AUTHOR=Lobo Jevitha , Kamath Asha , Kalwaje Eshwara Vandana 

TITLE=Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.969777

DOI=10.3389/fpubh.2022.969777

ISSN=2296-2565

ABSTRACT=Background: Antimicrobial resistance has emerged as one of the foremost public health troubles
 of the twenty-first century. This has ended in a public health disaster in the global situation, which
 threatens the exercise of present-day remedies. There is an urgent requirement for cost-effective
 strategy to reduce antimicrobial resistance. Infectious disease control researchers most often
 analyze and predict antimicrobial resistance rate data which includes zeros or ones. Commonly
 used time-series analyses such as the Auto-regressive moving average model are inappropriate for
 such data and may arrive at biased results.
 Objective: To propose a time-series model for continuous rates or proportions when the interval
 of the series include zeros or ones and compare the model with existing models.
 Data: The Escherichia coli, isolated from blood cultures showing variable susceptibility results to
 different antimicrobial agents have been obtained from a clinical microbiology laboratory of a tertiary
 care hospitals, Udupi district, Karnataka, during the years between 2011 to 2019.
 Methodology: We proposed an Extended Beta Autoregressive model which is a mixture of
 continuous and discrete distributions with probability mass at zero or one. The proposed model
 includes autoregressive terms along with explanatory variables. The estimation is done using
 maximum partial likelihood with a non-linear optimization algorithm. An R shiny app has been provided
 for the same.
 Results: The proposed Extended Beta Autoregressive model performed well compared to the
 existing Auto-regressive moving average model. The forecasted antimicrobial resistance rate has
 obtained for the next six months.
Conclusions: The findings of this article could be beneficial to the infectious disease researchers
 to use an appropriate time-series model to forecast the resistance rate for the future and to have
 better or advance public health policies to control the rise in resistance rate.