OBJECTIVES: Vaccination is the most efficient and cost effective method to prevent influenza, reducing morbidity and mortality rates not only for those vaccinated, but also for the entire population by reducing the spread of the virus. In the context of contact network epidemiology, an individual who is located in the center of the network is more likely to become infected. Thus, vaccinating such individuals before others would be more efficient in reducing the influenza burden. METHODS: We show that immunizing those who have been infected in the previous season, especially before the peak of the disease, can substantially reduce infection rates for a wide range of influenza viruses. Using the Susceptible Infected Recovered (SIR) compartmental model we ran 2,100,000 simulations, each reflecting two successive influenza seasons over a 1.5 million population contact network based on the Portland population. The second season was checked for a Random Vaccination Policy (RVP) and when using a vaccination policy prioritizing first those who were infected in the previous season especially before the peak (PFIP). RESULTS:  When no vaccination is offered, individuals who became infected in the previous season have a higher probability of becoming infected in the following season. Accordingly, PFIP can reduce the number of infected by up to 80% compared to RVP, even if the cross-reactivity rate between the viruses of two seasons is as high as 60-80%. We provide a simple tool describing the conditions when each policy should be used. CONCLUSIONS:  No CDC recommendations have ever considered the effect of a previous season on an individual in determining future vaccination policy. The PFIP can be achieved easily by sending pamphlets, telephone reminders or even family doctor recommendations to those who were diagnosed by the family doctor as suffering from influenza like illness (ILI) in the previous season.