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.
