This compendium represents a set of guides to understanding the challenging scientific, epidemiological, clinical, social, and economic phenomenon that is represented by the COVID-19 pandemic.
The book explains the mathematical modeling of COVID-19 infection, with emphasis on traditional epidemiological principles. It represents a rigorous, comprehensive and multidisciplinary approach to a complex phenomenon. The chapters take into account the knowledge arising from different disciplines (epidemiology, pathophysiology, immunology, medicine, biology, vaccine development, etc.). It also covers COVID-19 data analysis, giving the reader a perspective of statistics and data science, and includes a discussion about social and economic issues of the pandemic. Each chapter is devoted to a specific topic, and is contributed by experts in epidemiology.
Because of its multidisciplinary nature, this book is intended as a reference on mathematical models and basic immunotherapy for COVID-19 for a broad community of readers, from scholars who have scientific training, to general readers who have an interest in the disease.
The book explains the mathematical modeling of COVID-19 infection, with emphasis on traditional epidemiological principles. It represents a rigorous, comprehensive and multidisciplinary approach to a complex phenomenon. The chapters take into account the knowledge arising from different disciplines (epidemiology, pathophysiology, immunology, medicine, biology, vaccine development, etc.). It also covers COVID-19 data analysis, giving the reader a perspective of statistics and data science, and includes a discussion about social and economic issues of the pandemic. Each chapter is devoted to a specific topic, and is contributed by experts in epidemiology.
Because of its multidisciplinary nature, this book is intended as a reference on mathematical models and basic immunotherapy for COVID-19 for a broad community of readers, from scholars who have scientific training, to general readers who have an interest in the disease.
Table of Contents
- Foreword
- Preface
- Dedication
- List of Contributors
1 Introduction
1.1 Importance
1.2 the Covid-19 Pandemic as a Complex Phenomenon
1.3 Mathematical and Computational Modeling in Epidemiology
2 Different Perspectives for the Study of the Disease and the Covid-19 Pandemic
2.1 Action of the Virus at Different Scales of Biological Organization
2.2 Temporal Evolution of Covid-19 Disease at the Individual Level
2.3 Temporal Evolution of the Covid-19 Pandemic at the Country Level And At the Global Level; the Role of Human Mobility
2.4 Collateral Consequences for Human Health, Society and the Economy; Final Comments
3 Mathematical and Computational Modeling of Covid-19: Results and Perspectives
3.1 Main Characteristics of Forecasting and Compartmental Models and The Basic Assumptions That Support Them
3.2 Renewal Equations: Another Scheme for the Construction Of Compartmental Models
3.3 Methodology for the Application of Compartmental Models of the Sir-Type 47
3.4 What Are Compartmental Epidemiological Models Useful for and What Are Their Limitations?
3.5 How Are the Components of An Epidemiological Compartmental Model Constructed in Correspondence With the Application It is Intended For?
3.6 Some Important Results Obtained on Covid-19 With the Use Of Compartmental Mathematical Models
3.7 Some Pending Issues in the Collection and Analysis of Data and in The Study of the Mathematical Models, Associated With Covid-19
4 Concluding Remarks
5 Consent for Publication
6 Conflict of Interest
7 Acknowledgements
- References
1 Introduction
1.1 Importance
1.2 Preliminaries
2 Coronaviruses of Public Health Importance
3 the Sars-Cov-2 Virus
4 Origin and Evolution of the Pandemic
5 International Health Regulations and Mechanisms for Preparing and Responding To Threats to Public Health
6 What is a Public Health Emergency of International Concern?
7 Global Epidemiological Situation and in Mexico of Covid-19
8 Transmission Mechanisms and “Reproduction Number” (R0)
9 Preventive Measures
10 Concluding Remarks
11 Consent for Publication
12 Conflict of Interest
13 Acknowledgements
- References
1 Introduction
1.1 Importance
1.2 Preliminaries
2 Pathophysiology of the Disease
2.1 Virology
2.2 Inflammatory Response
2.3 Thrombotic Phenomena
3 Clinical Manifestations of Sars-Cov-2 Infection
3.1 Asymptomatic Infection
3.2 Symptomatic Infection
3.3 Clinical Manifestations
4 Drug Treatment
4.1 Convalescent Patient Plasma
4.2 Remdesivir
4.3 Baricitinib
4.4 Anticoagulation and Covid-19
4.5 Steroid Use in Covid-19
4.6 Tocilizumab
5 Concluding Remarks
6 Consent for Publication
7 Conflict of Interest
8 Acknowledgements
- References
1 Introduction
1.1 Importance
1.2 Preliminaries
2 Basic Results of the Kermack-Mckendrick Model
3 More General Models
4 Modification of the Basic Models
5 Epidemic Curves
6 Epidemic Interactions
7 Concluding Remarks
8 Consent for Publication
9 Conflict of Interest
10 Acknowledgements
- References
1 Introduction
1.1 Importance
1.2 Preliminaries
2 Artificial Intelligence: the New Boom
3 General Framework of Data Science Projects
3.1 the Data Science Workflow
3.2 Exploratory Data Analysis and Preprocessing
4 Case Study: the Mexican Covid-19 Data
4.1 the Database
4.2 Bayesian Networks
4.3 Decision Trees
5 Concluding Remarks
6 Consent for Publication
7 Conflict of Interest
8 Acknowledgements
- References
Contributors
- Andrés Fraguela-Collar
