Subsidy strategy based on history information can stimulate voluntary vaccination behaviors on seasonal diseases

Abstract

Subsidy policies are always used to offer some incentive for individual voluntary vaccination behaviors. The selection of subsidized individuals in proposed policies, such as random subsidy (RAN) and target subsidy (TAR), do not always consider an individual’s history of vaccination behaviors. In this paper, we studied a seasonal influenza-like disease model and proposed two history information-based subsidy policies in which individuals are selected as donees based on vaccination information in the previous seasons: HI-RAN randomly selects individuals who did not voluntarily vaccinate in the previous season, and HI-TAR combines the degree centrality on this basis. Simulations in different networks show that the two proposed subsidy policies both limit the extent of an epidemic outbreak, and the HI-TAR policy is more effective. Moreover, both of our proposed policies are most effective when only one step history information is considered. Through microscopic analysis of the evolution of vaccination behaviors, we found history information-based subsidy policies can enhance the vaccination probability of non-hub nodes. Our work is expected to provide valuable information for vaccination policymaking by considering vaccination history behaviors.