ArticleBMC infectious diseases2025
Forecasting antimicrobial resistance in China using a hybrid ARIMA-GM(1,1) model.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Tuberculosis incidence and mortality trends in mainland China, 2004-2024: control program and elimination progress.Tropical medicine and health · 2026Article
- Antimicrobial resistance trends among dominant pathogens in six clinical departments of the Fourth Affiliated Hospital of Guangxi Medical University, 2020-2024.Frontiers in public health · 2026Article
- Seven-Year Surveillance and AI-Based Forecasting of Antimicrobial Resistance in PediatricInfection and drug resistance · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo evaluate the application value of the ARIMA-GM(1,1) combined model in predicting the resistance rates of key drug-resistant bacteria in China, providing a scientific basis for optimizing antimicrobial management strategies.
methodsBased on data from the China Antibacterial Resistance Surveillance Network from 2014 to 2023, we selected six types of key drug-resistant bacteria, including methicillin-resistant Staphylococcus aureus (MRSA) and cefotaxime/ceftriaxone-resistant Klebsiella pneumoniae (CTX/CRO-R-KP), to construct the ARIMA-GM(1,1) combined model. The model performance was evaluated using five metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R
resultsThe model exhibited strong predictive performance, with MSE, RMSE, MAE, and MAPE below 10 for all six strains, and R
conclusionThe ARIMA-GM(1,1) model has been statistically validated in predicting the resistance rates of MRSA, CTX/CRO-R-KP, CRKP, and CRPA, indicating a significant downward trend driven by the National Action Plan for Curbing Bacterial Drug Resistance (2016-2020). While effective in capturing temporal dynamics, future research should integrate antibiotic usage data and other influencing factors for more targeted interventions.
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