UM
Prediction for Decision Support During the COVID-19 Pandemic
Marques,Joao Alexandre Lobo1; Gois,Francisco Nauber Bernardo2; Xavier-Neto,José3; Fong,Simon James4
2021
Source PublicationSpringerBriefs in Applied Sciences and Technology
Pages1-13
AbstractThe 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英语
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Document TypeBook chapter
CollectionUniversity of Macau
Corresponding AuthorMarques,Joao Alexandre Lobo
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
Marques,Joao Alexandre Lobo,Gois,Francisco Nauber Bernardo,Xavier-Neto,José,et al. Prediction for Decision Support During the COVID-19 Pandemic,2021:1-13.
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