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Updated: Jul 2, 2025
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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
Published on: December 27, 2010
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Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes
Fan Bu1,2, Joseph Kagaayi3, Mary Kate Grabowski4
1Department of Biostatistics, University of California - Los Angeles, Los Angeles, CA 90024, United States.
Biometrics
|February 19, 2024
Summary
This study introduces a novel spatial model to analyze human immunodeficiency virus (HIV) transmission patterns using deep-sequencing data. The method probabilistically infers transmission links and directions, offering a more comprehensive understanding of disease spread.
Area of Science:
- Epidemiology
- Computational Biology
- Statistical Modeling
Background:
- Viral deep-sequencing offers higher resolution than Sanger sequencing for understanding disease transmission.
- Phylogenetic analyses of viral sequences provide evidence for transmission but have inherent uncertainties.
- Existing methods often require preclassification of transmission statuses, limiting their flexibility.
Purpose of the Study:
- To develop a novel spatial Poisson process model for inferring human immunodeficiency virus (HIV) transmission flow patterns.
- To jointly infer latent transmission statuses and transmission surfaces from deep-sequencing data.
- To provide a probabilistic and computationally advantageous framework for analyzing HIV transmission networks.
Main Methods:
- Utilized a spatial Poisson process model representing individuals as typed points with covariates (e.g., age, gender).
- Incorporated scores from deep-sequence phylogenetic analysis as evidence for transmission linkage and direction.
- Employed a fully Bayesian inference scheme to probabilistically learn transmission statuses and surfaces without preclassification.
- Modeled continuous spatial processes for computational efficiency compared to discretized covariate space methods.
Main Results:
- Successfully inferred latent transmission statuses and transmission flow surfaces.
- Demonstrated the ability to capture high-resolution age structures in HIV transmission.
- Showcased the framework's effectiveness in a case study using deep-sequencing data from Southern Uganda.
Conclusions:
- The proposed spatial Poisson process model offers a robust and computationally efficient approach to analyzing HIV transmission dynamics.
- This probabilistic framework enhances the utilization of deep-sequencing data for understanding population-level disease transmission.
- The method provides valuable insights into factors influencing HIV transmission, such as age structures.
