Time series model for a proportion of antimicrobial resistance rate

Background: Antimicrobial resistance acts as a global problem in many regions of the world. The prevention and treatment of modern medicine are becoming ineffective. Governments all over the world are working effortlessly to overcome this problem and there is a requirement for extra care by the government and healthcare delivery systems to strengthen antimicrobial policy and standardize treatment guidelines. Objective: This study aims to forecast the antimicrobial resistance rate for the future and simultaneously to bring awareness of new time-series proportion models available to model the rate/proportion data in the field of clinical and public health by taking an example of antimicrobial resistance rate data. Methods: Data on Escherichia coli isolated from blood cultures showing variable susceptibility to different antimicrobial agents has received from a clinical microbiology laboratory of tertiary care hospital, Manipal, Karnataka, between the years June 2015 and December 2019. Beta auto-regressive moving average model is used to forecast the antimicrobial resistance rate data. To help non-statisticians an R shiny app named BARMA. app has been developed for the same. Results: A resistance rate of a total of 55-time points was used to forecast the resistance rate of E.coli to the antimicrobial Amoxicillin-clavulanic acid. On average, the forecasted resistance rate is 57% (50%–65%). Conclusion: Forecasting of antimicrobial resistance rate can help to alert healthcare policymakers to have appropriate precautionary measures and to attain the sustainable development goals (SDGs).