An Innovative Influenza Vaccination Policy: Targeting Last Season's Patients

Dan Yamin*, Arieh Gavious, Eyal Solnik, Nadav Davidovitch, Ran D. Balicer, Alison P. Galvani, Joseph S. Pliskin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Influenza 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.

Original languageEnglish
Article numbere1003643
JournalPLoS Computational Biology
Issue number5
StatePublished - May 2014
Externally publishedYes


FundersFunder number
National Institutes of HealthU01 GM105627
National Institute of General Medical SciencesU01GM105627


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