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Analysing pneumococcal invasiveness using Bayesian models of pathogen progression rates.

Authors :
Løchen A
Truscott JE
Croucher NJ
Source :
PLoS computational biology [PLoS Comput Biol] 2022 Feb 17; Vol. 18 (2), pp. e1009389. Date of Electronic Publication: 2022 Feb 17 (Print Publication: 2022).
Publication Year :
2022

Abstract

The disease burden attributable to opportunistic pathogens depends on their prevalence in asymptomatic colonisation and the rate at which they progress to cause symptomatic disease. Increases in infections caused by commensals can result from the emergence of "hyperinvasive" strains. Such pathogens can be identified through quantifying progression rates using matched samples of typed microbes from disease cases and healthy carriers. This study describes Bayesian models for analysing such datasets, implemented in an RStan package (https://github.com/nickjcroucher/progressionEstimation). The models converged on stable fits that accurately reproduced observations from meta-analyses of Streptococcus pneumoniae datasets. The estimates of invasiveness, the progression rate from carriage to invasive disease, in cases per carrier per year correlated strongly with the dimensionless values from meta-analysis of odds ratios when sample sizes were large. At smaller sample sizes, the Bayesian models produced more informative estimates. This identified historically rare but high-risk S. pneumoniae serotypes that could be problematic following vaccine-associated disruption of the bacterial population. The package allows for hypothesis testing through model comparisons with Bayes factors. Application to datasets in which strain and serotype information were available for S. pneumoniae found significant evidence for within-strain and within-serotype variation in invasiveness. The heterogeneous geographical distribution of these genotypes is therefore likely to contribute to differences in the impact of vaccination in between locations. Hence genomic surveillance of opportunistic pathogens is crucial for quantifying the effectiveness of public health interventions, and enabling ongoing meta-analyses that can identify new, highly invasive variants.<br />Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AL was funded by an investigator-initiated grant to NJC from GlaxoSmithKline (https://www.gsk.com/en-gb/home/), who manufacture the PCV10 vaccine. NJC has consulted for Pfizer (https://www.pfizer.com/), who manufacture the PCV13 and PCV20 vaccines.

Details

Language :
English
ISSN :
1553-7358
Volume :
18
Issue :
2
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
35176026
Full Text :
https://doi.org/10.1371/journal.pcbi.1009389