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020 _a9783030479947
_9978-3-030-47994-7
024 7 _a10.1007/978-3-030-47994-7
_2doi
050 4 _aR858-859.7
072 7 _aMBG
_2bicssc
072 7 _aUB
_2bicssc
072 7 _aMED117000
_2bisacsh
072 7 _aUXT
_2thema
082 0 4 _a610.285
_223
245 1 0 _aLeveraging Data Science for Global Health
_h[electronic resource] /
_cedited by Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXII, 475 p. 196 illus., 175 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart 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.
506 0 _aOpen Access
520 _aThis 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.
650 0 _aMedical informatics.
650 0 _aMedical economics.
650 1 4 _aHealth Informatics.
650 2 4 _aHealth Economics.
700 1 _aCeli, Leo Anthony.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMajumder, Maimuna S.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aOrdóñez, Patricia.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aOsorio, Juan Sebastian.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aPaik, Kenneth E.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSomai, Melek.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030479930
776 0 8 _iPrinted edition:
_z9783030479954
776 0 8 _iPrinted edition:
_z9783030479961
856 4 0 _uhttps://doi.org/10.1007/978-3-030-47994-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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942 _cSPRINGER
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