Monitoring a bivariate INAR(1) process with application to Hepatitis A

Francis G. Pascual and Sherzod Akhundjanov

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 5, 1036-1058

Abstract: In this article, we study multivariate monitoring systems based on a bivariate integer-valued autoregressive process of order 1, BINAR(1). The charting procedures are evaluated using extensive simulated shift scenarios, and are compared to multiple univariate charts. The proposed methods improve the efficiency of surveillance systems by taking account of both pairwise correlation and autocorrelation in a bivariate data structure. The methods also account for overdispersion in responses for which the Poisson distribution is inappropriate. An example of hepatitis A incidents in Australia is presented to demonstrate the application of these methods. The proposed methods have important applications in public healthcare and biosurveillance as well as industrial and business applications.

Date: 2021
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DOI: 10.1080/03610926.2019.1645856

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