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  • Textbook
  • Open Access
  • © 2020

Leveraging Data Science for Global Health

Editors:

(view affiliations)
  • 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

Buying options

Softcover Book
USD 49.99
Price excludes VAT (USA)
Hardcover Book
USD 59.99
Price excludes VAT (USA)

Table of contents (29 chapters)

  1. Data for Global Health Projects

    1. Front Matter

      Pages 305-305
    2. Establishing a Regional Digital Health Interoperability Lab in the Asia-Pacific Region: Experiences and Recommendations

      • Philip Christian C. Zuniga, Susann Roth, Alvin B. Marcelo
      Pages 315-327Open Access
    3. Mbarara University of Science and Technology (MUST)

      • Richard Kimera, Fred Kaggwa, Rogers Mwavu, Robert Mugonza, Wilson Tumuhimbise, Gloria Munguci et al.
      Pages 329-350Open Access
    4. Data Integration for Urban Health

      • Yuan Lai, David J. Stone
      Pages 351-363Open Access
    5. Ethics in Health Data Science

      • Yvonne MacPherson, Kathy Pham
      Pages 365-372Open Access
  2. Case Studies

    1. Front Matter

      Pages 383-383
    2. A Data-Driven Approach for Addressing Sexual and Reproductive Health Needs Among Youth Migrants

      • Pragati Jaiswal, Amber Nigam, Teertha Arora, Uma Girkar, Leo Anthony Celi, Kenneth E. Paik
      Pages 397-416Open Access
    3. Yellow Fever in Brazil: Using Novel Data Sources to Produce Localized Policy Recommendations

      • Shalen De Silva, Ramya Pinnamaneni, Kavya Ravichandran, Alaa Fadaq, Yun Mei, Vincent Sin
      Pages 417-428Open Access
    4. Sana.PCHR: Patient-Controlled Electronic Health Records for Refugees

      • Patrick McSharry, Andre Prawira Putra, Rachel Shin, Olivia Mae Waring, Maiamuna S. Majumder, Ned McCague et al.
      Pages 429-441Open Access
    5. Using Non-traditional Data Sources for Near Real-Time Estimation of Transmission Dynamics in the Hepatitis-E Outbreak in Namibia, 2017–2018

      • Michael Morley, Maiamuna S. Majumder, Tony Gallanis, Joseph Wilson
      Pages 443-452Open Access
    6. Building a Data Science Program Through Hackathons and Informal Training in Puerto Rico

      • Patricia Ordóñez Franco, María Eglée Pérez Hernández, Humberto Ortiz-Zuazaga, José García Arrarás
      Pages 453-467Open Access
  3. Back Matter

    Pages 469-475

About this book

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.


Keywords

  • Open Access
  • Big Data
  • Machine Learning
  • Artificial Intelligence
  • Health Informatics
  • Digital Disease Surveillance
  • Health Mapping
  • Health Records for Non-Communicable Diseases
  • HealthMap
  • Tools for Clinical Trials

Reviews

“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)

Editors and Affiliations

  • Massachusetts Institute of Technology, Cambridge, USA

    Leo Anthony Celi

  • Boston Children’s Hospital, Harvard Medical School, Boston, USA

    Maimuna S. Majumder

  • University of Puerto Rico Río Piedras, San Juan, USA

    Patricia Ordóñez

  • ScienteLab, Department of Global Health, University of Washington, Seattle, USA

    Juan Sebastian Osorio

  • Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, USA

    Kenneth E. Paik

  • Imperial College London, London, UK

    Melek Somai

About the editors

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. 

Bibliographic Information

  • Book Title: Leveraging Data Science for Global Health

  • Editors: Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai

  • DOI: https://doi.org/10.1007/978-3-030-47994-7

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2020

  • License: CC BY

  • Hardcover ISBN: 978-3-030-47993-0

  • Softcover ISBN: 978-3-030-47996-1

  • eBook ISBN: 978-3-030-47994-7

  • Edition Number: 1

  • Number of Pages: XII, 475

  • Number of Illustrations: 21 b/w illustrations, 175 illustrations in colour

  • Topics: Health Informatics, Health Economics

Buying options

Softcover Book
USD 49.99
Price excludes VAT (USA)
Hardcover Book
USD 59.99
Price excludes VAT (USA)