Exploring Susceptible-Infectious-Recovered (SIR) Model for COVID-19 Investigation

Exploring Susceptible-Infectious-Recovered (SIR) Model for COVID-19 Investigation
Author: Rahul Saxena
Publisher: Springer Nature
Total Pages: 63
Release: 2022-09-02
Genre: Technology & Engineering
ISBN: 9811941750
GET BOOK

Download Exploring Susceptible-Infectious-Recovered (SIR) Model for COVID-19 Investigation Book in PDF, Epub and Kindle

The book focuses on mathematical modelling of COVID-19 pandemic using the Susceptible, Infectious, and Recovered (SIR) model. The predictive modelling of the disease, with the exact facts and figures, provides a ground to reason about growing trends and its future trajectory. The book emphasizes on how the pandemic actually spreads out, lockdown impact analysis, and future course of actions based on mathematical calculations. Moreover, since COVID-19 spread outburst has been twice, the intensity studies and comparative analysis of the two waves of COVID-19 are another interesting feature of the book content. The book is a knowledge base for various researchers and academicians to dive into the detailing of the COVID spread (mathematical) model and understand how it could be explored to draw outcomes. To represent the factual information and analytical results effectively, graphical and diagrammatic representations have been appended at appropriate places. To keep the explanation simple and yet concrete, mathematical concepts have also been introduced; to carry out analysis to generate results for understanding the viral dynamics.

Applied Mathematical Ecology

Applied Mathematical Ecology
Author: Simon A. Levin
Publisher: Springer Science & Business Media
Total Pages: 498
Release: 2012-12-06
Genre: Mathematics
ISBN: 3642613179
GET BOOK

Download Applied Mathematical Ecology Book in PDF, Epub and Kindle

The Second Autumn Course on Mathematical Ecology was held at the Intern ational Centre for Theoretical Physics in Trieste, Italy in November and December of 1986. During the four year period that had elapsed since the First Autumn Course on Mathematical Ecology, sufficient progress had been made in applied mathemat ical ecology to merit tilting the balance maintained between theoretical aspects and applications in the 1982 Course toward applications. The course format, while similar to that of the first Autumn Course on Mathematical Ecology, consequently focused upon applications of mathematical ecology. Current areas of application are almost as diverse as the spectrum covered by ecology. The topiys of this book reflect this diversity and were chosen because of perceived interest and utility to developing countries. Topical lectures began with foundational material mostly derived from Math ematical Ecology: An Introduction (a compilation of the lectures of the 1982 course published by Springer-Verlag in this series, Volume 17) and, when possible, progressed to the frontiers of research. In addition to the course lectures, workshops were arranged for small groups to supplement and enhance the learning experience. Other perspectives were provided through presentations by course participants and speakers at the associated Research Conference. Many of the research papers are in a companion volume, Mathematical Ecology: Proceedings Trieste 1986, published by World Scientific Press in 1988. This book is structured primarily by application area. Part II provides an introduction to mathematical and statistical applications in resource management.

Analysis of the SIR Model on a Network of Nodes

Analysis of the SIR Model on a Network of Nodes
Author: Isabelle Stepler
Publisher:
Total Pages: 0
Release: 2023
Genre:
ISBN:
GET BOOK

Download Analysis of the SIR Model on a Network of Nodes Book in PDF, Epub and Kindle

The Susceptible-Infected-Recovered model, or SIR model, was one of the first infectious disease models created. Since its first application, it has been modified in many different ways to more accurately model infectious diseases in different situations. This paper explores the standard SIR model, the standard SIR model without lifetime immunity, and the multi-node SIR model in order to investigate the effectiveness and the differences between each model. We analyze the convergence of each model as time approaches infinity, simulate the two-node model to confirm that multi-node SIR models can reproduce multiple infection peaks, and discuss parameter estimation to discover when SIR models, especially on multiple nodes, can be fit to data and then used for prediction. Due to these investigations, we conclude that multi-node SIR models are a great option for modeling complicated, real-world disease situations because of their inherent flexibility and ability to reproduce more varied infection dynamics. For ideal parameter estimation, multi-node SIR models should only have a few nodes and use data points as spaced out in time as possible, allowing prediction to be more accurate.

Mathematical Epidemiology

Mathematical Epidemiology
Author: Fred Brauer
Publisher: Springer Science & Business Media
Total Pages: 415
Release: 2008-04-30
Genre: Medical
ISBN: 3540789103
GET BOOK

Download Mathematical Epidemiology Book in PDF, Epub and Kindle

Based on lecture notes of two summer schools with a mixed audience from mathematical sciences, epidemiology and public health, this volume offers a comprehensive introduction to basic ideas and techniques in modeling infectious diseases, for the comparison of strategies to plan for an anticipated epidemic or pandemic, and to deal with a disease outbreak in real time. It covers detailed case studies for diseases including pandemic influenza, West Nile virus, and childhood diseases. Models for other diseases including Severe Acute Respiratory Syndrome, fox rabies, and sexually transmitted infections are included as applications. Its chapters are coherent and complementary independent units. In order to accustom students to look at the current literature and to experience different perspectives, no attempt has been made to achieve united writing style or unified notation. Notes on some mathematical background (calculus, matrix algebra, differential equations, and probability) have been prepared and may be downloaded at the web site of the Centre for Disease Modeling (www.cdm.yorku.ca).

An Introduction to Mathematical Epidemiology

An Introduction to Mathematical Epidemiology
Author: Maia Martcheva
Publisher: Springer
Total Pages: 462
Release: 2015-10-20
Genre: Mathematics
ISBN: 1489976124
GET BOOK

Download An Introduction to Mathematical Epidemiology Book in PDF, Epub and Kindle

The book is a comprehensive, self-contained introduction to the mathematical modeling and analysis of infectious diseases. It includes model building, fitting to data, local and global analysis techniques. Various types of deterministic dynamical models are considered: ordinary differential equation models, delay-differential equation models, difference equation models, age-structured PDE models and diffusion models. It includes various techniques for the computation of the basic reproduction number as well as approaches to the epidemiological interpretation of the reproduction number. MATLAB code is included to facilitate the data fitting and the simulation with age-structured models.

Modeling Infectious Diseases in Humans and Animals

Modeling Infectious Diseases in Humans and Animals
Author: Matt J. Keeling
Publisher: Princeton University Press
Total Pages: 385
Release: 2011-09-19
Genre: Science
ISBN: 1400841038
GET BOOK

Download Modeling Infectious Diseases in Humans and Animals Book in PDF, Epub and Kindle

For epidemiologists, evolutionary biologists, and health-care professionals, real-time and predictive modeling of infectious disease is of growing importance. This book provides a timely and comprehensive introduction to the modeling of infectious diseases in humans and animals, focusing on recent developments as well as more traditional approaches. Matt Keeling and Pejman Rohani move from modeling with simple differential equations to more recent, complex models, where spatial structure, seasonal "forcing," or stochasticity influence the dynamics, and where computer simulation needs to be used to generate theory. In each of the eight chapters, they deal with a specific modeling approach or set of techniques designed to capture a particular biological factor. They illustrate the methodology used with examples from recent research literature on human and infectious disease modeling, showing how such techniques can be used in practice. Diseases considered include BSE, foot-and-mouth, HIV, measles, rubella, smallpox, and West Nile virus, among others. Particular attention is given throughout the book to the development of practical models, useful both as predictive tools and as a means to understand fundamental epidemiological processes. To emphasize this approach, the last chapter is dedicated to modeling and understanding the control of diseases through vaccination, quarantine, or culling. Comprehensive, practical introduction to infectious disease modeling Builds from simple to complex predictive models Models and methodology fully supported by examples drawn from research literature Practical models aid students' understanding of fundamental epidemiological processes For many of the models presented, the authors provide accompanying programs written in Java, C, Fortran, and MATLAB In-depth treatment of role of modeling in understanding disease control

Computational Modeling and Data Analysis in COVID-19 Research

Computational Modeling and Data Analysis in COVID-19 Research
Author: Chhabi Rani Panigrahi
Publisher: CRC Press
Total Pages: 271
Release: 2021-05-09
Genre: Medical
ISBN: 1000384977
GET BOOK

Download Computational Modeling and Data Analysis in COVID-19 Research Book in PDF, Epub and Kindle

This book covers recent research on the COVID-19 pandemic. It includes the analysis, implementation, usage, and proposed ideas and models with architecture to handle the COVID-19 outbreak. Using advanced technologies such as artificial intelligence (AI) and machine learning (ML), techniques for data analysis, this book will be helpful to mitigate exposure and ensure public health. We know prevention is better than cure, so by using several ML techniques, researchers can try to predict the disease in its early stage and develop more effective medications and treatments. Computational technologies in areas like AI, ML, Internet of Things (IoT), and drone technologies underlie a range of applications that can be developed and utilized for this purpose. Because in most cases there is no one solution to stop the spreading of pandemic diseases, and the integration of several tools and tactics are needed. Many successful applications of AI, ML, IoT, and drone technologies already exist, including systems that analyze past data to predict and conclude some useful information for controlling the spread of COVID-19 infections using minimum resources. The AI and ML approach can be helpful to design different models to give a predictive solution for mitigating infection and preventing larger outbreaks. This book: Examines the use of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and drone technologies as a helpful predictive solution for controlling infection of COVID-19 Covers recent research related to the COVID-19 pandemic and includes the analysis, implementation, usage, and proposed ideas and models with architecture to handle a pandemic outbreak Examines the performance, implementation, architecture, and techniques of different analytical and statistical models related to COVID-19 Includes different case studies on COVID-19 Dr. Chhabi Rani Panigrahi is Assistant Professor in the Department of Computer Science at Rama Devi Women’s University, Bhubaneswar, India. Dr. Bibudhendu Pati is Associate Professor and Head of the Department of Computer Science at Rama Devi Women’s University, Bhubaneswar, India. Dr. Mamata Rath is Assistant Professor in the School of Management (Information Technology) at Birla Global University, Bhubaneswar, India. Prof. Rajkumar Buyya is a Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia.