Leveraging Data Science for Global Health

Citations of this article
Mendeley users who have this article in their library.

This article is free to access.


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.




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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free