Population-level mathematical modeling of antimicrobial resistance: A systematic review

Anna Maria Niewiadomska, Bamini Jayabalasingham, Jessica C. Seidman, Lander Willem, Bryan Grenfell, David Spiro, Cecile Viboud

Research output: Contribution to journalReview article

7 Scopus citations

Abstract

Background: Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work. Methods: We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens. Results: We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals. Conclusions: The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Original languageEnglish (US)
Article number81
JournalBMC Medicine
Volume17
Issue number1
DOIs
StatePublished - Apr 24 2019

All Science Journal Classification (ASJC) codes

  • Medicine(all)

Keywords

  • Antimicrobial
  • Communicable diseases
  • Computational
  • Epidemiology
  • Mathematical
  • Models
  • Resistance
  • Transmission

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    Niewiadomska, A. M., Jayabalasingham, B., Seidman, J. C., Willem, L., Grenfell, B., Spiro, D., & Viboud, C. (2019). Population-level mathematical modeling of antimicrobial resistance: A systematic review. BMC Medicine, 17(1), [81]. https://doi.org/10.1186/s12916-019-1314-9