Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens

Area of Science:

• Veterinary Medicine
• Microbiology
• Public Health

Background:

• Antibiotic overuse fuels antimicrobial resistance (AMR), endangering animal and human health.
• China faces challenges in AMR surveillance and risk assessment compared to global standards.
• This study addresses the need for advanced monitoring and risk evaluation of AMR in China.

Purpose of the Study:

• To develop advanced methods for monitoring antibiotic use and AMR data in China.
• To evaluate AMR risk using piglets and E. coli as a model.
• To highlight the severity of AMR and promote responsible antibiotic use.

Main Methods:

• Principal component analysis (PCA) for an anti-E. coli drug resistance index model.
• Second-order Monte Carlo simulation for a piglet E. coli disease risk assessment model.
• Development of a browser/server architecture-based visualization database system for pathogens.

Main Results:

• Hohhot identified as the highest AMR risk area for E. coli in China.
• The disease risk assessment model predicted a 7.174% probability of E. coli disease, with a 3.074% absolute error from the true 4.1% risk.
• The developed system and models offer a novel approach to AMR risk assessment.

Conclusions:

• An innovative method for rapid and accurate drug resistance risk assessment was established, using E. coli as a case study.
• The developed system and models are valuable for AMR monitoring and evaluation.
• Findings underscore the importance of prudent antibiotic use for food safety and public health.