COVID Transmission Modeling

An Insight into Infectious Diseases Mechanism

DM Basavarajaiah, B Narasimha Murthy

• Publisher: Chapman and Hall/CRC
• Year: 2022
• Print ISBN: 9781032518886
• eBook ISBN: 9781000593211
• Topic: Mathematics
• Subtopic: Mathematics General
• Index: Mathematics

COVID Transmission Modeling: An Insight into Infectious Diseases Mechanism provides an interdisciplinary overview of the COVID-19 pandemic crisis and covers various aspects of newer modeling techniques and practical solutions for health emergencies. This book aims to formulate various innovative and pragmatic mathematical, statistical, and epidemiological models using COVID-19 real data sets. It emphasizes interdisciplinary theoretical postulates derived from practical insights and knowledge of public health. Each of the book's 12 chapters provides invaluable and exploratory tools to enable explicit assumptions, highlights key health indicators, and determines the geometric progression and control measures of the disease. The present developed models will allow readers to extrapolate the exact reason for the outbreak and pave the way for scientific information on vaccine trials and socioeconomic, psychological, and disease burden worldwide. These advanced techniques of modeling and their applications are in greater need than ever for effective connection between mathematicians, statisticians, epidemiologists, researchers, clinicians, and policymakers for making appropriate decisions at the right time. With the advent of emerging health science, all models are demonstrated with real-life data sets and provided with illustrations and eye-catching graphs and diagrams so that the readers can easily understand the concept of COVID-19 pandemic interventions and their control measures, and their impact.

Features

• Addresses all aspects of mitigation/control measures, estimation of transmission rate, economic impact assessment, genetic complexity of COVID-19, herd immunity, and various methods, including newer mathematical, statistical, and epidemiological models in the analysis of COVID-19 pandemic outbreak
• Covers the application of innovative, advanced statistical and epidemiological models and demonstrates possible solutions toward supportive treatment aspects of COVID-19 and its control measures
• Includes models that can easily be followed in formulating the mathematical derivations and key points
• Supplemented with ample illustrations, images, diagrams, and figures

This book is aimed at postgraduate students studying medicine and healthcare, mathematics, and statistical information. Researchers will also find this book very helpful.

1 Mathematical Modeling Approach to COVID-19: Vetted Real Data

DOI: 10.1201/9781003204794-1

1.1 Introduction

The novel coronavirus (nCov2019) is also known as COVID-19 or SARS-CoV-2. Primarily, the disease spreads from one individual to another through droplets released while coughing and sneezing, hugging or handshaking. These actions would enable the virus to land on the mouth or nose of nearby people. WHO declared nCov as a pandemic on March 11, 2020. This global pandemic gregariously affected approximately 7.30 million individuals, among which 0.16 million have lost their lives and 2.03 million are still suffering from debilitating nCov2019 (WHO, Dec 2020). People of all age groups are infected by this new virus; older people and people with comorbidities (such as asthma, diabetes, CVD, hypertension, and so on) appear to be more vulnerable, becoming severely ill. WHO advise people of all age groups to take steps to protect themselves from the virus, for example by practicing good hand hygiene and good respiratory hygiene. As far as the COVID outbreak is concerned, the virus seeded outside China and subsequently got localized, which may be inevitable, unless some kind of draconian measures were taken at the global level, viz. cancellation of mass gathering, school closures, and instituting work from home arrangements. Since the containment failed in the epidemic area, local transmission was established for the purpose of migrating to a secured place. A lot of measures were taken during the previous major outbreaks, e.g., SARS, MERS, or pandemic influenza, which could serve as useful cited information for the implementation of causative measures on health policy at a global level. While the lockdown did mitigate the impact of coronavirus outbreak on the population, it has dire implications on economic activity, affecting almost every sector. Both private entrepreneurs and governments should consider the fine nuances of this impact and respond appropriately to ameliorate the crisis.

Decision making while managing the health crisis is certainly the most important task of healthcare workers and is often a very difficult one. This study offers a decision-making procedure for solving complex problems step by step. It presents the decision analysis process for both public and public health policymakers, thus enabling them to formulate the criteria of planner of health policy using different types of information pertaining to COVID-19 pandemic. We resourced information of varying quality with due thumb rules and tradition. Mere prediction of disease pandemics is no longer sufficient for addressing such common decisions vis-à-vis prevention and eradication. In general, the forces of various mathematical predictions provide accurate decision making at all levels of control measures. The decision analysts provide quantitative supports for the decision makers in all areas of health science, including analysis of economic impact after disease outbreak. During the intervention of COVID-19, the following table is essential for a review of the entire spectrum of disease outbreaks at the population level (Figure 1.1 and Table 1.1).

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Table of contents

• Cover
• Half Title Page
• Title Page
• Copyright Page
• Dedication Page
• Table of Contents
• Preface
• Acknowledgments
• Authors
• 1. Mathematical Modeling Approach to COVID-19: Vetted Real Data
• 2. Time Series Stochastic Projection Models for the Estimation of COVID Trend
• 3. Study of Anxiety and Fear of COVID-19
• 4. COVID-19 Gene Sequencing Modeling
• 5. Real-Time PCR (RT-PCR) for COVID-19 Diagnosis and Changes in Threshold Cycle (Ct) in Association with Different Parameters
• 6. COVID-19 Vaccination Modeling Approach to Public Health Policy
• 7. Trend of COVID-19 Surge Projection
• 8. Risk Analysis of COVID-19—Vetted by Real Data Sets
• 9. Data-Driven Decision Support Model for COVID-19
• 10. Age-Specific SARS-Cov-19 Incidence Modeling
• 11. National Health Policies and Their Perspectives on the Approach to Control COVID-19 and Other Infectious Diseases
• 12. Statistical Ethics for Computational Biology and Medical Science
• Index