Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics

ISBN: 9783031330490
Код товара 155357

Mondaini

Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics

Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics

ISBN: 9783031330490
Код товара 155357

19 564 ₽

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Если Вы закажете книгу до 17.01.2025, то мы привезём её ориентировочно 28.02.2025.

  • Автор

    Mondaini

  • Издатель

    Springer

  • Тип обложки

    Hardback

  • Год издания

    2023

  • ISBN

    9783031330490

  • Язык

    ENG

О чём книга?

This volume gathers together selected peer-reviewed works presented at the BIOMAT 2022 International Symposium, which was virtually held on November 7-11, 2022, with an organization staff based in Rio de Janeiro, Brazil. Topics touched on in this volume include infection spread in a population described by an agent-based approach; the study of gene essentiality via network-based computational modeling; stochastic models of neuronal dynamics; and the modeling of a statistical distribution of amino acids in protein domain families. The reader will also find texts in epidemic models with dynamic social distancing; with no vertical transmission; and with general incidence rates. Aspects of COVID-19 dynamics: the use of an SEIR model to analyze its spread in Brazil; the age-dependent manner of modeling its spread pattern; the impact of media awareness programs; and a web-based computational tool for Non-invasive hemodynamics evaluation of coronary stenosis are also covered. Held every year since 2001, The BIOMAT International Symposium gathers together, in a single conference, researchers from Mathematics, Physics, Biology, and affine fields to promote the interdisciplinary exchange of results, ideas and techniques, promoting truly international cooperation for problem discussion. BIOMAT volumes published from 2017 to 2021 are also available by Springer.

Dynamics of an SIS Epidemic Model with no Vertical Transmission.- Infection Spread in Populations: An Agent Based Model.- Network-based Computational Modeling to unravel Gene Essentiality.- Nonlinear Dynamics in an SIR model with Ratio-Dependent Incidence
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