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Estimating between-country migration in pneumococcal populations.

Sophie Belman1, Henri Pesonen2, Nicholas J Croucher3

  • 1Parasites and Microbes, Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK.

G3 (Bethesda, Md.)
|March 20, 2024
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Summary
This summary is machine-generated.

Streptococcus pneumoniae (pneumococcus) migration between African countries is not uniform. Population genomics reveal country-specific movement patterns for different pneumococcal lineages, informing disease control strategies.

Keywords:
approximate bayesian computation (ABC)bacterial populationsgenomicsmigrationpneumococcus

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Area of Science:

  • Population Genomics
  • Bacterial Pathogenesis
  • Epidemiology

Background:

  • Streptococcus pneumoniae (pneumococcus) is a major cause of invasive bacterial disease worldwide.
  • Understanding pneumococcal dissemination patterns between countries is crucial for public health interventions.
  • Factors influencing pneumococcal spread include population structure, vaccination, and human mobility.

Purpose of the Study:

  • To estimate migration parameters and infer country-specific movement patterns of Streptococcus pneumoniae across Africa.
  • To investigate the heterogeneity in migration rates among different pneumococcal lineages.
  • To demonstrate the utility of coalescent models in tracking bacterial international movement.

Main Methods:

  • Utilized genome data from the Global Pneumococcal Sequencing project for populations in South Africa, Malawi, Kenya, and The Gambia.
  • Calculated fixation index (Fst) based on allele frequencies at housekeeping-like loci.
  • Applied likelihood-free inference with coalescent models using the ELFI software package to estimate migration rates.

Main Results:

  • Identified country-pair specific migration patterns for Streptococcus pneumoniae.
  • Observed significant heterogeneity in migration extents among different pneumococcal lineages.
  • Validated the inference approach using synthetic data.

Conclusions:

  • Coalescent modeling is effective for inferring bacterial migration rates with granular population genomics data.
  • Findings highlight the interconnectedness of respiratory pathogens between nations.
  • Results can inform long-term public health policy and intervention strategies for pneumococcal disease.