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Mathematics of Public Health

Mathematical Modelling from the Next Generation

  • Book
  • © 2023

Overview

Editors:
  1. Jummy David

    1. Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada

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  2. Jianhong Wu

    1. Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Introduces readers to basic epidemiology and relevant control and prevention issues of prototypic diseases
  • Provides a great resource for newcomers in the field to see the real-world applicability of mathematical methodologies
  • Addresses SDG 3 from a mathematical standpoint

Part of the book series: Fields Institute Communications (FIC, volume 88)

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About this book

This volume addresses SDG 3 from a mathematical standpoint, sharing novel perspectives of existing communicable disease modelling technologies of the next generation and disseminating new developments in modelling methodologies and simulation techniques. These methodologies are important for training and research in communicable diseases and can be applied to other threats to human health.  The contributions contained in this collection/book cover a range of modelling techniques that have been and may be used to support decision-making on critical health related issues such as:

  • Resource allocation
  • Impact of climate change on communicable diseases
  • Interaction of human behaviour change, and disease spread
  • Disease outbreak trajectories projection
  • Public health interventions evaluation
  • Preparedness and mitigation of emerging and re-emerging infectious diseases outbreaks
  • Development of vaccines and decisions around vaccine allocation and optimization
The diseases and public health issues in this volume include, but are not limited to COVID-19, HIV, Influenza, antimicrobial resistance (AMR), the opioid epidemic, Lyme Disease, Zika, and Malaria. In addition, this volume compares compartmental models, agent-based models, machine learning and network. Readers have an opportunity to learn from the next generation perspective of evolving methodologies and algorithms in modelling infectious diseases, the mathematics behind them, the motivation for them, and some applications to supporting critical decisions on prevention and control of communicable diseases.

This volume was compiled from the weekly seminar series organized by the Mathematics for Public Health (MfPH) Next Generation Network. This network brings together the next generation of modellers from across Canada and the world, developing the latest mathematical models, modeling methodologies, and analytical and simulation tools for communicable diseases of global public health concerns. The weekly seminar series provides a unique forum for this network and their invited guest speakers to share their perspectives on the status and future directions of mathematics of public health.

Keywords

Table of contents (12 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Mathematical Models: Perspectives of Mathematical Modelers and Public Health Professionals

    • Jummy David, Gabrielle Brankston, Idriss Sekkak, Sungju Moon, Xiaoyan Li, Sana Jahedi et al.
    Pages 1-35

  3. Discovering First Principle of Behavioural Change in Disease Transmission Dynamics by Deep Learning

    • Pengfei Song, Yanni Xiao, Jianhong Wu
    Pages 37-54

  4. Understanding Epidemic Multi-wave Patterns via Machine Learning Clustering and the Epidemic Renormalization Group

    • Shahram Vatani, Giacomo Cacciapaglia
    Pages 55-86

  5. Contact Matrices in Compartmental Disease Transmission Models

    • Jesse Knight, Sharmistha Mishra
    Pages 87-110

  6. An Optimal Control Approach for Public Health Interventions on an Epidemic-Viral Model in Deterministic and Stochastic Environments

    • Idriss Sekkak, Bouchra R. Nasri
    Pages 111-128

  7. Modeling Airborne Disease Dynamics: Progress and Questions

    • Arnab Mukherjee, Saptarshi Basu, Shubham Sharma, Swetaprovo Chaudhuri
    Pages 129-159

  8. Modeling Mutation-Driven Emergence of Drug-Resistance: A Case Study of SARS-CoV-2

    • Congjie Shi, Thomas N. Vilches, Ao Li, Jianhong Wu, Seyed M. Moghadas
    Pages 161-174

  9. A Categorical Framework for Modeling with Stock and Flow Diagrams

    • John C. Baez, Xiaoyan Li, Sophie Libkind, Nathaniel D. Osgood, Eric Redekopp
    Pages 175-207

  10. Agent-Based Modeling and Its Trade-Offs: An Introduction and Examples

    • G. Wade McDonald, Nathaniel D. Osgood
    Pages 209-242

  11. Mathematical Assessment of the Role of Interventions Against SARS-CoV-2

    • Salman Safdar, Abba B. Gumel
    Pages 243-294

  12. Long-Term Dynamics of COVID-19 in a Multi-strain Model

    • Elisha B. Are, Jessica Stockdale, Caroline Colijn
    Pages 295-317

  13. Correction to: Contact Matrices in Compartmental Disease Transmission Models

    • Jesse Knight, Sharmistha Mishra
    Pages C1-C1
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Editors and Affiliations

  • Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, Canada

    Jummy David, Jianhong Wu

About the editors

Jummy David is currently a public health analytics and modeling fellow at the Centers for Disease Control and Prevention (CDC), USA. Prior to CDC, she was a postdoctoral fellow at York University, and obtained her PhD (2020) and Master’s degree from the University of British Columbia, Canada. She is an applied mathematician, a computational and research-driven scientist with in-depth, hands-on advanced mathematical modeling and simulation methods. She is great at leveraging on analytics and quantitative experiences to develop and analyze mechanistically relevant mathematical models, PK/PD models, and health economic models. Results of her modeling and simulation analysis are used to evaluate, forecast, predict, optimize and accelerate policy designs toward improving decision making in public health, medicine, computational biology and science.

 

Jianhong Wu is the founding Director of the Laboratory for Industrial and Applied Mathematics, and theInaugural Director of the York Emergency Mitigation, Engagement, Response, and Governance Institute. He holds the life-time title of University Distinguished Research Professor, the Canada Research Chair (2001-2022) and York Research Chair (2022-) in Industrial and Applied Mathematics at York University. He was also awarded the NSERC/Sanofi Industrial Research Chair in Vaccine Mathematics, Modelling and Manufacturing in 2017-2022. He is an elected Fellow of the Canadian Academy of Health Sciences, and Fellow of the Royal Society of Canada. He received the 2003 Canadian Applied and Industrial Mathematical Society’s Research Prize and the 2019 CAIMS-Fields Industrial Mathematics Prize, for his contribution to the following fields: nonlinear dynamics and delay differential equations; neural networks and pattern recognition; mathematical ecology and epidemiology; big data analytics.

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