Global prediction of antimicrobial resistance trends using statistical and machine learning models: Evaluating national action plan policy impacts through interrupted time series analysis
Abstract
OBJECTIVE: Antimicrobial resistance (AMR) is a pressing global health challenge, particularly affecting low- and middle-income countries. This study aims to evaluate the spread of AMR both across time and across different regions of the world.
METHODS: We analysed clinical isolates from 65 countries. A country-specific time-series forecasting (i.e. seasonal autoregressive integrated moving average (SARIMA), long short-term memory (LSTM), and seasonal autoregressive integrated moving average-LSTM hybrid models) were performed for Acinetobacter baumannii in Argentina (2004-2030) as a case study to demonstrate model applicability for national-level prediction. Moreover, interrupted time series regression was applied to predict antibiotic-resistance trends and assess the global impact of national action plans.
RESULTS: Southeast Asia and Africa exhibited the highest AMR burdens, with Indonesia (0.65), Egypt (0.52), and Malawi (0.49) having the highest resistance scores. An income-based gradient was observed across key pathogens, third-generation cephalosporin and carbapenem-resistant Escherichia coli, Klebsiella pneumoniae, and A. baumannii were significantly more prevalent in low- and middle-income countries. Gender-wise analysis revealed significantly higher resistance rates in males across most antibiotics, especially levofloxacin. Age-stratified analyses revealed higher resistance in elderly populations, particularly to fluoroquinolones and β-lactams. Forecasting for A.…
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