Leveraging Data Science for Global Health

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

Autorentext

Leo Anthony Celi, M.D., M.S., M.P.H., has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP) and as an attending physician at the Beth Israel Deaconess Medical Center (BIDMC), he brings together clinicians and data scientists to support research using data routinely collected in the process of care. Leo also founded and co-directs Sana, a cross-disciplinary organization based at the Institute for Medical Engineering and Science at MIT, whose objective is to leverage information technology to improve health outcomes in low- and middle-income countries. He is one of the course directors for global health informatics to improve quality of care, and collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. Leo has spoken in 25 countries about the value of data in improving health outcomes.

Zusammenfassung
"This book seems to empower the reader to gradually embark on the development of medical applications incorporating data science. ... This book is well structured, written with a good level of linguistic guts, and could be recommended to data science students rather than researchers or health professionals." (Thierry Edoh, Computing Reviews, March 24, 2022)

Inhalt
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.