Optimal Vaccine Distribution Strategy for Different Age Groups of Population: A Differential Evolution Algorithm Approach

Xiao-Min Hu,Haihong Chen,Jun Zhang

doi:10.1155/2014/702973

Abstract

Vaccination is one of the effective ways for protecting susceptible individuals from infectious diseases. Different age groups of population have different vulnerability to the disease and different contact frequencies. In order to achieve the maximum effects, the distribution of vaccine doses to the groups of individuals needs to be optimized. In this paper, a differential evolution (DE) algorithm is proposed to address the problem. The performance of the proposed algorithm has been tested by a classical infectious disease transmission model and a series of simulations have been made. The results show that the proposed algorithm can always obtain the best vaccine distribution strategy which can minimize the number of infectious individuals during the epidemic outbreak. Furthermore, the effects of vaccination on different days and the vaccine coverage percentages have also been discussed.

Highlights

Infectious diseases are harmful to public health and have made millions of economic loses each year [1]
For solving the vaccine distribution optimization problem, we propose the use the differential evolution (DE) algorithm [14, 15] to enhance the effectiveness of vaccine protection
The vaccine distribution by the DE algorithm for the five age groups is shown in Figure 5 for the cases with the vaccines taken on day tvacc = 1 to 150 and 5% vaccine coverage

Summary

Introduction

Infectious diseases are harmful to public health and have made millions of economic loses each year [1]. A vaccine distribution strategy is proposed in this paper for different age groups of individuals. Vaccine distribution mainly focuses on the transmissibility and infection risk of the disease and the vulnerability of individuals [8,9,10]. Patel et al [12] proposed a genetic algorithm to find the optimal vaccine distributions to minimize the number of illnesses or deaths in the population. Tuite and his partners [13] used a transmission model to explain spatial spread of disease and identify optimal control interventions.

Infectious Disease Transmission and Control

Implementation of the Proposed Algorithm

Simulations and Analysis

Methods

Findings

Conclusion