Computational health engineering applied to model infectious diseases and antimicrobial resistance spread

Research output: Contribution to journalReview article

Abstract

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host-pathogen-protein interactions, combined with a better understanding of a host's immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

Details

Authors
Organisations
External organisations
  • University of Georgia
  • Complejo Hospitalario Universitario de Santiago
  • Clarkson University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Infectious Medicine
  • Microbiology

Keywords

  • Antimicrobial resistance, Health engineering, Infection disease, Mathematic models
Original languageEnglish
Article number2486
JournalApplied Sciences (Switzerland)
Volume9
Issue number12
Publication statusPublished - 2019 Jun
Publication categoryResearch
Peer-reviewedYes