A global dataset of pandemic- and epidemic-prone disease outbreaks

This paper presents a new dataset of infectious disease outbreaks collected from the Disease Outbreak News and the Coronavirus Dashboard produced by the World Health Organization. The dataset contains information on 70 infectious diseases and 2227 public health events that occurred over the period f...

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Bibliographic Details
Published in:Scientific data 2022-11, Vol.9 (1), p.683-12, Article 683
Main Authors: Torres Munguía, Juan Armando, Badarau, Florina Cristina, Díaz Pavez, Luis Rodrigo, Martínez-Zarzoso, Inmaculada, Wacker, Konstantin M.
Format: Article
Language:English
Subjects:
692/699/255
706/648/697/129
Communicable Diseases
Coronavirus Infections - epidemiology
Coronaviruses
Data Descriptor
Data mining
Datasets
Disease hot spots
Disease Outbreaks
Economics and Finance
Epidemics
Globalization
Humanities and Social Sciences
Humans
Infectious diseases
multidisciplinary
Outbreaks
Pandemics
Public health
Science
Science (multidisciplinary)
Socioeconomic factors
Socioeconomics
Spatial analysis
Spatial distribution
World Health Organization
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Summary:This paper presents a new dataset of infectious disease outbreaks collected from the Disease Outbreak News and the Coronavirus Dashboard produced by the World Health Organization. The dataset contains information on 70 infectious diseases and 2227 public health events that occurred over the period from January 1996 to March 2022 in 233 countries and territories around the world. We illustrate the potential use of this dataset to the research community by analysing the spatial distribution of disease outbreaks. We find evidence of spatial clusters of high incidences (“hot spots”) in Africa, America, and Asia. This spatial analysis enables policymakers to identify the regions with the greatest likelihood of suffering from disease outbreaks and, taking into account their degree of preparedness and vulnerability, to develop policies that may help contain the spreading of future outbreaks. Further applications could focus on combining our data with other information sources to study, for instance, the link between environmental, globalization, and/or socioeconomic factors with disease outbreaks. Measurement(s) Pandemic- and epidemic-prone disease outbreaks Technology Type(s) Text mining using R Sample Characteristic - Organism Disease outbreaks Sample Characteristic - Environment spatiotemporal region Sample Characteristic - Location Global
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-022-01797-2