Buch(elektronisch)2022

COVID transmission modelling: an insight into infectious diseases mechanism

Basavarajaiah, D. M.; Murthy, B. Narasimha

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Abstract

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- Authors -- 1. Mathematical Modeling Approach to COVID-19: Vetted Real Data -- 1.1 Introduction -- 1.1.1 Main Objectives of This Book -- 1.2 Anatomical Structure of COVID-19 Virus -- 1.3 Virus Incubation Period -- 1.3.1 Disease Carriers -- 1.3.2 Case Fatality Rate (CFR, %) -- 1.3.3 COVID-19 Transmission Mechanisms -- 1.4 Epidemiological Aspects of nCov2019 (SARS-Cov-19) -- 1.5 Economic Impacts of Novel Coronavirus -- 1.6 Mathematical Model for the Prediction of Novel Coronavirus (nCov2019) -- 1.6.1 Variables Used for Model Building -- 1.6.2 Endemic and Epidemic Equilibria -- 1.7 Model Discussion -- 1.7.1 Model Conclusions -- 1.8 Epidemiological Model for the Estimation of Hazard Rate and Geometric Progression of nCov2019 -- 1.8.1 Formulation of the Epidemiological Risk Assessment COVID Model -- 1.9 Epidemiological Model Approach of New Diseases -- 1.9.1 Model Formulation -- 1.9.1.1 Latent growth model of novel coronavirus -- 1.9.2 Gauss-Markov Theorem (GMT) -- 1.9.3 Maximum Likelihood Estimation (MLE) of Gauss-Markov Theorem (GMT) -- 1.9.4 Gauss-Markov Weighted Least Squares Analysis -- 1.10 Susceptible-Infective-Recovered (SIR) Epidemiological Model of COVID -- 1.10.1 Model Formulation -- 1.10.2 SIR Model Discussion -- 1.11 EP Model with Varying Population -- 1.11.1 Reproduction Number Approach to Binomial Distribution (R[sub(0)]) -- 1.12 Machine Learning Model for SARS-Cov-19 -- 1.12.1 Machine Learning Model -- 1.13 Models of Machine Learning -- 1.13.1 Measurement Error (u) -- 1.13.2 Stochastic Error (u) -- 1.13.3 Hidden Gauss-Markov Theorem (HGMT) -- 1.14 COVID-19 Mathematical Model Approach to Selective Sample -- 1.14.1 Model Formulation -- 1.15 Recommendations -- 1.16 Study Limitations -- 1.17 Conflict of Interest.

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