Electronic Resource

Leveraging Data Science for Global Health

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Availability

Detail Information

Series Title

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Call Number

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Publisher

: Springer International Publishing., 2020

Collation

XII, 475 hlm; ill., lamp.,

Language

English

ISBN/ISSN

9783030479947

Classification

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Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Health Informatics
Health Economics

Specific Detail Info

Is the first and currently the only book on digital disease surveillance through the application of machine learning to non-traditional data sources Focuses on combating disease and promoting health, especially in resource-constrained settings Includes and expands on the latest non-traditional data sources such as Google Trends, Google Street View, the news media, and social media Is an open access book

Statement of Responsibility

Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai

Other Information

Cataloger

-

Source

https://link.springer.com/book/10.1007/978-3-030-47994-7

Validator

ida

Digital Object Identifier (DOI)

https://doi.org/10.1007/978-3-030-47994-7

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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