Mathematics of Public Health
Jummy David
Sold by buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketSold by buchversandmimpf2000, Emtmannsberg, BAYE, Germany
AbeBooks Seller since 23 January 2017
Condition: New
Quantity: 1 available
Add to basketThis item is printed on demand - Print on Demand Titel. Neuware -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 allocationImpact of climate change on communicable diseasesInteraction of human behaviour change, and disease spreadDisease outbreak trajectories projectionPublic health interventions evaluationPreparedness and mitigation of emerging and re-emerging infectious diseases outbreaksDevelopment of vaccines and decisions around vaccine allocation and optimizationThe 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 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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