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Updated: Jun 23, 2026

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

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

Published on: December 27, 2010

Estimating HIV incidence based on combined prevalence testing.

Raji Balasubramanian¹, Stephen W Lagakos

¹Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, 715 North Pleasant Street, Amherst, Massachusetts 01003, USA. rbalasub@schoolph.umass.edu

|April 29, 2009

Summary

This summary is machine-generated.

Estimating human immunodeficiency virus (HIV) incidence is crucial for epidemic control. This study introduces a novel longitudinal approach, offering a more robust method than single-point estimations for designing effective HIV prevention strategies.

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

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

Published on: December 27, 2010

Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses

Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses

Published on: May 4, 2015

Area of Science:

Epidemiology
Biostatistics
Infectious Disease Modeling

Background:

Accurate estimation of disease incidence, particularly for human immunodeficiency virus (HIV), is vital for assessing epidemic status and informing intervention strategies.
Longitudinal studies for disease incidence estimation are often resource-intensive.
Previous methods, like the Janssen estimator, offer single-point incidence estimation using antibody assays.

Purpose of the Study:

To frame HIV incidence estimation from a longitudinal perspective.
To derive and compare a maximum likelihood estimator (MLE) with the existing Janssen estimator.
To develop a flexible framework for incidence estimation applicable to diverse study designs, including varying test batteries and covariate inclusion.

Main Methods:

The study formulates HIV incidence estimation within a longitudinal framework.
It derives the maximum likelihood estimator (MLE) for incidence.
The proposed methods are validated using data from an HIV intervention trial and a Botswana seroprevalence survey.

Main Results:

The article presents a longitudinal maximum likelihood estimator for HIV incidence.
This estimator is compared against the established Janssen estimator.
The framework accommodates complex scenarios, including multiple testing strategies and covariates, enhancing its applicability.

Conclusions:

The developed longitudinal framework provides a statistically robust method for estimating HIV incidence.
This approach offers advantages over single-point estimations, particularly in guiding the design of public health interventions.
The flexibility of the model supports comparative evaluations of different diagnostic test strategies for improved study design.