  If we can lower the number of people needed to vaccinate for a community to
be immune against contagious diseases, we can save resources and life. A key to
reach such a lower threshold of immunization is to find and vaccinate people
who, through their behavior, are more likely to become infected and effective
to spread the disease than the average. Fortunately, the very behavior that
makes these people important to vaccinate can help us finding them. People you
have met recently are more likely to be socially active and thus central in the
contact pattern, and important to vaccinate. We propose two immunization
schemes exploiting temporal contact patterns. Both of these rely only on
obtainable, local information and could implemented in practice. We show that
these schemes outperform benchmark protocols in four real data sets under
various epidemic scenarios. The data sets are dynamic, which enables us to make
more realistic evaluations than other studies - we use information only about
the past to perform the vaccination and the future to simulate disease
outbreaks. We also use models to elucidate the mechanisms behind how the
temporal structures make our immunization protocols efficient.
