Full Description
Mathematical 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.
Contents
1. Modeling during an unprecedented pandemic
2. Global epidemiology and impact of the SARS-CoV-2 pandemic
3. Analysis of an ongoing epidemic: Advantages and limitations of COVID-19 modeling
4. On spatial heterogeneity of COVID-19 using shape analysis of pandemic curves
5. Pandemic response: Isolationism or solidarity?
6. Optimizing contact tracing: Leveraging contact network structure
7. Applications of deep learning in forecasting COVID-19 pandemic and county-level risk warning
8. COVID-19 population dynamics neural control from a complex network perspective
9. An agent-based model for COVID-19 and its interventions and impact in different social phenomena
10. Implementation of mitigation measures and modeling of in-hospital dynamics depending on the COVID-19 infection status
11. A mathematical model for the reopening of schools in Mexico
12. Mathematical assessment of the role of vaccination against COVID-19 in the United States
13. Ascertainment and biased testing rates in surveillance of emerging infectious diseases
14. Dynamical study of SARS-CoV-2 mathematical models under antiviral treatments
15. Statistical modeling to understand the COVID-19 pandemic
16. After COVID-19: Mathematical models, epidemic preparedness, and external factors in epidemic management
