Challenges in Forecasting Antimicrobial Resistance

Author(s)

Sen Pei, Columbia University
Seth Blumberg, University of California, San Francisco
Jaime Cascante Vega, Columbia University
Tal Robin, Columbia University
Yue Zhang, University of Utah
Richard J. Medford, University of Texas Southwestern Medical Center at Dallas
Bijaya Adhikari, University of Iowa
Jeffrey Shaman, Columbia University

Document Type

Article

Publication Date

4-2023

Publication Source

Emerging Infectious Diseases

Volume

29

Issue

4

Publisher

Centers for Disease Control and Prevention

Inclusive pages

679-685

ISBN/ISSN

1080-6040

Peer Reviewed

yes

Abstract

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

Keywords

Antimicrobial resistance, antimicrobial-resistant organisms

Disciplines

Bacteria | Biology

Comments

Article is open-access. DOI: https://doi.org/10.3201/eid2904.221552

eCommons Citation

Pei, Sen; Blumberg, Seth; Vega, Jaime Cascante; Robin, Tal; Zhang, Yue; Medford, Richard J.; Adhikari, Bijaya; and Shaman, Jeffrey, "Challenges in Forecasting Antimicrobial Resistance" (2023). Research on Antimicrobial Resistance. 1.
https://ecommons.udayton.edu/amr_research/1