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Predictive Models for Decision Support in the COVID-19 Crisis

  • Book
  • © 2021

Overview

Authors:
  1. Joao Alexandre Lobo Marques

    1. Laboratory of Neuroapplications, University of Saint Joseph, Macau, Macao

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  2. Francisco Nauber Bernardo Gois

    1. Machine Learning Department, Secretary of Health of the Government of the State of Ceara, Fortaleza, Brazil

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    You can also search for this author in PubMed   Google Scholar

  3. José Xavier-Neto

    1. Government Intelligence Cell, Secretary of Health of the Government of the State of Ceara, Fortaleza, Brazil

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  4. Simon James Fong

    1. Department of Computer and Information Science, University of Macau, Macau, Macao

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  • Showcases how artificial intelligence has been used to support the fight against COVID-19
  • Presents the benefits and limitations of the predictive models implemented during the epidemic
  • Advises on how these tools may be better used in the future

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-vii
    Download chapter PDF

  2. Prediction for Decision Support During the COVID-19 Pandemic

    • Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
    Pages 1-13

  3. Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention

    • Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
    Pages 15-39

  4. Forecasting COVID-19 Time Series Based on an Autoregressive Model

    • Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
    Pages 41-54

  5. Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering

    • Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
    Pages 55-68

  6. Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML

    • Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
    Pages 69-87

  7. Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil

    • Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
    Pages 89-98
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Keywords

About this book

COVID-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.

Reviews

“This book is … of great interest for mathematical modelers--it nicely summarizes many important tools, with concrete examples, that could be adapted for other situations. … I strongly recommend this book to advanced undergraduate engineers and mathematicians as well as specialists dealing with dynamical system modeling.” (Arturo Ortiz-Tapia, Computing Reviews, July 26, 2022)

Authors and Affiliations

  • Laboratory of Neuroapplications, University of Saint Joseph, Macau, Macao

    Joao Alexandre Lobo Marques

  • Machine Learning Department, Secretary of Health of the Government of the State of Ceara, Fortaleza, Brazil

    Francisco Nauber Bernardo Gois

  • Government Intelligence Cell, Secretary of Health of the Government of the State of Ceara, Fortaleza, Brazil

    José Xavier-Neto

  • Department of Computer and Information Science, University of Macau, Macau, Macao

    Simon James Fong

About the authors

Professor João Alexandre Lobo Marques gained his master’s degree in 2007 and his PhD in 2009, both from the Federal University of Ceará, UFC, Brasil. He works as an associate professor at the University of Saint Joseph, Macau, and as a visiting associate professor at the Chinese Academy of Sciences. He is the CEO and co-founder of the XS Innovation Group in Brazil, which is focused on bioengineering innovation for education. He has published over 60 journal and conference papers, and has co-authored three books. His research interests include computational and artificial intelligence, data sciences, and neuroeconomics.

Professor Francisco Nauber Bernardo Gois is an adjunct professor at the Federal University of Ceará. He holds a master’s degree and a PhD from the University of Fortaleza, gained in 2010 and 2017 respectively. He has experience in computer science, with emphasis on machine learning and software testing, deep learning, continuous integration, testing and extreme programming.

Dr José Xavier Neto holds a medical degree from Federal University of Ceará and a PhD from the University of São Paulo, which he gained in 1989 and 1993 respectively. He has worked in medical research for decades, including his current role as the Chief Health Scientist of Ceará and a Visiting Professor at the Federal University of Ceará. He has been involved in creating an experimental model for developmental neuropathy induced by the Zika virus, as well as leading a multidisciplinary team which described the first fossilised heart.

Professor Simon James Fong gained his master’s degree and PhD from La Trobe University in 1994 and 1998 respectively. He has worked in several academic positions, including his current role as Associate Professor at the University of Macau. He has been on the committee for several conferences, including acting as chair, and has worked as a book series editor. His research interests include data mining, artificial intelligence, machine learning, and biomedical applications.

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