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Leveraging Data Science for Global Health

Inside this Book

If you make use of this material, you may credit the authors as follows:

Celi Leo Anthony et al. (Editors), "Leveraging Data Science for Global Health", Springer Nature, 2020, DOI: 10.1007/978-3-030-47994-7, License: http://creativecommons.org/licenses/by/4.0/

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.

Keywords

Health Informatics, Health Economics, Open Access, Big Data, Machine Learning, Artificial Intelligence, Digital Disease Surveillance, Health Mapping, Health Records For Non-communicable Diseases, Healthmap, Tools For Clinical Trials, Medical Equipment & Techniques, Information Technology: General Issues, Health & Safety Aspects Of It, Health Economics

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