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Objective <p>To 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.</p> Methods <p>Based on data from the China Antibacterial Resistance Surveillance Network from 2014 to 2023, we selected six types of key drug-resistant bacteria, including <i>methicillin-resistant Staphylococcus aureus</i> (MRSA) and <i>cefotaxime/ceftriaxone-resistant Klebsiella pneumoniae</i> (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 <i>R</i><sup>2</sup>, and the resistance rate trends for 2024–2028 were predicted.</p> Results <p>The model exhibited strong predictive performance, with MSE, RMSE, MAE, and MAPE below 10 for all six strains, and <i>R</i><sup>2</sup> values exceeding 0.8 for five strains. Projected resistance rates for 2024 are as follows: MRSA at 27.46% (95% CI: 26.90%-28.02%), CTX/CRO-R-KP at 26.47% (25.81%-27.12%), CRKP at 9.87% (8.99%-10.74%), and CRPA at 15.17% (14.10%-16.24%). By 2028, these rates are expected to decrease to 24.05% (22.81%-25.29%), 22.30% (20.82%-23.77%), 7.58% (6.53%-8.63%), and 11.01% (8.61%-13.40%) (all <i>P</i> &lt; 0.05). For 2024, resistance rates for CREC and CRAB are projected to be 1.63% (1.58%-1.68%) and 53.33% (51.37%-55.28%), respectively, with continued decline expected by 2028.</p> Conclusion <p>The 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.</p>

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Forecasting antimicrobial resistance in China using a hybrid ARIMA-GM(1,1) model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • Feng Liu,
  • Caixia Dang,
  • Hengliang Lv,
  • Ziqian Zhao,
  • Sijin Zhu,
  • Yang Wang,
  • Hongbin Song,
  • Yuanyong Xu,
  • Hui Chen

摘要

Objective

To 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.

Methods

Based 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 R2, and the resistance rate trends for 2024–2028 were predicted.

Results

The model exhibited strong predictive performance, with MSE, RMSE, MAE, and MAPE below 10 for all six strains, and R2 values exceeding 0.8 for five strains. Projected resistance rates for 2024 are as follows: MRSA at 27.46% (95% CI: 26.90%-28.02%), CTX/CRO-R-KP at 26.47% (25.81%-27.12%), CRKP at 9.87% (8.99%-10.74%), and CRPA at 15.17% (14.10%-16.24%). By 2028, these rates are expected to decrease to 24.05% (22.81%-25.29%), 22.30% (20.82%-23.77%), 7.58% (6.53%-8.63%), and 11.01% (8.61%-13.40%) (all P < 0.05). For 2024, resistance rates for CREC and CRAB are projected to be 1.63% (1.58%-1.68%) and 53.33% (51.37%-55.28%), respectively, with continued decline expected by 2028.

Conclusion

The 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.