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dc.contributor.authorYamin, Danen_US
dc.contributor.authorGavious, Ariehen_US
dc.contributor.authorSolnik, Eyalen_US
dc.contributor.authorDavidovitch, Nadaven_US
dc.contributor.authorBalicer, Ran D.en_US
dc.contributor.authorGalvani, Alison P.en_US
dc.contributor.authorPliskin, Joseph S.en_US
dc.date.accessioned2014-07-07T18:13:25Z
dc.date.issued2014en_US
dc.identifier.citationYamin, Dan, Arieh Gavious, Eyal Solnik, Nadav Davidovitch, Ran D. Balicer, Alison P. Galvani, and Joseph S. Pliskin. 2014. “An Innovative Influenza Vaccination Policy: Targeting Last Season's Patients.” PLoS Computational Biology 10 (5): e1003643. doi:10.1371/journal.pcbi.1003643. http://dx.doi.org/10.1371/journal.pcbi.1003643.en
dc.identifier.issn1553-734Xen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12406883
dc.description.abstractInfluenza vaccination is the primary approach to prevent influenza annually. WHO/CDC recommendations prioritize vaccinations mainly on the basis of age and co-morbidities, but have never considered influenza infection history of individuals for vaccination targeting. We evaluated such influenza vaccination policies through small-world contact networks simulations. Further, to verify our findings we analyzed, independently, large-scale empirical data of influenza diagnosis from the two largest Health Maintenance Organizations in Israel, together covering more than 74% of the Israeli population. These longitudinal individual-level data include about nine million cases of influenza diagnosed over a decade. Through contact network epidemiology simulations, we found that individuals previously infected with influenza have a disproportionate probability of being highly connected within networks and transmitting to others. Therefore, we showed that prioritizing those previously infected for vaccination would be more effective than a random vaccination policy in reducing infection. The effectiveness of such a policy is robust over a range of epidemiological assumptions, including cross-reactivity between influenza strains conferring partial protection as high as 55%. Empirically, our analysis of the medical records confirms that in every age group, case definition for influenza, clinical diagnosis, and year tested, patients infected in the year prior had a substantially higher risk of becoming infected in the subsequent year. Accordingly, considering individual infection history in targeting and promoting influenza vaccination is predicted to be a highly effective supplement to the current policy. Our approach can also be generalized for other infectious disease, computer viruses, or ecological networks.en
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofdoi:10.1371/journal.pcbi.1003643en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4031061/pdf/en
dash.licenseLAAen_US
dc.subjectBiology and Life Sciencesen
dc.subjectPopulation Biologyen
dc.subjectMedicine and Health Sciencesen
dc.subjectEpidemiologyen
dc.titleAn Innovative Influenza Vaccination Policy: Targeting Last Season's Patientsen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalPLoS Computational Biologyen
dash.depositing.authorPliskin, Joseph S.en_US
dc.date.available2014-07-07T18:13:25Z
dc.identifier.doi10.1371/journal.pcbi.1003643*
dash.contributor.affiliatedPliskin, Joseph


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