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Related Experiment Video

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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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Reconstructing multi-strain pathogen interactions from cross-sectional survey data via statistical network inference.

Irene Man1,2, Elisa Benincà1, Mirjam E Kretzschmar2

  • 1Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.

Journal of the Royal Society, Interface
|August 9, 2023
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Summary
This summary is machine-generated.

Understanding pathogen strain interactions is crucial for infectious disease control. This study shows statistical network inference can accurately map these complex, heterogeneous relationships from survey data.

Keywords:
cross-sectional datainteractionsmulti-strainnetwork inferencepathogen

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Infectious Disease Dynamics

Background:

  • Infectious diseases frequently involve multiple pathogen species or strains.
  • Existing methods for inferring pathogen interactions are limited, often overlooking indirect effects and leading to biased results.
  • Accurate understanding of pathogen interactions is vital for effective disease intervention strategies.

Purpose of the Study:

  • To evaluate statistical network inference for reconstructing heterogeneous interactions among multiple pathogen strains.
  • To assess the ability of these methods to detect joint presence/absence patterns of pathogen strains within hosts using cross-sectional survey data.

Main Methods:

  • Applied various network models to simulated survey data representing endemic infection states with potential interactions.
  • Investigated the impact of regularization and penalization techniques for sample size on interaction network reconstruction.
  • Assessed the influence of host heterogeneity and explored corrections using individual-level risk factors.

Main Results:

  • Statistical network inference estimators converged to true interactions, demonstrating satisfactory performance in simulations.
  • Accurate reconstruction of complex interaction networks was achieved, particularly with regularization or penalization for sample size.
  • Host heterogeneity impacted performance but was successfully overcome by correcting for individual-level risk factors.

Conclusions:

  • Statistical network inference is a powerful tool for detecting multi-strain pathogen interactions from population-level survey data.
  • The developed methods can accurately reconstruct heterogeneous interaction networks, accounting for indirect effects.
  • This approach holds significant potential for improving epidemiological studies and informing targeted disease interventions.