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Related Experiment Video

Updated: May 24, 2026

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

Inferring pandemic growth rates from sequence data.

Eric de Silva¹, Neil M Ferguson, Christophe Fraser

¹Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London W2 1PG, UK.

Journal of the Royal Society, Interface

|February 17, 2012

Summary

This summary is machine-generated.

This study reveals biases in non-parametric effective population size estimation during epidemic analysis. Parametric methods and specific sampling strategies can improve accuracy for reconstructing epidemic dynamics.

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Last Updated: May 24, 2026

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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

Area of Science:

Epidemiology
Population Genetics
Computational Biology

Background:

Sequence data analysis is crucial for understanding outbreak dynamics.
Coalescent inference methods are common but require rigorous testing with simulated epidemics.

Purpose of the Study:

To evaluate parametric and non-parametric methods for inferring effective population size in epidemic scenarios.
To identify biases in population size estimation and their impact on reconstructing epidemic dynamics.

Main Methods:

Simulated epidemic data, including scenarios for pandemic influenza.
Testing of parametric and non-parametric effective population size estimation methods within the BEAST package.
Analysis of various sampling strategies and their influence on estimation biases.

Main Results:

Non-parametric methods exhibit systematic biases, falsely suggesting epidemic slowdown.
Parametric methods show potential for bias correction with large infected populations.
Poor sampling strategies, like over-representing linked cases, significantly worsen bias.

Conclusions:

Effective population size estimation requires careful method selection and consideration of sampling strategies.
A diagnostic indicator based on coalescent density can identify periods of reliable estimation.
Findings are applicable to various exponentially growing epidemics, including the 2009 H1N1 pandemic.