Mathematics of Public Health
Jummy David
Sold by BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
AbeBooks Seller since 11 January 2012
New - Hardcover
Condition: New
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Add to basketSold by BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
AbeBooks Seller since 11 January 2012
Condition: New
Quantity: 2 available
Add to basketThis item is printed on demand - it takes 3-4 days longer - Neuware -This volume addresses SDG 3 from a mathematical standpoint, sharing novel perspectives of existing communicable diseasemodelling technologies of the next generation and disseminating new developments in modelling methodologiesand simulation techniques. These methodologies are important fortraining and research in communicable diseases and can be applied to otherthreats to human health. The contributions contained in this collection/book cover a range ofmodelling 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 wascompiled from the weekly seminar series organized by the Mathematics for PublicHealth (MfPH) Next Generation Network. This network brings together the nextgeneration of modellers from across Canada and the world, developing the latestmathematical models, modeling methodologies, and analytical and simulation toolsfor communicable diseases of global public health concerns. The weekly seminarseries provides a unique forum for this network and their invited guest speakersto share their perspectives on the status and future directions ofmathematics of public health. 332 pp. Englisch.
Seller Inventory # 9783031408045
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:
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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