Leveraging Data Science for Global Health

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Abstract

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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APA

Celi, L. A., Majumder, M. S., Ordóñez, P., Osorio, J. S., Paik, K. E., & Somai, M. (2020). Leveraging Data Science for Global Health. Leveraging Data Science for Global Health (pp. 1–475). Springer International Publishing. https://doi.org/10.1007/978-3-030-47994-7

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