Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link
Volume 29, Number 7—July 2023
Letter

Challenges in Forecasting Antimicrobial Resistance

Cite This Article

To the Editor: We read with interest the article by Pei et al. (1), which discussed challenges in forecasting antimicrobial resistance (AMR) and emphasized the need for improved AMR predictive intelligence. We complement the authors on their findings and share our experience with a threshold-logistic modeling concept that we recently introduced to improve understanding of the relationship between antimicrobial drug use thresholds and incidence of resistant pathogens and a threshold transfer function model that can be used to project AMR prevalence (2,3).

AMR modeling and forecasting are challenging because of the evolutionary ability of pathogens to adapt to changing environmental conditions. To be useful, a model should address time-varying effects of explanatory variables on response function and changes in the structural relationship between predictor and target variables. Concepts such as threshold transfer function modeling that includes autoregressive moving average components can partially address those issues. Model recalibration is essential and dictated by breakdown in predictive ability. Model parameters can be reestimated after recording new observations. Model structure does not change, but parameter estimates are updated to reflect new information. If statistical significance of a parameter falls below a defined confidence level, model structure might need modification. In practice, model estimation and assessment can be automated. Forecasting model accuracy can also be automated by comparing ongoing performance to baseline accuracy. When model or predictive performance degradation is flagged, a more comprehensive model recalibration is dictated. The time between model calibrations is unknown and depends on the stability of identified relationships in the model and degree to which evolutionary changes in AMR are observed. Further real-world testing is required to determine other factors that can explain resistance and define thresholds, find optimal interventions to reduce antimicrobial drug use to identified thresholds, and assess feasibility of implementing those interventions in daily clinical practice.

Top

Mamoon A. AldeyabComments to Author  and William J. Lattyak
Author affiliations: University of Huddersfield, Huddersfield, UK (M.A. Aldeyab); Scientific Computing Associates Corp., River Forest, Illinois, USA (W.J. Lattyak)

Top

References

  1. Pei  S, Blumberg  S, Vega  JC, Robin  T, Zhang  Y, Medford  RJ, et al.; CDC MIND-Healthcare Program. CDC MIND-Healthcare Program. Challenges in forecasting antimicrobial resistance. Emerg Infect Dis. 2023;29:67985. DOIPubMedGoogle Scholar
  2. Aldeyab  MA, Bond  SE, Conway  BR, Lee-Milner  J, Sarma  JB, Lattyak  WJ. Identifying antibiotic use targets for the management of antibiotic resistance using an extended-spectrum β-lactamase-producing Escherichia coli case: a threshold logistic modeling approach. Antibiotics (Basel). 2022;11:1116. DOIPubMedGoogle Scholar
  3. Aldeyab  MA, Bond  SE, Conway  BR, Lee-Milner  J, Sarma  JB, Lattyak  WJ. A threshold logistic modelling approach for identifying thresholds between antibiotic use and methicillin-resistant Staphylococcus aureus incidence rates in hospitals. Antibiotics (Basel). 2022;11:1250. DOIPubMedGoogle Scholar

Top

Cite This Article

DOI: 10.3201/eid2907.230489

Original Publication Date: May 30, 2023

Related Links

Top

Table of Contents – Volume 29, Number 7—July 2023

EID Search Options
presentation_01 Advanced Article Search – Search articles by author and/or keyword.
presentation_01 Articles by Country Search – Search articles by the topic country.
presentation_01 Article Type Search – Search articles by article type and issue.

Top

Comments

Please use the form below to submit correspondence to the authors or contact them at the following address:

Mamoon A Aldeyab, Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK

Send To

10000 character(s) remaining.

Top

Page created: May 11, 2023
Page updated: June 21, 2023
Page reviewed: June 21, 2023
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
file_external