Global prediction of antimicrobial resistance trends using statistical and machine learning models: Evaluating national action plan policy impacts through interrupted time series analysis
This study provides a comprehensive data-driven framework to monitor and forecast AMR, evaluate policy interventions, and, hence, suggest targeted interventions and strategies for each income group and region, moving beyond the one-size-fits-all approach.
Authors
- Amjad Khan (ORCID: https://orcid.org/0000-0002-6006-4159)
- Jie Chang (ORCID: https://orcid.org/0000-0001-7296-8641)
- Rehan Zafar Paracha (ORCID: https://orcid.org/0000-0003-4227-1354)
- Zamir Hussain (ORCID: https://orcid.org/0000-0001-5020-9169)
- Z Hussain (ORCID: https://orcid.org/0000-0001-9769-136X)
- Saima Mushtaq (ORCID: https://orcid.org/0000-0003-4168-2207)
- Linta Khalid
- Imran Sajid (ORCID: https://orcid.org/0000-0002-4781-1455)
- Iltaf Hussain (ORCID: https://orcid.org/0000-0003-0556-0181)
- Kashif Saleem
- Yu Fang
Institutions
- University of the Punjab (PK)
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
- National Institute of Health (PK)
- Xi'an Jiaotong University (CN)
- National University of Sciences and Technology (PK)
Publication Details
- Journal
- Journal of Global Antimicrobial Resistance
- Published
- 2025-12-19
- DOI
- https://doi.org/10.1016/j.jgar.2025.11.022
- Primary Topic
- Antibiotic Use and Resistance
- Type
- article
- Field-Weighted Citation Impact
- 0.00