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Prediction for Decision Support During the COVID-19 Pandemic
Joao Alexandre Lobo Marques1; Francisco Nauber Bernardo Gois2; José Xavier-Neto3; Simon James Fong4
2021
Source PublicationPredictive Models for Decision Support in the COVID-19 Crisis
Author of SourceJoao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
Publication PlaceCham
PublisherSpringer
Pages1-13
Abstract

The task known as prediction is widely applied in several different areas of knowledge, from popular applications such as weather forecasting, going through supply chain management, an increasing range of adoption in healthcare and, more specifically in epidemiology, the central topic of this book. The new challenges brought with the COVID-19 pandemic highlighted the possibilities and necessity of using prediction techniques to support decisions related to epidemiology in both managerial and clinical areas. In practice, the current outbreak created a strong need for the adoption of different computational models to support both medical teams and public health administrators. The methods vary from simple linear regressions to very complex algorithms based on Artificial Intelligence (AI) techniques. The present chapter contextualizes the use of prediction for decision support as a foundation of the following chapters which are focused on the application for the COVID-19 pandemic time series. With such a large number of methods for data-driven predictions, a clear distinction between explanation and prediction is firstly provided. From there, a methodological framework is presented, from the data source definition and selection of countries as references for the analysis, going through data handling for validation, until the definition of the evaluation criteria for the proposed models.

DOI10.1007/978-3-030-61913-8_1
URLView the original
Language英語English
ISBN978-3-030-61913-8
Scopus ID2-s2.0-85097192187
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Citation statistics
Document TypeBook chapter
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorJoao Alexandre Lobo Marques
Affiliation1.Laboratory of Neuroapplications,University of Saint Joseph,Macao
2.Machine Learning Department,Secretary of Health of the Government of the State of Ceara,Fortaleza,Brazil
3.Government Intelligence Cell,Secretary of Health of the Government of the State of Ceara,Fortaleza,Brazil
4.Department of Computer and Information Science,University of Macau,Macao
Recommended Citation
GB/T 7714
Joao Alexandre Lobo Marques,Francisco Nauber Bernardo Gois,José Xavier-Neto,et al. Prediction for Decision Support During the COVID-19 Pandemic. Cham:Springer,2021:1-13.
APA Joao Alexandre Lobo Marques,Francisco Nauber Bernardo Gois,José Xavier-Neto,&Simon James Fong.(2021).Prediction for Decision Support During the COVID-19 Pandemic.Predictive Models for Decision Support in the COVID-19 Crisis,1-13.
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