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Two-phase importance sampling for inference about transmission trees.

Elina Numminen1, Claire Chewapreecha2, Jukka Sirén3

  • 1Department of Mathematics and Statistics, University of Helsinki, PO Box 68, 00014 Helsinki, Finland elina.numminen@helsinki.fi.

Proceedings. Biological Sciences
|September 26, 2014
PubMed
Summary
This summary is machine-generated.

We developed a new statistical method to trace infectious disease transmission pathways using molecular data. This approach effectively handles missing data, revealing that closer geographic proximity between households increases plausible transmission routes.

Keywords:
Streptococcus pneumoniamolecular epidemiologytransmission tree

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Area of Science:

  • Statistics
  • Computational Biology
  • Epidemiology

Background:

  • Inferring infectious pathogen transmission pathways from molecular sequence data is a growing area of interest in statistics.
  • Computational challenges arise from the high dimensionality of missing data in many datasets.

Purpose of the Study:

  • To introduce an importance sampling scheme for inferring transmission pathways and pathogen phylogenies.
  • To develop a method that can handle arbitrary transmission models, overcoming limitations of earlier approaches.

Main Methods:

  • Implemented an importance sampling scheme to sample transmission trees and phylogenies from suitable importance distributions.
  • Applied the scheme to analyze Streptococcus pneumoniae transmission within a refugee camp, using incomplete host data.

Main Results:

  • The method effectively alleviates inference challenges associated with large missing data dimensions.
  • Analysis revealed within-household transmission dynamics and plausible transmission pairs between households.
  • Geographic proximity was a significant factor, with closer households showing higher plausible transmission probability than expected by chance.

Conclusions:

  • The developed importance sampling scheme offers a flexible and computationally tractable approach for pathogen transmission inference.
  • Findings highlight the importance of geographic proximity in understanding infectious disease transmission dynamics, even when direct transmission probabilities are low.