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- Title
Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections.
- Authors
Krieger, Madison S.; Denison, Carson E.; Anderson, Thayer L.; Nowak, Martin A.; Hill, Alison L.
- Abstract
Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management. Author summary: The burden of drug-resistant bacterial infections is rising, and the fear that we are nearing a "post-antibiotic" era has seeped into the public consciousness. Scientists and public health officials often rely on mathematical models to predict changes in resistance levels over time and the effects of hypothetical interventions. However, most models struggle to reproduce common trends seen in real-world data, limiting their practical use. Here we propose a simple model to account for variations in the likelihood of taking antibiotics if infected, which arise within and between regions due to factors like drug-prescribing practices, healthcare access or care-seeking behavior, or the co-occurrence of other diseases. This model extension robustly reproduces trends seen in data, such as sustained coexistence of both drug-resistant and drug-sensitive strains of bacteria, and differences in resistance levels between similar or adjacent regions.
- Subjects
DRUG resistance in bacteria; DRUG efficacy; BACTERIAL diseases; PUBLIC officers; MEDICAL scientists; MULTIDRUG-resistant tuberculosis
- Publication
PLoS Computational Biology, 2020, Vol 16, Issue 7, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1008010