Estimating between-country migration in pneumococcal populations

Author:

Belman Sophie1ORCID,Pesonen Henri2ORCID,Croucher Nicholas J3ORCID,Bentley Stephen D1ORCID,Corander Jukka45ORCID

Affiliation:

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

2. Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital , Oslo, 0372 , Norway

3. MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, White City Campus, Imperial College London , London W12 0BZ , UK

4. Department of Biostatistics, University of Oslo , Oslo, 0372 , Norway

5. Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki , Espoo, Helsinki, 02150 , Finland

Abstract

Abstract Streptococcus pneumoniae (the pneumococcus) is a globally distributed, human obligate opportunistic bacterial pathogen which, although often carried commensally, is also a significant cause of invasive disease. Apart from multi-drug resistant and virulent clones, the rate and direction of pneumococcal dissemination between different countries remains largely unknown. The ability for the pneumococcus to take a foothold in a country depends on existing population configuration, the extent of vaccine implementation, as well as human mobility since it is a human obligate bacterium. To shed light on its international movement, we used extensive genome data from the Global Pneumococcal Sequencing project and estimated migration parameters between multiple countries in Africa. Data on allele frequencies of polymorphisms at housekeeping-like loci for multiple different lineages circulating in the populations of South Africa, Malawi, Kenya, and The Gambia were used to calculate the fixation index (Fst) between countries. We then further used these summaries to fit migration coalescent models with the likelihood-free inference algorithms available in the ELFI software package. Synthetic datawere additionally used to validate the inference approach. Our results demonstrate country-pair specific migration patterns and heterogeneity in the extent of migration between different lineages. Our approach demonstrates that coalescent models can be effectively used for inferring migration rates for bacterial species and lineages provided sufficiently granular population genomics surveillance data. Further, it can demonstrate the connectivity of respiratory disease agents between countries to inform intervention policy in the longer term.

Funder

Bill and Melinda Gates Foundation

Wellcome Sanger Institute

Wellcome

Medical Research Council

Department for International Development

Sir Henry Dale Fellowship

Royal Society

Publisher

Oxford University Press (OUP)

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