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

Exploiting temporal network structures of human interaction to effectively immunize populations