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Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...

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Related Experiment Video

Updated: Jul 28, 2025

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Published on: February 20, 2021

Epidemiological inference for emerging viruses using segregating sites.

Yeongseon Park¹, Michael A Martin^1,2, Katia Koelle^3,4

¹Graduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, GA, 30322, USA.

Nature Communications

|May 29, 2023

Summary

This summary is machine-generated.

This study introduces a new method using pathogen genetic data to estimate disease spread parameters early in an outbreak. The approach accurately models viral lineage expansion and infectious disease dynamics.

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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

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Last Updated: Jul 28, 2025

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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

Area of Science:

Epidemiology
Genomics
Computational Biology

Background:

Epidemiological models often use case and pathogen sequence data for parameter estimation and inferring disease dynamics.
Early-stage viral lineage expansion presents unique challenges for traditional modeling approaches.

Purpose of the Study:

To develop and validate an inference approach for fitting epidemiological models using pathogen sequence data during the early stages of viral lineage expansion.
To assess the utility of population genetic summary statistics for inferring epidemiological parameters.

Main Methods:

A Sequential Monte Carlo (SMC) framework was employed, utilizing a trajectory of segregating sites from pathogen sequence data.
The approach was tested using simulated data under a single-introduction scenario.
The method was applied to real-world SARS-CoV-2 sequence data from France.

Main Results:

The approach accurately recovered key epidemiological quantities in simulated data.
Inference of a basic reproduction number (R0) between 2.3-2.7 for SARS-CoV-2 in France, allowing for multiple introductions.
Demonstrated the informativeness of population genetic summary statistics for epidemiological inference.

Conclusions:

The presented inference approach is effective for early-stage viral lineage expansion.
Sequence data-based inference can reconstruct infectious disease dynamics.
Population genetic methods offer valuable insights into epidemiological parameters.