Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app - Lobo, Jevitha; Kamath, Asha; Kalwaje Eshwara, Vandana (Frontiers in Public Health, 2023)
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.