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Challenges in Forecasting Antimicrobial Resistance
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Centers for Disease Control and Prevention
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045679/ https://www.ncbi.nlm.nih.gov/pubmed/36958029 http://dx.doi.org/10.3201/eid2904.221552 |
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author | Pei, Sen Blumberg, Seth Vega, Jaime Cascante Robin, Tal Zhang, Yue Medford, Richard J. Adhikari, Bijaya Shaman, Jeffrey |
author_facet | Pei, Sen Blumberg, Seth Vega, Jaime Cascante Robin, Tal Zhang, Yue Medford, Richard J. Adhikari, Bijaya Shaman, Jeffrey |
author_sort | Pei, Sen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10045679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Centers for Disease Control and Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-100456792023-04-01 Challenges in Forecasting Antimicrobial Resistance Pei, Sen Blumberg, Seth Vega, Jaime Cascante Robin, Tal Zhang, Yue Medford, Richard J. Adhikari, Bijaya Shaman, Jeffrey Emerg Infect Dis Perspective 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. Centers for Disease Control and Prevention 2023-04 /pmc/articles/PMC10045679/ /pubmed/36958029 http://dx.doi.org/10.3201/eid2904.221552 Text en https://creativecommons.org/licenses/by/4.0/Emerging Infectious Diseases is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited. |
spellingShingle | Perspective Pei, Sen Blumberg, Seth Vega, Jaime Cascante Robin, Tal Zhang, Yue Medford, Richard J. Adhikari, Bijaya Shaman, Jeffrey Challenges in Forecasting Antimicrobial Resistance |
title | Challenges in Forecasting Antimicrobial Resistance |
title_full | Challenges in Forecasting Antimicrobial Resistance |
title_fullStr | Challenges in Forecasting Antimicrobial Resistance |
title_full_unstemmed | Challenges in Forecasting Antimicrobial Resistance |
title_short | Challenges in Forecasting Antimicrobial Resistance |
title_sort | challenges in forecasting antimicrobial resistance |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045679/ https://www.ncbi.nlm.nih.gov/pubmed/36958029 http://dx.doi.org/10.3201/eid2904.221552 |
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