Mathematics of Public Health Mathematical Modelling from the Next Generation /
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 trainin...
-д хадгалсан:
| Байгууллагын зохиогч: | |
|---|---|
| Бусад зохиолчид: | , |
| Формат: | Цахим Цахим ном |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
|
| Хэвлэл: | 1st ed. 2023. |
| Цуврал: | Fields Institute Communications,
88 |
| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.1007/978-3-031-40805-2 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
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| 245 | 1 | 0 | |a Mathematics of Public Health |h [electronic resource] : |b Mathematical Modelling from the Next Generation / |c edited by Jummy David, Jianhong Wu. |
| 250 | |a 1st ed. 2023. | ||
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| 490 | 1 | |a Fields Institute Communications, |x 2194-1564 ; |v 88 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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 simulationtools 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Public health. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Epidemiology. | |
| 650 | 1 | 4 | |a Mathematical Modeling and Industrial Mathematics. |
| 650 | 2 | 4 | |a Public Health. |
| 650 | 2 | 4 | |a Optimization. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Epidemiology. |
| 700 | 1 | |a David, Jummy. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Wu, Jianhong. |e editor. |0 (orcid)0000-0003-0376-325X |1 https://orcid.org/0000-0003-0376-325X |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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