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VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota

Oksana E Glushchenko1,2, Nikita A Prianichnikov1, Evgenii I Olekhnovich1

  • 1Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency, Moscow, Russia.

Bioinformatics (Oxford, England)
|March 3, 2019
PubMed
Abstract

Insights

Antibiotic resistance in pathogens is a major health concern. The VERA model simulates resistance spread via horizontal gene transfer, aiding prediction and monitoring of antibiotic resistance in populations.

Area of Science:

  • Microbiology
  • Computational Biology
  • Epidemiology

Background:

  • Antibiotic resistance in bacterial pathogens poses a significant threat to modern healthcare.
  • The human microbiota can acquire and transfer antibiotic resistance determinants to pathogens through horizontal gene transfer.
  • Effective prediction and monitoring of antibiotic resistance in human populations are crucial.

Purpose of the Study:

  • To present the agent-based VERA model for simulating the spread of antibiotic resistance.
  • To incorporate factors such as healthcare settings, self-treatment, and antibiotic use in the model.
  • To assess optimal monitoring frequencies for infection spread.

Main Methods:

  • Agent-based modeling using the VERA model.
  • Simulation of pathogen spread, including horizontal gene transfer of resistance.
  • Inclusion of various factors influencing resistance dynamics, such as patient location and treatment regimens.

Main Results:

  • The VERA model simulates pathogen spread and horizontal gene transfer of resistance determinants from commensal microbiota.
  • Non-linear dependencies were observed, including an exponential relationship between total infections and average pathogen resistance.
  • The model allows for the assessment of optimal observation frequencies for infection spread.

Conclusions:

  • The VERA model provides a tool for understanding and predicting the spread of antibiotic resistance.
  • The model highlights the complex dynamics of antibiotic resistance, influenced by microbiota and treatment practices.
  • The findings support the development of strategies for monitoring and controlling antibiotic resistance.

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