Amazon cover image
Image from Amazon.com

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

Contributor(s): Material type: TextTextPublisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020Description: XII, 475 p. 196 illus., 175 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030479947
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 610.285 23
LOC classification:
  • R858-859.7
Online resources:
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.
In: Springer Nature eBookSummary: 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.
List(s) this item appears in: e-Book / ebook
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
No physical items for this record

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.

Open Access

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.

There are no comments on this title.

to post a comment.

Universiti Islam Sultan Sharif Ali
Spg 347, Jalan Pasar Gadong, BE1310
Brunei Darussalam

+ 673 2462000 ext 603/604

library@unissa.edu.bn
norhasinah.moksin@unissa.edu.bn
syukriyyah.kahar@unissa.edu.bn

Library Operating Hours:

Gadong Campus School Terms:
Monday – Thursday & Saturday:
8.00 AM – 5.00 PM
Friday, Sunday & Public Holidays :
Closed

Revision & Exam Week:
Monday – Wednesday:
8.00 AM – 9.00 PM
(Unless Otherwise Stated)
Thursday & Saturday:
8.00 AM – 5.00 PM
Friday & Sunday :
8.00 AM – 12.00 PM & 1.30 PM – 5.00 PM
Public Holidays :
Closed

Mid / Inter-Semester Break / Long Vacation:
Monday – Thursday & Saturday:
8.00 AM – 12.15 PM & 1.30 PM – 4.30 PM
Friday, Sunday & Public Holidays :
Closed

Sinaut Campus

School Terms:
Monday – Thursday & Saturday:
8.00 AM – 4.30 PM
Friday, Sunday & Public Holidays :
Closed

Revision & Exam Week:
Monday – Thursday & Saturday:
8.00 AM – 4.30 PM
Friday, Sunday & Public Holidays :
Closed

Mid / Inter-Semester Break / Long Vacation:
Monday – Thursday & Saturday:
8.00 AM – 12.15 PM & 1.30 PM – 4.30 PM
Friday, Sunday &
Public Holidays :
Closed

Flag Counter

© All Right Reserved 2023. Universiti Islam Sultan Sharif Ali

Administered and upheld by
 Rayyan Secutech