Parameter identification for pandemic influenza SEIQR model

Abstract

© 2008 Global Information Publisher (H.K) Co., Limited. All rights reserved. In this paper, we study the identification of model parameters for the pandemic influenza susceptibleexposed- infected-quarantine-recovered (SEIQR) model using the differential evolution (DE) algorithm. From a given set of the measured data, say from the first outbreak, all parameters used in the model can be determined by the algorithm. We have also shown from numerical investigation that the DE algorithm converges to parameter values for different initial guesses. With the parameter property determined, the model can then be used to capture the behavior of the next outbreaks of the disease. The work provides an effective tool for predicting the spread of the disease.