Dynamical Immunization Strategy for Seasonal Epidemics

TL;DR

This paper introduces a dynamic immunization strategy for seasonal epidemics that leverages local infection data to optimize vaccination placement, effectively targeting both global and local hubs to reduce epidemic spread.

Contribution

It presents a novel adaptive immunization method based on local infection information, improving upon existing strategies for seasonal epidemic control.

Findings

Immunization targets both global and local hubs.

The strategy outperforms previous methods in simulations.

The approach is supported by a heterogeneous mean-field analysis.

Abstract

The topic of finding effective strategy to halt virus in complex network is of current interest. We propose an immunization strategy for seasonal epidemics that occur periodically. Based on the local information of the infection status from the previous epidemic season, the selection of vaccinated nodes is optimized gradually. The evolution of vaccinated nodes during iterations demonstrates that the immunization tends to locate in both global hubs and local hubs. We analyze the epidemic prevalence by a heterogeneous mean-field method and present numerical simulations of our model. This immunization performs superiorly to some other previously known strategies. Our work points out a new direction in immunization of seasonal epidemics.