The evolution of antibiotic resistance in a structured host population

Author:

Blanquart François1234ORCID,Lehtinen Sonja34,Lipsitch Marc567,Fraser Christophe34

Affiliation:

1. Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, Paris, France

2. IAME, UMR 1137, INSERM, Université Paris Diderot, Site Xavier Bichat, Paris, France

3. Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK

4. Department of Infectious Disease Epidemiology, Imperial College London, London, UK

5. Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

6. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

7. Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA

Abstract

The evolution of antibiotic resistance in opportunistic pathogens such as Streptococcus pneumoniae , Escherichia coli or Staphylococcus aureus is a major public health problem, as infection with resistant strains leads to prolonged hospital stay and increased risk of death. Here, we develop a new model of the evolution of antibiotic resistance in a commensal bacterial population adapting to a heterogeneous host population composed of untreated and treated hosts, and structured in different host classes with different antibiotic use. Examples of host classes include age groups and geographic locations. Explicitly modelling the antibiotic treatment reveals that the emergence of a resistant strain is favoured by more frequent but shorter antibiotic courses, and by higher transmission rates. In addition, in a structured host population, localized transmission in host classes promotes both local adaptation of the bacterial population and the global maintenance of coexistence between sensitive and resistant strains. When transmission rates are heterogeneous across host classes, resistant strains evolve more readily in core groups of transmission. These findings have implications for the better management of antibiotic resistance: reducing the rate at which individuals receive antibiotics is more effective to reduce resistance than reducing the duration of treatment. Reducing the rate of treatment in a targeted class of the host population allows greater reduction in resistance, but determining which class to target is difficult in practice.

Funder

Li Ka Shing Foundation

National Institutes of Health

H2020 Marie Skłodowska-Curie Actions

National Institute of General Medical Sciences

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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