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Related Experiment Video

Updated: Oct 25, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

A generalizable data assembly algorithm for infectious disease outbreaks.

Maimuna S Majumder^1,2, Sherri Rose³

¹Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA.

|August 5, 2021

Summary

This summary is machine-generated.

This study presents an automated algorithm to convert text-based infectious disease outbreak data into a machine-readable format. The tool accurately curates information from various sources, aiding outbreak researchers.

Keywords:

automation data curation infectious diseases outbreaks regular expressions

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Last Updated: Oct 25, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

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Published on: June 28, 2013

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

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Published on: July 26, 2019

Area of Science:

Epidemiology
Public Health Informatics
Computational Biology

Background:

Health agencies disseminate crucial outbreak data as text, posing accessibility challenges for researchers.
Lack of machine-readable data hinders timely analysis and response during infectious disease emergencies.

Purpose of the Study:

To develop and validate a generalizable algorithm for automatically curating text-based outbreak information.
To transform unstructured outbreak data into a machine-readable format for enhanced research accessibility.

Main Methods:

Developed a data assembly algorithm utilizing regular expressions.
Curated data from formal reports, email newsletters, and Twitter across three distinct outbreaks.
Created manual validation datasets for performance comparison.

Main Results:

The algorithm demonstrated high accuracy, with cumulative missingness and misidentification rates ≤2% and ≤1%, respectively.
Successfully curated data across diverse sources and three different infectious disease outbreaks.
Validated performance against manually curated datasets.

Conclusions:

The developed algorithm effectively transforms text-based outbreak information into machine-readable data.
Addresses a critical need for generalizable tools in outbreak research.
Facilitates improved data analysis and response for future infectious disease events.