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PLoS Comput Biol. 2014 May 22;10(5):e1003643. doi: 10.1371/journal.pcbi.1003643. eCollection 2014.

An innovative influenza vaccination policy: targeting last season's patients.

Author information

1
Department of Epidemiology of Microbial Diseases, Yale University, New Haven, Connecticut, United States of America; Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel.
2
Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel; Faculty of Business Administration, Ono Academic College, Kiryat Ono, Israel.
3
Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel.
4
Department of Health Systems Management, Ben Gurion University of the Negev, Beersheba, Israel.
5
Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.
6
Department of Epidemiology of Microbial Diseases, Yale University, New Haven, Connecticut, United States of America.
7
Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel; Department of Health Systems Management, Ben Gurion University of the Negev, Beersheba, Israel; Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, United States of America.

Abstract

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.

PMID:
24851863
PMCID:
PMC4031061
DOI:
10.1371/journal.pcbi.1003643
[Indexed for MEDLINE]
Free PMC Article
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