Full Description
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.
Contents
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.
