An innovative influenza vaccination policy: targeting last season's patients.
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.
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.
Dan
D
Yamin
Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel; Faculty of Business Administration, Ono Academic College, Kiryat Ono, Israel.
Arieh
A
Gavious
Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel.
Eyal
E
Solnik
Department of Health Systems Management, Ben Gurion University of the Negev, Beersheba, Israel.
Nadav
N
Davidovitch
Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.
Ran D
RD
Balicer
Department of Epidemiology of Microbial Diseases, Yale University, New Haven, Connecticut, United States of America.
Alison P
AP
Galvani
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.
Joseph S
JS
Pliskin
D000465:Algorithms; D004196:Disease Outbreaks / Q000517:prevention & control* / Q000706:statistics & numerical data; D006291:Health Policy; D006801:Humans; D015994:Incidence; D007252:Influenza Vaccines / Q000627:therapeutic use*; D007251:Influenza, Human / Q000453:epidemiology* / Q000517:prevention & control*; D007557:Israel / Q000453:epidemiology; D032541:Mass Vaccination / Q000379:methods / Q000706:statistics & numerical data*; D012621:Seasons; D016896:Treatment Outcome
D007252:Influenza Vaccines