Virtual useR! 2024

Antimicrobial resistance (AMR) has become a major public health challenge in the 21st century, posing a global health crisis that jeopardizes modern medicine. The traditional time-series analysis methods such as the Auto-regressive moving average model has been used to analyze and forecast AMR rates. However, these methods are unsuitable when analyzing rates or proportions that feature zero or one. This study proposes a new time-series model called DeβARMA (Degenerate Beta Auto-regressive moving average) that fits data in the interval [0, 1) or (0, 1]. This model is designed to predict the rate of AMR and plan accordingly. Healthcare providers need to be alerted in real-time to the AMR rate patterns in their respective settings so that they can better anticipate changes in resistance rates over time and develop more effective anti-microbial management policies. Shiny is an exciting R programming tool for creating various applications such as exploratory data analysis, statistical inference, and regression analysis. This article highlights DeβARMA, a specialized tool for modeling time-series data with zeroes or ones, adeptly handling lag effects and regressor variables.