Volume 29, Number 7—July 2023
Letter
Challenges in Forecasting Antimicrobial Resistance (Response)
In Response: Real-time evaluation of predictive models for antimicrobial resistance (AMR) is critical for real-world applications, as indicated in our recently published article (1). Aldeyab and Lattyak introduced a threshold-logistic regression model that links antimicrobial drug use to AMR prevalence in hospital settings (2). The authors advocate implementing and testing this model in hospitals to assess operational utility. I agree that this is a practical starting point to challenge time-series model use for real-time AMR predictions. Most time-series models have been validated in retrospective analyses. Translational research is needed to promote the use of those models for real-world AMR control.
The authors mention several practical considerations when applying time-series models in real time, including stationarity of both predictor and target variables and criteria for model recalibration. Evaluating methods to address those issues is crucial to achieve desirable performance in hospital settings. In addition to those technical challenges, several broader questions remain regarding model design and utility. First, how much AMR prevalence variation can be explained by antimicrobial drug use? Are there other essential factors (e.g., community introduction) that should be included in the model? Second, how will healthcare providers and hospitals use AMR forecasts? What policies will be informed by forecasts, and what are the downstream effects? Answers to those questions will help determine the eventual real-world utility of predictive models.
Evaluating real-time AMR prediction is a complicated task. By drawing experience from computer vision (3) and forecasts for other infectious diseases (4–6), open-access challenges with transparent and fair evaluation methods run in a common task framework (7) can substantially stimulate the advance of predictive methods and might produce robust application models. Such collaborative efforts are needed to evaluate existing methods, identify difficulties and solutions, and push the operational use of AMR predictive models forward.
Acknowledgment
This work was supported by the US Centers for Disease Control and Prevention, grant nos. U01CK000592 and 75D30122C14289.
References
- Pei S, Blumberg S, Vega JC, Robin T, Zhang Y, Medford RJ, et al.; CDC MIND-Healthcare Program. Challenges in forecasting antimicrobial resistance. Emerg Infect Dis. 2023;29:679–85. DOIPubMedGoogle Scholar
- Aldeyab MA, Lattyak WJ. Challenges in forecasting antimicrobial resistance. Emerg Infect Dis. 2023 Jul [date cited].
- Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52. DOIGoogle Scholar
- Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, Castro Rivadeneira AJ, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc Natl Acad Sci U S A. 2022;119:
e2113561119 . DOIPubMedGoogle Scholar - Reich NG, Brooks LC, Fox SJ, Kandula S, McGowan CJ, Moore E, et al. A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. Proc Natl Acad Sci U S A. 2019;116:3146–54. DOIPubMedGoogle Scholar
- Johansson MA, Apfeldorf KM, Dobson S, Devita J, Buczak AL, Baugher B, et al. An open challenge to advance probabilistic forecasting for dengue epidemics. Proc Natl Acad Sci U S A. 2019;116:24268–74. DOIPubMedGoogle Scholar
Original Publication Date: May 30, 2023
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Table of Contents – Volume 29, Number 7—July 2023
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Please use the form below to submit correspondence to the authors or contact them at the following address:
Sen Pei, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
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