| Description |
1 online resource (x, 317 pages) : illustrations (chiefly color) |
| Series |
Fields Institute communications ; volume 88 |
|
Fields Institute communications ; v. 88.
|
| Contents |
Preface -- Mathematical models: perspectives of mathematical modelers and public health professionals -- Discovering first-principle of behavioural change in disease transmission dynamics by deep learning -- Understanding Epidemic Multi-Wave Patterns via Machine Learning Clustering and the Epidemic Renormalization Group -- Contact Matrices in Compartmental Disease Transmission Models -- An optimal control approach for public health interventions on an Epidemic-Viral model in deterministic and stochastic environments -- Modeling airborne disease dynamics: progress and questions -- Modelling mutation-driven emergence of drug-resistance: a case study of SARS-CoV-2 -- A Categorical Framework for Modeling with Stock and Flow Diagrams -- Agent-Based Modeling and its Tradeoffs: An Introduction & Examples -- Mathematical assessment of the role of interventions against SARS-CoV-2 -- Long term dynamics of COVID-19 in a multi-strain model |
| Summary |
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 |
| Notes |
Print version record |
| Subject |
Public health -- Mathematical models
|
|
Communicable diseases -- Mathematical models
|
| Form |
Electronic book
|
| Author |
David, Jummy, editor
|
|
Wu, Jianhong, 1964- editor.
|
| ISBN |
9783031408052 |
|
3031408055 |
|
