A flexible INAR(1) time series model with dependent zero-inflated count series and medical contagious cases

M. Shirozhan, Hassan S. Bakouch and M. Mohammadpour

Mathematics and Computers in Simulation (MATCOM), 2023, vol. 206, issue C, 216-230

Abstract: In order to understand the behavior of disease transmission and develop better policies to overcome the problem, time series modeling and forecasting of infectious diseases are crucial. Therefore, a flexible INAR(1) time series model based on dependent zero-inflated count series is proposed. A flexible discrete distribution is considered for innovation terms of the process with some interesting behavior. Some statistical properties of the proposed INAR(1) time series model are provided, and its interpretation of contagious diseases is represented. The unknown parameters of the proposed process are estimated through several estimation methods. The efficiency of the estimates is evaluated by the Monte Carlo simulation approach. Fitting, modeling and analyzing some recent contagious cases are investigated, namely the weekly counts of Hantavirus, Chickenpox and Tuberculosis diseases. The earlier data sets forecasts are investigated under coherent procedures, including the median and modified Sieve bootstrap approaches.

Keywords: Dependent counting series; Estimation; Forecasting; Modified bootstrap; Zero-inflation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:206:y:2023:i:c:p:216-230

DOI: 10.1016/j.matcom.2022.11.014

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