Leveraging Data Science for Global Health
Springer Nature; Springer, 2020
Online
E-Book
- 475
Zugriff:
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.
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Leveraging Data Science for Global Health
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Autor/in / Beteiligte Person: | Celi, Leo Anthony ; Majumder, Maimuna S. ; Ordóñez, Patricia ; Osorio, Juan Sebastian ; Paik, Kenneth E. ; Somai, Melek |
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Veröffentlichung: | Springer Nature; Springer, 2020 |
Medientyp: | E-Book |
Umfang: | 475 |
ISBN: | 978-3-030-47994-7 (print) ; 3-030-47994-3 (print) |
DOI: | 10.1007/978-3-030-47994-7 |
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