Mathematical Modeling, Simulations, and AI for Emergent Pandemic Diseases
Lessons Learned From COVID-19
- 1st Edition - March 21, 2023
- Imprint: Academic Press
- Editors: Edgar N. Sanchez, Esteban A. Hernandez-Vargas, Jorge X. Velasco-Hernandez
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 0 6 4 - 0
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 0 6 5 - 7
Mathematical Modeling, Simulations, and Artificial Intelligence for Emergent Pandemic Diseases: Lessons Learned from COVID-19 includes new research, models and simulatio… Read more
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Request a sales quoteMathematical Modeling, Simulations, and Artificial Intelligence for Emergent Pandemic Diseases: Lessons Learned from COVID-19 includes new research, models and simulations developed during the COVID-19 pandemic into how mathematical methods and practice can impact future response. Chapters go beyond forecasting COVID-19, bringing different scale angles and mathematical techniques (e.g., ordinary differential and difference equations, agent-based models, artificial intelligence, and complex networks) which could have potential use in modeling other emergent pandemic diseases. A major part of the book focuses on preparing the scientific community for the next pandemic, particularly the application of mathematical modeling in ecology, economics and epidemiology.
Readers will benefit from learning how to apply advanced mathematical modeling to a variety of topics of practical interest, including optimal allocations of masks and vaccines but also more theoretical problems such as the evolution of viral variants.
- Provides a comprehensive overview of the state-of-the-art in mathematical modeling and computational simulations for emerging pandemics
- Presents modeling techniques that go beyond COVID-19, and that can be applied to tailoring interventions to attenuate high death tolls
- Includes illustrations, tables and dialog boxes to explain highly specialized concepts and insights with complex algorithms, along with links to programming code
Researchers, developers, and industry professionals in Mathematical Modeling in Life Sciences, Mathematical Biology, Infection Biology, Mathematical Epidemiology, Computer Science, Applied Informatics, Bioinformatics, and Computational Biology and/or Control Systems
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Acknowledgments
- 1: Modeling during an unprecedented pandemic
- Abstract
- 1: Modeling epidemics
- 2: Book overview
- References
- 2: Global epidemiology and impact of the SARS-CoV-2 pandemic
- Abstract
- 1: Introduction
- 2: Global epidemiology
- 3: Epidemiological parameters of SARS-COV-2
- 4: Mitigation strategies
- 5: Reinfections
- 6: SARS-COV-2 variants
- 7: Lessons learned from COVID-19
- Appendix
- References
- 3: Analysis of an ongoing epidemic: Advantages and limitations of COVID-19 modeling
- Abstract
- Acknowledgments
- 1: Introduction
- 2: The beginning of the pandemic
- 3: Implementation and relaxation of nonpharmaceutical interventions
- 4: Estimating the total number of COVID-19-infected people
- 5: Vaccination
- 6: Lessons learned from COVID-19
- Competing interests
- References
- 4: On spatial heterogeneity of COVID-19 using shape analysis of pandemic curves☆
- Abstract
- 1: Introduction
- 2: Methodology
- 3: Experimental results
- 4: Lessons learned from COVID-19
- References
- 5: Pandemic response: Isolationism or solidarity?: An evolutionary perspective
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Background
- 3: Methods and results
- 4: Lessons learned from COVID-19
- Appendix A. Supplementary data
- References
- 6: Optimizing contact tracing: Leveraging contact network structure
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Methods
- 3: Results
- 4: Lessons learned from COVID-19
- References
- 7: Applications of deep learning in forecasting COVID-19 pandemic and county-level risk warning
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Applications of DL in predicting COVID-19 pandemic
- 3: Spatial-temporal analysis
- 4: Epidemiological model-driven DL
- 5: Lessons learned from COVID-19
- References
- 8: COVID-19 population dynamics neural control from a complex network perspective
- Abstract
- 1: Introduction
- 2: MSEIR model
- 3: Inverse optimal impulsive control
- 4: Results
- 5: Lessons learned from COVID-19
- References
- Further reading
- 9: An agent-based model for COVID-19 and its interventions and impact in different social phenomena
- Abstract
- 1: Introduction
- 2: Nonpharmaceutical interventions assessment
- 3: An ABM to evaluate NPI to support postpandemic work activities and the well-being of workers
- 4: Sensitivity analysis
- 5: Assumptions and scenario
- 6: Computational analysis
- 7: Lessons learned from COVID-19
- References
- 10: Implementation of mitigation measures and modeling of in-hospital dynamics depending on the COVID-19 infection status
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Dynamic model of quarantine scenarios and hospital overload
- 3: Stochastic extension for in-hospital dynamics
- 4: Conclusions
- Code availability
- Appendix
- References
- 11: A mathematical model for the reopening of schools in Mexico
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Mathematical model for reopening schools
- 3: Reopening schools in the city of Queretaro
- 4: Epidemic scenarios
- 5: Risk level of being in contact with an infected individual at school
- 6: Lessons learned in COVID-19
- References
- 12: Mathematical assessment of the role of vaccination against COVID-19 in the United States
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Basic vaccination model
- 3: Basic vaccination model with waning immunity
- 4: Modeling dynamics and impact of SARS-CoV-2 variants
- 5: Lessons learned from COVID-19
- References
- 13: Ascertainment and biased testing rates in surveillance of emerging infectious diseases
- Abstract
- Acknowledgment
- 1: Biases in the epidemiological analysis of emerging infectious diseases
- 2: Temporal variation of the confirmed case fatality rate reflects the societal response to outbreaks
- 3: Lessons learned from COVID-19
- References
- 14: Dynamical study of SARS-CoV-2 mathematical models under antiviral treatments
- Abstract
- 1: Introduction
- 2: Review of the target-cell-limited model for SARS-CoV-2 infection
- 3: Antiviral treatment effectiveness
- 4: Inclusion of the PK of antiviral treatment
- 5: Control strategy to tailor therapies
- 6: Conclusions and future works
- Appendix A: Stability theory
- Appendix B: Behavior of the terminal healthy cells count
- References
- 15: Statistical modeling to understand the COVID-19 pandemic
- Abstract
- Acknowledgment
- 1: Introduction
- 2: Epidemic curves via censorship
- 3: COVID-19 footprint and mortality
- 4: Lessons learned from COVID-19
- References
- 16: After COVID-19: Mathematical models, epidemic preparedness, and external factors in epidemic management
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Data
- 3: Confronting the challenge
- 4: Vaccination
- 5: Some features of the epidemic in Mexico
- 6: On public trust in science
- 7: Lessons learned in COVID-19
- References
- Index
- Edition: 1
- Published: March 21, 2023
- Imprint: Academic Press
- No. of pages: 348
- Language: English
- Paperback ISBN: 9780323950640
- eBook ISBN: 9780323950657
ES
Edgar N. Sanchez
Edgar N. Sanchez was born in 1949, in Sardinata, Colombia, South America. He obtained his BSEE major in Power Systems from Universidad Industrial de Santander (UIS, Bucaramanga, Colombia) in 1971, his MSEE from CINVESTAV-IPN (Advanced Studies and Research Center of the National Polytechnic Institute), his major in Automatic Control (Mexico City, Mexico) in 1974, and his Docteur Ingenieur degree in Automatic Control from Institut Nationale Polytechnique de Grenoble, France in 1980.
In 1971, 1972, 1975 and 1976, he worked for different electrical engineering consulting companies in Bogota, Colombia. In 1974 he was a professor in the Electrical Engineering Department of UIS, Colombia. From January 1981 to November 1990, he worked as a researcher at the Electrical Research Institute, Cuernavaca, Mexico. He was a professor of the graduate program in electrical engineering at the Universidad Autonoma de Nuevo Leon (UANL), Monterrey, Mexico, from December 1990 to December 1996. Since January 1997, he has been with CINVESTAV-IPN (Guadalajara Campus, Mexico) as a Professor of Electrical Engineering in their graduate programs. His research interests are in neural networks and fuzzy logic as applied to automatic control systems. He has been the advisor of 21 Ph. D. theses and 40 M. Sc theses.
He was granted a USA National Research Council Award as a research associate at NASA Langley Research Center, Hampton, Virginia, USA (January 1985 to March 1987). He is also a member of the Mexican National Research System (promoted to highest rank, III, in 2005), the Mexican Academy of Science and the Mexican Academy of Engineering. He has published four books, more than 150 technical papers in international journals and conferences, and has served as a reviewer for different international journals and conferences. He has also been a member of many international conferences, both IEEE and IFAC.
Affiliations and expertise
Research Professor, CINVESTAV GuadalajaraEH
Esteban A. Hernandez-Vargas
Dr. Esteban A. Hernandez-Vargas is a Professor at the Frankfurt Institute for Advanced Studies, Germany, and a research group leader and principal investigator at the Institute of Mathematics (UNAM, Mexico). He obtained his Ph.D. in Mathematics from the Hamilton Institute at the National University of Ireland. In July 2014, he founded the pioneering research group of Systems Medicine of Infectious Diseases at the Helmholtz Centre for Infection Research (HZI). He is a member of the Society of Mathematical Biology, the European Society of Virology, and the Mexican Research Council (CONACYT). Additionally, he is an Editorial Board member of Plos One, Journal of Franklin Institute, as well as a Research Topic Editor at Frontiers of Microbiology and Frontiers of Immunology.
Affiliations and expertise
Professor, Frankfurt Institute for Advanced Studies, Frankfurt am Main, GermanyJV
Jorge X. Velasco-Hernandez
Dr. Jorge X. Velasco Hernández did his doctoral studies in mathematics at the Claremont Graduate School. In July 1991 he started his postdoctoral studies at the Biometrynit/Mathematical Sciences Institute of Cornell University. In the summer of 1992, he returned to Mexico to UAM-Xochimilco, changing his assignment to the Department of Mathematics at UAM-Iztapalapa. From 94-96 he did a second postdoc at Cornell University. From 1996 to 2003 he was Full Professor (A, B and C) at UAMIztapalapa. In 2000 he did his sabbatical stay in the Department of Applied Mathematics at ITAM and IMP and in October 2001 he joined the Mexican Petroleum Institute as a Researcher in the Applied Mathematics and Computing Program, where he was Coordinator from 2004 to 2013. In October of that year, he joined the Institute of Mathematics of the UNAM in Queretaro as a Senior Researcher. He is a National Researcher III, Fellow of the Society for Industrial and Applied Mathematics, International Fellow of the Santa Fe Institute, member of the Mexican Academy of Sciences and president of the Mexican Mathematical Society for the period 2014-2016.
Affiliations and expertise
Adjunct Professor, Institute de Matemáticas, Universidad Nacional Autonoma de Mexico, Queretaro, MexicoRead Mathematical Modeling, Simulations, and AI for Emergent Pandemic Diseases on ScienceDirect