Journal article
Challenges in Forecasting Antimicrobial Resistance
Emerging infectious diseases, Vol.29(4), pp.679-685
04/01/2023
DOI: 10.3201/eid2904.221552
PMCID: PMC10045679
PMID: 36958029
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.
Details
- Title: Subtitle
- Challenges in Forecasting Antimicrobial Resistance
- Creators
- Pei SenSeth BlumbergJaime Cascante VegaRobin TalYue ZhangRichard MedfordBijaya AdhikariJeffrey ShamanCDC MIND-Healthcare Program
- Resource Type
- Journal article
- Publication Details
- Emerging infectious diseases, Vol.29(4), pp.679-685
- DOI
- 10.3201/eid2904.221552
- PMID
- 36958029
- PMCID
- PMC10045679
- NLM abbreviation
- Emerg Infect Dis
- ISSN
- 1080-6040
- eISSN
- 1080-6059
- Publisher
- Centers for Disease Control and Prevention
- Language
- English
- Date published
- 04/01/2023
- Academic Unit
- Computer Science
- Record Identifier
- 9984410851202771
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