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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

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Published on: December 27, 2010

Approximate inference for longitudinal mechanistic HIV contact network.

Octavious Smiley¹, Till Hoffmann¹, Jukka-Pekka Onnela¹

¹Biostatistics, Harvard University, 677 Huntington Ave, Boston, MA 02115 USA.

Applied Network Science

|May 3, 2024

Summary

Network models aid infectious disease research. Longitudinal studies, collecting data twice, improve inference accuracy for sexually transmitted disease spread, but optimal timing and data are crucial.

Keywords:

ABC Agent based modeling HIV Inference MSM Mechanistic model Networks

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Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays

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Published on: September 26, 2011

Area of Science:

Epidemiology
Network Science
Computational Biology

Background:

Network models are vital for understanding infectious disease transmission dynamics.
Mechanistic network models offer advantages over traditional methods for studying diseases like sexually transmitted infections (STIs).
These models capture individual behaviors and network evolution, crucial for longitudinal analysis.

Purpose of the Study:

To implement and evaluate a discrete-time mechanistic network model for evolving contact networks.
To develop and apply an Approximate Bayesian Computation (ABC) based inference scheme for this model.
To assess the impact of longitudinal study designs on inference accuracy for STI spread.

Main Methods:

Implementation of a discrete-time mechanistic model for contact network evolution.
Application of an ABC-based approximate inference scheme for parameter estimation.
Comparison of inference accuracy between cross-sectional and two-wave longitudinal study designs.

Main Results:

A two-wave longitudinal study design significantly improves inference accuracy compared to a cross-sectional design.
Data collection twice can increase precision by up to 18%, contingent on wave spacing.
Inference gains are sensitive to the selection of appropriate summary statistics.

Conclusions:

Longitudinal network studies, particularly with optimal wave timing, enhance the accuracy of infectious disease modeling.
Methodological advancements in mechanistic network models and ABC inference are valuable for STI research.
Findings provide guidance for designing future longitudinal studies on STIs, optimizing data collection strategies.