trade-off between the risk and the side effects or cost of vaccination. Remarkably and quite excitingly, we find that disease outbreak can be more effectively inhibited on scale-free networks than on random networks. This is because the hub nodes of scale-free networks are more inclined to take self-vaccination after balancing the pros and cons. This result is encouraging as it indicates that real-world networks, which are often claimed to be scale free, can be favorably and easily controlled under voluntary vaccination. Our 2 work provides a way of understanding how to prevent the outbreak of diseases under voluntary vaccination, and is expected to provide valuable information on effective disease control and appropriate decision-making.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Hub nodes inhibit the outbreak of epidemic under voluntary vaccination","attachmentId":46689027,"attachmentType":"pdf","work_url":"https://www.academia.edu/11471715/Hub_nodes_inhibit_the_outbreak_of_epidemic_under_voluntary_vaccination","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/11471715/Hub_nodes_inhibit_the_outbreak_of_epidemic_under_voluntary_vaccination"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="47492508" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/47492508/Enhanced_vaccine_control_of_epidemics_in_adaptive_networks">Enhanced vaccine control of epidemics in adaptive networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="56135069" href="https://independent.academia.edu/IraSchwartz3">Ira Schwartz</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Physical Review E, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">We study vaccine control for disease spread on an adaptive network modeling disease avoidance behavior. Control is implemented by adding Poisson-distributed vaccination of susceptibles. We show that vaccine control is much more effective in adaptive networks than in static networks due to feedback interaction between the adaptive network rewiring and the vaccine application. When compared to extinction rates in static social networks, we find that the amount of vaccine resources required to sustain similar rates of extinction are as much as two orders of magnitude lower in adaptive networks.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Enhanced vaccine control of epidemics in adaptive networks","attachmentId":66557186,"attachmentType":"pdf","work_url":"https://www.academia.edu/47492508/Enhanced_vaccine_control_of_epidemics_in_adaptive_networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/47492508/Enhanced_vaccine_control_of_epidemics_in_adaptive_networks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="81654286" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/81654286/Protection_Strategy_for_Edge_Weighted_Graphs_in_Disease_Spread">Protection Strategy for Edge-Weighted Graphs in Disease Spread</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="82604121" href="https://uv-cl.academia.edu/carlataramasco">carla taramasco</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Applied Sciences, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Fake news, viruses on computer systems or infectious diseases on communities are some of the problems that are addressed by researchers dedicated to study complex networks. The immunization process is the solution to these challenges and hence the importance of obtaining immunization strategies that control these spreads. In this paper, we evaluate the effectiveness of the DIL-Wα ranking in the immunization of nodes that are attacked by an infectious disease that spreads on an edge-weighted graph using a graph-based SIR model. The experimentation was done on real and scale-free networks and the results illustrate the benefits of this ranking.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Protection Strategy for Edge-Weighted Graphs in Disease Spread","attachmentId":87624133,"attachmentType":"pdf","work_url":"https://www.academia.edu/81654286/Protection_Strategy_for_Edge_Weighted_Graphs_in_Disease_Spread","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/81654286/Protection_Strategy_for_Edge_Weighted_Graphs_in_Disease_Spread"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="65644448" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/65644448/On_the_Effect_of_Complex_Network_Topology_in_Managing_Epidemic_Outbreaks">On the Effect of Complex Network Topology in Managing Epidemic Outbreaks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4368914" href="https://lp.academia.edu/DmytroDosyn">Dmytro Dosyn</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper investigates the effect of the structure of the contact network on the dynamics of the epidemic outbreak. In particular, we focus on the peak number of critically infected nodes (PCIN), determining the maximum workload of intensive healthcare units that should be kept low when managing an epidemic. As a realistic model and simulation method, we developed a continuous-time Markov chain model, and an open-source available efficient simulation software based on Gillespie&#39;s Stochastic Simulation Algorithm (SSA). Virus propagation is compared on random graph models featuring a selected range of complex network topologies: Erdős-Rényi, Watts-Strogatz, Barabási-Albert and complete graph (Clique). Continuous-time Markov chains are used to simulate the infection process. The simulation was performed in networks with 200 nodes and different numbers of edges. In addition, we study age- and gender-determined and weighted characteristics of nodes on the PCIN as well as the correla...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"On the Effect of Complex Network Topology in Managing Epidemic Outbreaks","attachmentId":77150884,"attachmentType":"pdf","work_url":"https://www.academia.edu/65644448/On_the_Effect_of_Complex_Network_Topology_in_Managing_Epidemic_Outbreaks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/65644448/On_the_Effect_of_Complex_Network_Topology_in_Managing_Epidemic_Outbreaks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":113759551,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":113759551,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_113759551" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="112318359" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/112318359/Immunization_Strategies_Based_on_the_Overlapping_Nodes_in_Networks_with_Community_Structure">Immunization Strategies Based on the Overlapping Nodes in Networks with Community Structure</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="621317" href="https://u-bourgogne.academia.edu/hocinecherifi">Hocine Cherifi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computational Social 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data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Epidemic spreading and immunization strategy in multiplex networks","attachmentId":41395934,"attachmentType":"pdf","work_url":"https://www.academia.edu/20480089/Epidemic_spreading_and_immunization_strategy_in_multiplex_networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/20480089/Epidemic_spreading_and_immunization_strategy_in_multiplex_networks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="2" data-entity-id="91338358" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/91338358/Reactive_immunization_on_complex_networks">Reactive immunization on complex networks</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="12976326" href="https://unisalento.academia.edu/eleonoraalfinito">eleonora alfinito</a></div><p class="ds-related-work--metadata ds2-5-body-xs">EPL (Europhysics Letters), 2017</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Reactive immunization on complex networks","attachmentId":94654084,"attachmentType":"pdf","work_url":"https://www.academia.edu/91338358/Reactive_immunization_on_complex_networks","alternativeTracking":true}"><span class="material-symbols-outlined" 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data-author-id="47472151" href="https://independent.academia.edu/GaoxiXiao">Gaoxi Xiao</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Physics Letters A, 2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Epidemics spreading in interconnected complex networks","attachmentId":45004142,"attachmentType":"pdf","work_url":"https://www.academia.edu/24674197/Epidemics_spreading_in_interconnected_complex_networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/24674197/Epidemics_spreading_in_interconnected_complex_networks"><span class="ds2-5-text-link__content">View 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