PEARS | Predicting the Evolution of Antibiotic Resistance in Streptococcus pneumoniae

Summary
Streptococcus pneumoniae (the pneumococcus) is a bacterial species generally commensal to humans, but which occasionally causes infections responsible for the death of 800, 000 infants each year worldwide. Multiple genotypes exhibiting resistance to antibiotics have emerged in past years. Intriguingly, despite extensive antibiotic consumption operating strong selection for resistance, the latter remains at a stable frequency, 15% on average over the last 20 years in Europe. This is paradoxical, as robust coexistence of resistant and sensitive strains is unexpected under the simplest epidemiological models. In this project, I will investigate the possibility that coexistence is instead maintained by a more complex mechanism, relying on local adaptation to several niches characterized by different rates of antibiotic administration. I will develop a series of novel models with increasing realism and relevance to the context of S. pneumoniae, drawing from the often separate fields of population genetics and epidemiology. Starting with simple but general two- and multiple-niches models that allow for analytical solutions to provide initial insights, I will then build a more complex simulation model parameterized with biologically realistic contact and treatment structures. Output of this model will be confronted to large-scale patterns of spatial variation in resistance observed in epidemiological datasets. The analysis of these models will help us understand what factors facilitate the maintenance of coexistence in S. pneumoniae. This work may lead to better treatment policies to manage antibiotic resistance in this major pathogen.
Unfold all
/
Fold all
More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/657768
Start date: 01-06-2015
End date: 31-05-2017
Total budget - Public funding: 195 454,80 Euro - 195 454,00 Euro
Cordis data

Original description

Streptococcus pneumoniae (the pneumococcus) is a bacterial species generally commensal to humans, but which occasionally causes infections responsible for the death of 800, 000 infants each year worldwide. Multiple genotypes exhibiting resistance to antibiotics have emerged in past years. Intriguingly, despite extensive antibiotic consumption operating strong selection for resistance, the latter remains at a stable frequency, 15% on average over the last 20 years in Europe. This is paradoxical, as robust coexistence of resistant and sensitive strains is unexpected under the simplest epidemiological models. In this project, I will investigate the possibility that coexistence is instead maintained by a more complex mechanism, relying on local adaptation to several niches characterized by different rates of antibiotic administration. I will develop a series of novel models with increasing realism and relevance to the context of S. pneumoniae, drawing from the often separate fields of population genetics and epidemiology. Starting with simple but general two- and multiple-niches models that allow for analytical solutions to provide initial insights, I will then build a more complex simulation model parameterized with biologically realistic contact and treatment structures. Output of this model will be confronted to large-scale patterns of spatial variation in resistance observed in epidemiological datasets. The analysis of these models will help us understand what factors facilitate the maintenance of coexistence in S. pneumoniae. This work may lead to better treatment policies to manage antibiotic resistance in this major pathogen.

Status

CLOSED

Call topic

MSCA-IF-2014-EF

Update Date

28-04-2024
Images
No images available.
Geographical location(s)
Structured mapping
Unfold all
/
Fold all
Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.3. EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions (MSCA)
H2020-EU.1.3.2. Nurturing excellence by means of cross-border and cross-sector mobility
H2020-MSCA-IF-2014
MSCA-IF-2014-EF Marie Skłodowska-Curie Individual Fellowships (IF-EF)