deterministic model

Pathogenic factors can affect several groups of an epidemic. In this paper, by using the Brownian motion, an Ito stochastic differential equation is presented to express the stochastic behavior of an infected disease based on mean and variance. The model includes different species of population with the presence of suspected, exposed, infected and immune types. The model covers all the previous models including SI, SIS, SIR, SEIR, and SIRS. Also, the incubation, an important period in the epidemic, is considered. Finally, a few numerical examples of this model's behavior are presented.

The link between geographic information systems and decision making approach own the invention and development of spatial data melding method. These methods combine different data sets, to achieve better results. In this paper, the Bayesian melding method for combining the measurements and outputs of deterministic models and kriging are considered. Then the ozone data in Tehran city are analyzed by using these methods. Next their results are evaluated and compared in terms of mean square error criterion. Finally, discussion and results are given.