Spatial identification of component-based relative risks

Article type: Research Article

Authors: Kadhem, Safaa K.

Affiliations: Department of Mathematics and Computer Applications, College of Science, Al Muthanna University | E-mail: safaakadhem@mu.edu.iq

Correspondence: [*] Corresponding author: Department of Mathematics and Computer Applications, College of Science, Al Muthanna University. E-mail: safaakadhem@mu.edu.iq.

Abstract: This article aims at identifying the high risk provinces in Iraq using a finite Poisson mixture. Through this methodology, the levels of relative risk is determined through identifying the number of components. In this article we do not investigate spatial correlation among regions and assume that the levels of risk observed in different regions are independent each other. The estimation of the model parameters and the model selection are performed using the Bayesian approach which allow to allocate each province to an identified risk level. We consider the data of the Coronavirus disease (COVID-19) infections in 18 provinces in Iraq and determining the levels of relative risks of this pandemic. The results are spatially shown in map which illustrates that the best Bayesian model fitted the data is 3 components model (high, medium and low risk).

Keywords: MCMC, relative risk mapping, mixture model, COVID-19

DOI: 10.3233/MAS-210516

Journal: Model Assisted Statistics and Applications, vol. 16, no. 1, pp. 65-72, 2021