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Spatial-Temporal Networks for Antibiogram Pattern Prediction.

Xingbo Fu1, Chen Chen1, Yushun Dong1

  • 1University of Virginia, Charlottesville, USA.

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|May 19, 2023
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Summary

Predicting future antibiogram patterns is crucial for monitoring antibiotic resistance. A new framework, STAPP, effectively uses spatial and temporal data to forecast these resistance patterns, outperforming existing methods.

Keywords:
antibiogram patternsantibiotic resistanceattention mechanismspatial-temporal learning

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Area of Science:

  • * Public Health
  • * Infectious Disease Epidemiology
  • * Computational Biology

Background:

  • * Antibiograms track antibiotic resistance, aiding clinical decisions and identifying regional resistance trends.
  • * Understanding antibiogram patterns is vital for monitoring multi-drug resistant organisms and infectious disease prevalence.
  • * Predicting future antibiogram patterns presents challenges due to non-i.i.d data, temporal dependencies, and spatial influences.

Approach:

  • * Propose a novel Spatial-Temporal Antibiogram Pattern Prediction (STAPP) framework.
  • * STAPP effectively leverages pattern correlations, temporal dependencies, and spatial information for accurate predictions.
  • * Conducted extensive experiments on a real-world US dataset spanning 1999-2012 for 203 cities.

Key Points:

  • * Antibiogram patterns exhibit interdependencies and temporal/spatial correlations, necessitating advanced predictive models.
  • * The proposed STAPP framework addresses these complexities by integrating spatial and temporal data.
  • * Experimental results demonstrate STAPP's superior performance compared to baseline methods in antibiogram pattern prediction.

Conclusions:

  • * STAPP offers a robust solution for predicting future antibiogram patterns, enhancing surveillance of antibiotic resistance.
  • * The framework's ability to capture complex data relationships is key to its effectiveness.
  • * This work opens new avenues for proactive strategies against the spread of antimicrobial resistance.