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

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.

Figure 1. The relative risk of infection given parameters of centrality.

The mean and 95% confidence interval of relative risk of infection for an individual compared to the rest of the population, given his/her K-shell (panels A and B), and number of contacts (panels C and D) for cross-reactivity levels of 0% (panels A and C) and 80% (panels B and D) for effective reproductive number, R_e = 1.2 (dotted red), 1.4 (dashed blue) and 1.6 (dot-dashed green). A relative risk above one represents higher risk of infection, compared with the rest of the population.

Figure 2. Mean risk of infection following vaccination.

The mean risk of infection evaluated over the parameters ranges in Table 1 for RVP (dashed blue), AIP (dashed-doted red), PIP (dashed green), as well as no vaccination (solid black), for cross-reactivity levels of A and B) 0%, C and D) 40%, E and F) 60% G and H) 80%. In the second season, for RVP, AIP and PIP strategies, vaccination coverage for A, C, E and G) 15% and for B, D, F and H) 30%, and vaccine efficacy of 75%. PIP is preferable than RVP in reducing morbidity for panels A–F, and more preferable than AIP for panels A–D. As explained in the main text, the risk of infection decreases as the cross-reactivity increases.

Figure 3. Mean indifference curves for PIP vs. RVP and PIP vs. AIP.

The curves are shown as a function of the effective reproductive number and cross-reactivity for A) the Portland Network B) Barabási algorithm-based network C) Brightkite Network D) Gowalla Network. Above each curve, RVP/AIP is the recommended policy, whereas below the curve, PIP is recommended.