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020 _a9783030619138
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024 7 _a10.1007/978-3-030-61913-8
_2doi
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082 0 4 _a658.5
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100 1 _aMarques, Joao Alexandre Lobo.
_eauthor.
_0(orcid)0000-0002-6472-8784
_1https://orcid.org/0000-0002-6472-8784
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951277
245 1 0 _aPredictive Models for Decision Support in the COVID-19 Crisis
_h[electronic resource] /
_cby Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aVII, 98 p. 48 illus., 41 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
505 0 _aChapter 1. Prediction for Decision Support during the COVID-19 Pandemic -- Chapter 2. Epidemiology Compartmental Models - SIR, SEIR and SEIR with Intervention -- Chapter 3. Forecasting COVID-19 Time Series based on an Auto Regressive Model -- Chapter 4. Nonlinear Prediction for the COVID-19 Data based on Quadratic Kalman Filtering -- Chapter 5. Artificial Intelligence Prediction for the COVID-19 Data based on LSTM Neural Networks and H2O AutoML -- Chapter 6. Predicting the Geographic Spread of the COVID-19 Pandemic: a case study from Brazil.
520 _aCOVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.
650 0 _aIndustrial Management.
_95847
650 0 _aEpidemiology.
_942870
650 0 _aOperations research.
_912218
650 0 _aData mining.
_93907
650 0 _aMedicine, Preventive.
_912690
650 0 _aHealth promotion.
_912689
650 1 4 _aIndustrial Management.
_95847
650 2 4 _aEpidemiology.
_942870
650 2 4 _aOperations Research and Decision Theory.
_931599
650 2 4 _aData Mining and Knowledge Discovery.
_951278
650 2 4 _aHealth Promotion and Disease Prevention.
_936799
700 1 _aGois, Francisco Nauber Bernardo.
_eauthor.
_0(orcid)0000-0001-9361-8659
_1https://orcid.org/0000-0001-9361-8659
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951279
700 1 _aXavier-Neto, José.
_eauthor.
_0(orcid)0000-0003-4648-789X
_1https://orcid.org/0000-0003-4648-789X
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951280
700 1 _aFong, Simon James.
_eauthor.
_0(orcid)0000-0002-1848-7246
_1https://orcid.org/0000-0002-1848-7246
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951281
710 2 _aSpringerLink (Online service)
_951282
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030619121
776 0 8 _iPrinted edition:
_z9783030619145
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
_951283
856 4 0 _uhttps://doi.org/10.1007/978-3-030-61913-8
912 _aZDB-2-ENG
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