ArticleFrontiers in microbiology2023
Predicting antimicrobial resistance of bacterial pathogens using time series analysis.
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Behaviour ofAntibiotics (Basel, Switzerland) · 2026Article
- Intrinsic temporal structure and lagged environmental effects shape the dynamics of airborne microscopic eukaryotes.Applied and environmental microbiology · 2026Article
- Two decades of ESKAPE pathogens: Longitudinal analysis of antibiotic resistance trends between 2002 and 2024 in a Hungarian clinical centre.Epidemiology and infection · 2026Article
- Antimicrobial use and Escherichia coli resistance patterns in Hungarian pig farms: a data-driven farm-level analysis.Scientific reports · 2026Article
- Modeling and predicting water consumption in fattening pigs using autoregressive moving average with external parameters.Scientific reports · 2026Article
- From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.Frontiers in public health · 2026Article
- Antibiotic susceptibility patterns of clinical isolates of salmonella species producing extended spectrum beta lactamases as predictor of multidrug resistance in a tertiary hospital, Southeastern Nigeria.BMC infectious diseases · 2025Article
- Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning.Scientific reports · 2025Article
- Long-term forecast for antibacterial drug consumption in Germany using ARIMA models.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand.Animals : an open access journal from MDPI · 2025Article
- Analysis and prediction of the incidence temporal trends of echinococcosis in China from 2010 to 2021.Scientific reports · 2025Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Role of artificial intelligence in bacterial diagnostics and surveillance of anti-microbial resistance.Digital healthReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (
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