A SEIQR model for pandemic influenza and its parameter identification

Abstract

In this paper, we first propose a pandemic influenza susceptib-leexposed- infected-quarantined-recovered (SEIQR) model and analyze the model properties. We then introduce a differential evolution (DE) algorithm for determining the numerical values of the parameters in the model. For a given set of measured data, e.g. from the first outbreak, all the values of the model parameters can be determined by the algorithm. We have also shown from numerical simulations that the DE algorithm yields the same parameter values for different sets of initial guesses. With the values of the parameters 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. © 2009 Academic Publications.