Part 1: Big Data and Global Health Landscape
• Chapter 1. Strengths and Weaknesses of Big Data for Global Health Surveillance
• Chapter 2. Opportunities for Health Big Data in Africa
• Chapter 3. HealthMap and Digital Disease Surveillance
• Chapter 4. Mobility Data and Genomics for Disease Surveillance
Part 2: Case Studies
• Chapter 5. Kumbh Mela Disease Surveillance
• Chapter 6. Using Google Mobility Data for Disaster Monitoring in Puerto Rico
• Chapter 7. StreetRx and the Opioid Epidemic
• Chapter 8. Twitter Data for Zika Virus Surveillance in Venezuela
• Chapter 9. Hepatitis E Outbreak in Namibia and Google Trends
• Chapter 10. Patient-Controlled Health Records for Non-Communicable Diseases in Humanitarian Settings
• Chapter 11. Addressing Sexual and Reproductive Health among Youth Migrants
• Chapter 12. Tanzanian cholera: epidemic or endemic?
• Chapter 13. Google Satellite Images to Predict Yellow Fever Incidence in Brazil
• Chapter 14. Feature Selection and Prediction of Treatment Failure in Tuberculosis
• Chapter 15. Tuberculosis, Refugees, and the Politics of Journalistic Objectivity: A qualitative review using HealthMap data
• Chapter 16. Designing Tools to Support the Cutaneous Leishmaniasis Trial in Colombia.
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.
• Is the first and currently the only book on digital disease surveillance through the application of machine learning to non-traditional data sources
• Focuses on combating disease and promoting health, especially in resource-constrained settings
• Includes and expands on the latest non-traditional data sources such as Google Trends, Google Street View, the news media, and social media
• Is an open access book