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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.
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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