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Hemorrhagic Fever, Ebola --- Hemorrhagic Fever, Ebola --- Communicable Disease Control. --- Disease Outbreaks. --- Public Health. --- Ebolavirus. --- Ebola virus disease --- Maladie à virus Ebola --- prevention & control. --- epidemiology.

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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.

Health informatics. --- Health economics. --- Health Informatics. --- Health Economics. --- Economics, Medical --- Health --- Health economics --- Hygiene --- Medical care --- Medicine --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Economic aspects --- Data processing --- 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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Hygiene. Public health. Protection --- Computer. Automation --- gezondheidseconomie --- medische informatica