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008			220520s2022 sz a o |||| 0|eng d
020			_a9783030952815
024	7		_a10.1007/978-3-030-95281-5 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aRA652 _b2022 EB
245	0	0	_aEpidemic Analytics for Decision Supports in COVID19 Crisis _cedited by Joao Alexandre Lobo Marques, Simon James Fong
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (VI, 158 páginas) _b87 ilustraciones, 77 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aChapter 1. Research and Technology Development Achievements During the COVID-19 Pandemic - An Overview -- Chapter 2. Analysis of the COVID-19 Pandemic Behavior based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models -- Chapter 3. The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak -- Chapter 4. Probabilistic Forecasting Model for the COVID-19 Pandemic based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System -- Chapter 5. The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID-19 Pandemic -- Chapter 6. A Quantum Field formulation for a pandemic propagation.
520			_aCovid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.
988			_aSpringer_Engineering_2022
650		7	_2embne _9138405 _aEpidemiología _xProceso de datos
650		7	_2embne _9683668 _aCOVID-19
700	1		_aMarques, Joao Alexandre Lobo _eeditor literario _0(orcid)0000-0002-6472-8784 _1https://orcid.org/0000-0002-6472-8784 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFong, Simon James _eeditor literario _0(orcid)0000-0002-1848-7246 _1https://orcid.org/0000-0002-1848-7246 _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030952808
776	0	8	_iPrinted edition: _z9783030952822
776	0	8	_iPrinted edition: _z9783030990213
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-95281-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eb _zSI