Connecting network properties of rapidly disseminating epizoonotics

dc.contributor.authorRivas, Ariel L.
dc.contributor.authorFasina, Folorunso Oludayo
dc.contributor.authorHoogesteijn, Almira L.
dc.contributor.authorKonah, Steven N.
dc.contributor.authorFebles, Jose L.
dc.contributor.authorPerkins, Douglas J.
dc.contributor.authorHyman, James M.
dc.contributor.authorFair, Jeanne M.
dc.contributor.authorHittner, James B.
dc.contributor.authorSmith, Steven D.
dc.contributor.editorVespignani, Alessandro
dc.date.accessioned2012-09-03T10:27:28Z
dc.date.available2012-09-03T10:27:28Z
dc.date.issued2012-06-25
dc.description.abstractBACKGROUND: To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure. METHODS: Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) ‘connectivity’, a model that integrated bio-physical concepts (the agent’s transmission cycle, road topology) into indicators designed to measure networks (‘nodes’ or infected sites with short- and long-range links), and 2) ‘contacts’, which focused on infected individuals but did not assess connectivity. RESULTS: The connectivity model showed five network properties: 1) spatial aggregation of cases (disease clusters), 2) links among similar ‘nodes’ (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a ‘‘20:800 pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads. CONCLUSIONS: Geo-temporal constructs of Network Theory’s nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.en
dc.description.librarianab2012en
dc.description.librarianab2013 (Author correction)
dc.description.sponsorshipThe National Veterinary Research Institute, Vom, Plateau, Nigeria; the Center for Non-Linear Studies of Los Alamos National Laboratory; and partially funded by Defense Threat Reduction Agency (DTRA) Grant CBT-09-IST-05-1-0092 (to JMF).en
dc.description.urihttp://www.plosone.orgen
dc.identifier.citationRivas AL, Fasina FO, Hoogesteyn AL, Konah SN, Febles JL, et al. (2012) Connecting Network Properties of Rapidly Disseminating Epizoonotics. PLoS ONE 7(6): e39778. DOI: 10.1371/journal.pone.0039778.en
dc.identifier.issn1932-6203
dc.identifier.other10.1371/journal.pone.0039778
dc.identifier.other16416667800
dc.identifier.otherH-9699-2013
dc.identifier.urihttp://hdl.handle.net/2263/19690
dc.language.isoenen
dc.publisherPublic Library of Scienceen
dc.relation.requiresAdobe Acrobat Readeren
dc.rights© 2012 Rivas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution Licenseen
dc.subjectNetwork propertiesen
dc.subjectEpizoonoticsen
dc.subject.lcshCommunicable diseases in animalsen
dc.subject.lcshVeterinary epidemiologyen
dc.titleConnecting network properties of rapidly disseminating epizoonoticsen
dc.typeArticleen

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