Predictive Models for Decision Support in the COVID-19 Crisis

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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.

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


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.


"The book is written for students, researchers, and other stakeholders in the COVID-19 pandemic with an interest in epidemiology and public health decision-making. ... The authors wrote this book to provide a comprehensive reference on predictive modeling and decision support during the unfolding COVID-19 Crisis. The timeliness of such a reference is valuable for public health stakeholders." (Douglas David Gunzler, Doody's Book Reviews, August 20, 2021)

Chapter 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. Articial 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.

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Predictive Models for Decision Support in the COVID-19 Crisis
Kartonierter Einband
Springer International Publishing
Allgemeines & Lexika
Anzahl Seiten
H235mm x B155mm x T6mm
1st ed. 2021
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