1.

Record Nr.

UNINA9910413437203321

Autore

Celi Leo Anthony

Titolo

Leveraging Data Science for Global Health / / edited by Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai

Pubbl/distr/stampa

Springer Nature, 2020

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020

ISBN

3-030-47994-3

Edizione

[1st ed. 2020.]

Descrizione fisica

1 online resource (XII, 475 p. 196 illus., 175 illus. in color.)

Disciplina

502.85

Soggetti

Health informatics

Health economics

Health Informatics

Health Economics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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.

Sommario/riassunto

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.