Constructing a global human epidemic database using open-source digital biosurveillance

We developed a dataset consisting of outbreak data collected from official, open-source surveillance reports representing more than 170 pathogens, 237 countries and territories, and more than 3300 events that occurred primarily between 1963 and 2023. Here we present and analyze a subset of these data, comprising a dataset of human epidemic events with onset between 2015 and 2020. Structuring of epidemiological data in the dataset follows a specific methodology to ensure consistency across all events. This methodology has been designed to produce the most reliable spatiotemporal view of an outbreak as possible. To ensure data are true-to-source, the structured data undergoes multiple rounds of both manual and automated review and validation. The extensive and standardized nature of the dataset makes it well-suited for both descriptive epidemiology and exploring outbreak dynamics and disease emergence.

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Publication Details

Journal
Scientific Data
Published
2025-02-26
DOI
https://doi.org/10.1038/s41597-025-04663-z
Primary Topic
Zoonotic diseases and public health
Type
article
Field-Weighted Citation Impact
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article

Constructing a global human epidemic database using open-source digital biosurveillance

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Scientific Data
Zoonotic diseases and public health
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Constructing a global human epidemic database using open-source digital biosurveillance

Rinette Badker, Ben Oppenheim, Benjamin Ash, Chris Pardee, Nita Madhav, Nicole Stephenson, Patrick D. Savage, Naama Kipperman
article en

Abstract

We developed a dataset consisting of outbreak data collected from official, open-source surveillance reports representing more than 170 pathogens, 237 countries and territories, and more than 3300 events that occurred primarily between 1963 and 2023. Here we present and analyze a subset of these data, comprising a dataset of human epidemic events with onset between 2015 and 2020. Structuring of epidemiological data in the dataset follows a specific methodology to ensure consistency across all events. This methodology has been designed to produce the most reliable spatiotemporal view of an outbreak as possible. To ensure data are true-to-source, the structured data undergoes multiple rounds of both manual and automated review and validation. The extensive and standardized nature of the dataset makes it well-suited for both descriptive epidemiology and exploring outbreak dynamics and disease emergence.

Scientific DataVol. 12(1)
Ginkgo Bioworks, Inc. (United States) (US)
Good health and well-being
Openalex Percentile: Top 95%
Zoonotic diseases and public health
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