05400nam 22008415 450 99646544690331620211005214708.03-030-47994-310.1007/978-3-030-47994-7(CKB)4100000011363758(DE-He213)978-3-030-47994-7(MiAaPQ)EBC6420171(Au-PeEL)EBL6420171(OCoLC)1182515218(MiAaPQ)EBC6978213(Au-PeEL)EBL6978213(oapen)https://directory.doabooks.org/handle/20.500.12854/30895(PPN)260302767(EXLCZ)99410000001136375820200731d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierLeveraging Data Science for Global Health[electronic resource] /edited by Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordóñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai1st ed. 2020.Springer Nature2020Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (XII, 475 p. 196 illus., 175 illus. in color.) 3-030-47993-5 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.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.Health informaticsHealth economicsHealth Informaticshttps://scigraph.springernature.com/ontologies/product-market-codes/H28009Health Informaticshttps://scigraph.springernature.com/ontologies/product-market-codes/I23060Health Economicshttps://scigraph.springernature.com/ontologies/product-market-codes/W35000Health InformaticsHealth EconomicsOpen AccessBig DataMachine LearningArtificial IntelligenceDigital Disease SurveillanceHealth MappingHealth Records for Non-Communicable DiseasesHealthMapTools for Clinical TrialsMedical equipment & techniquesInformation technology: general issuesHealth & safety aspects of ITHealth economicsHealth informatics.Health economics.Health Informatics.Health Informatics.Health Economics.502.85Celi Leo Anthonyedt1354887Celi Leo Anthonyedthttp://id.loc.gov/vocabulary/relators/edtMajumder Maimuna Sedthttp://id.loc.gov/vocabulary/relators/edtOrdóñez Patriciaedthttp://id.loc.gov/vocabulary/relators/edtOsorio Juan Sebastianedthttp://id.loc.gov/vocabulary/relators/edtPaik Kenneth Eedthttp://id.loc.gov/vocabulary/relators/edtSomai Melekedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996465446903316Leveraging Data Science for Global Health3358590UNISA05539nam 2200721Ia 450 991101881550332120251116163824.09786612164996978128216499412821649969780470611098047061109X9780470393673047039367X(CKB)2550000000005906(EBL)477695(SSID)ssj0000335966(PQKBManifestationID)11241254(PQKBTitleCode)TC0000335966(PQKBWorkID)10277663(PQKB)10758379(MiAaPQ)EBC477695(OCoLC)520990431(Perlego)2754974(EXLCZ)99255000000000590620070614d2008 uy 0engur|n|---|||||txtccrCombinatorial optimization and theoretical computer science interfaces and perspectives : 30th anniversary of the LAMSADE /edited by Vangelis Th. PaschosLondon ISTE ;Hoboken, NJ Wiley20081 online resource (518 p.)ISTE ;v.24Description based upon print version of record.9781848210219 1848210213 Includes bibliographical references and index.Combinatorial Optimization and Theoretical Computer Science; Contents; Preface; Chapter 1. The Complexity of Single Machine Scheduling Problems under Scenario-based Uncertainty; 1.1. Introduction; 1.2. Problem MinMax(1|prec|fmax, θ ); 1.2.1. Uncertainty on due dates; 1.2.2. Uncertainty on processing times and due dates; 1.3. Problem MinMax(1|| Σ wj Cj, Wj ); 1.4. Problem MinMax(1|| Σ Uj, θ ); 1.4.1. Uncertainty on due dates; 1.4.2. Uncertainty on processing times; 1.5. Bibliography; Chapter 2. Approximation of Multi-criteria Min and Max TSP(1, 2); 2.1. Introduction2.1.1. The traveling salesman problem2.1.2. Multi-criteria optimization; 2.1.3. Organization of the chapter; 2.2. Overview; 2.3. The bicriteria TSP(1, 2); 2.3.1. Simple examples of the non-approximability; 2.3.2. A local search heuristic for the bicriteria TSP(1, 2); 2.3.3. A nearest neighbor heuristic for the bicriteria TSP(1, 2); 2.3.4. On the bicriteria Max TSP(1, 2); 2.4. k-criteria TSP(1, 2); 2.4.1. Non-approximability related to the number of generated solutions; 2.4.2. A nearest neighbor heuristic for the k-criteria TSP(1, 2); 2.5. Conclusion; 2.6. BibliographyChapter 3. Online Models for Set-covering: The Flaw of Greediness3.1. Introduction; 3.2. Description of the main results and related work; 3.3. The price of ignorance; 3.4. Competitiveness of TAKE-ALL and TAKE-AT-RANDOM; 3.4.1. TAKE-ALL algorithm; 3.4.2. TAKE-AT-RANDOM algorithm; 3.5. The nasty flaw of greediness; 3.6. The power of look-ahead; 3.7. The maximum budget saving problem; 3.8. Discussion; 3.9. Bibliography; Chapter 4. Comparison of Expressiveness for Timed Automata and Time Petri Nets; 4.1. Introduction; 4.2. Time Petri nets and timed automata4.2.1. Timed transition systems and equivalence relations4.2.2. Time Petri nets; 4.2.3. Timed automata; 4.2.4. Expressiveness and equivalence problems; 4.3. Comparison of semantics I, A and PA; 4.3.1. A first comparison between the different semantics of TPNs; 4.3.2. A second comparison for standard bounded TPN; 4.4. Strict ordering results; 4.5. Equivalence with respect to timed language acceptance; 4.5.1. Encoding atomic constraints; 4.5.2. Resetting clocks; 4.5.3. The complete construction; 4.5.4. Δ (A) and A accept the same timed language; 4.5.5. Consequences of the previous results4.6. Bisimulation of TA by TPNs4.6.1. Regions of a timed automaton; 4.6.2. From bisimulation to uniform bisimulation; 4.6.3. A characterization of bisimilarity; 4.6.4. Proof of necessity; 4.6.5. First construction; 4.6.6. Second construction; 4.6.7. Complexity results; 4.7. Conclusion; 4.8. Bibliography; Chapter 5. A "Maximum Node Clustering" Problem; 5.1. Introduction; 5.2. Approximation algorithm for the general problem; 5.3. The tree case; 5.3.1. Dynamic programming; 5.3.2. A fully polynomial time approximation scheme; 5.4. Exponential algorithms for special cases; 5.5. BibliographyChapter 6. The Patrolling Problem: Theoretical and Experimental ResultsThis volume is dedicated to the theme "Combinatorial Optimization - Theoretical Computer Science: Interfaces and Perspectives" and has two main objectives: the first is to show that bringing together operational research and theoretical computer science can yield useful results for a range of applications, while the second is to demonstrate the quality and range of research conducted by the LAMSADE in these areas.ISTECombinatorial optimizationComputer programsComputer scienceMathematicsCombinatorial optimizationComputer programs.Computer scienceMathematics.519.6/4SK 890rvkST 130rvkPaschos Vangelis Th944252Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (France)MiAaPQMiAaPQMiAaPQBOOK9911018815503321Combinatorial optimization and theoretical computer science4422757UNINA