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001432758 001__ 1432758
001432758 003__ OCoLC
001432758 005__ 20230309003531.0
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001432758 008__ 201205s2021\\\\sz\\\\\\ob\\\\000\0\eng\d
001432758 019__ $$a1225068806$$a1237490969
001432758 020__ $$a9783030619138$$q(electronic book)
001432758 020__ $$a3030619133$$q(electronic book)
001432758 020__ $$z3030619125
001432758 020__ $$z9783030619121
001432758 0247_ $$a10.1007/978-3-030-61913-8$$2doi
001432758 035__ $$aSP(OCoLC)1225547628
001432758 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dN$T$$dOCLCO$$dYDXIT$$dGW5XE$$dSFB$$dOCLCO$$dOCLCF$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ
001432758 049__ $$aISEA
001432758 050_4 $$aRA644.C67$$bM37 2021
001432758 08204 $$a362.1962/414$$223
001432758 1001_ $$aMarques, Joao Alexandre Lobo,$$eauthor.
001432758 24510 $$aPredictive models for decision support in the COVID-19 crisis /$$cJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James, Fong.
001432758 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001432758 300__ $$a1 online resource (103 pages)
001432758 336__ $$atext$$btxt$$2rdacontent
001432758 337__ $$acomputer$$bc$$2rdamedia
001432758 338__ $$aonline resource$$bcr$$2rdacarrier
001432758 4901_ $$aSpringerBriefs in applied sciences and technology
001432758 504__ $$aIncludes bibliographical references.
001432758 5050_ $$aPrediction for Decision Support During the COVID-19 Pandemic -- Epidemiology Compartmental Models-SIR, SEIR, and SEIR with Intervention -- Forecasting COVID-19 Time Series Based on an Autoregressive Model -- Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering -- Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML -- Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil.
001432758 506__ $$aAccess limited to authorized users.
001432758 520__ $$aCOVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.
001432758 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 16, 2021).
001432758 650_0 $$aCOVID-19 (Disease)$$xEpidemiology.
001432758 650_0 $$aEpidemiology$$xStatistical methods.
001432758 650_0 $$aPredictive analytics.
001432758 650_0 $$aMedical policy$$xDecision making.
001432758 650_0 $$aEconomic policy$$xDecision making.
001432758 650_6 $$aCOVID-19$$xÉpidémiologie.
001432758 650_6 $$aÉpidémiologie$$xMéthodes statistiques.
001432758 650_6 $$aPolitique sanitaire$$xPrise de décision.
001432758 650_6 $$aPolitique économique$$xPrise de décision.
001432758 655_0 $$aElectronic books.
001432758 7001_ $$aGois, Francisco Nauber Bernardo,$$eauthor.
001432758 7001_ $$aXavier-Neto, José,$$eauthor.
001432758 7001_ $$aFong, Simon,$$eauthor.
001432758 77608 $$iPrint version:$$z9783030619121
001432758 830_0 $$aSpringerBriefs in applied sciences and technology.
001432758 852__ $$bebk
001432758 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-61913-8$$zOnline Access$$91397441.1
001432758 909CO $$ooai:library.usi.edu:1432758$$pGLOBAL_SET
001432758 980__ $$aBIB
001432758 980__ $$aEBOOK
001432758 982__ $$aEbook
001432758 983__ $$aOnline
001432758 994__ $$a92$$bISE