Fractional Stochastic Differential Equations: Applications to Covid-19 Modeling

This book discusses the spread modeling of Covid-19 using stochastics nonlocal differential and integral operators with singular and non-singular kernels, covering the global dynamic of Covid-19 spread behavior from December 2019 to September 2021.

Format: Hardback
Length: 540 pages
Publication date: 23 April 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the intricate foundations of COVID-19 spread modeling, employing stochastics nonlocal differential and integral operators with singular and non-singular kernels. It offers a detailed exploration of the dynamic nature of COVID-19 spread worldwide, highlighting the observed nonlocal behaviors that resemble power law, fading memory, crossover, and stochastic phenomena. Consequently, fractional stochastic differential equations are utilized to model the spread behaviors across various regions of the globe. The content encompasses a brief historical overview of COVID-19's global spread from December 2019 to September 2021, accompanied by a statistical analysis of the collected data for infected, deceased, and recovered individuals.

The book begins by providing a foundational introduction to stochastics, emphasizing its applications in modeling complex systems. It then delves into the theoretical framework of nonlocal differential and integral operators, highlighting their significance in capturing the intricate dynamics of COVID-19 spread. The authors introduce various kernels, including singular and non-singular ones, to model different aspects of the spread process.

The subsequent chapters delve into the application of nonlocal operators to model COVID-19 spread behavior. The authors discuss the use of fractional stochastic differential equations to capture the non-stationary and non-linear characteristics of the spread dynamic. They present examples of different models and their corresponding mathematical formulations, illustrating how these equations can be used to predict the spread of COVID-19 in different regions of the world.

Throughout the book, the authors emphasize the importance of data analysis in understanding COVID-19 spread patterns. They provide statistical analysis of the collected data for infected, deceased, and recovered individuals, highlighting key trends and patterns. The analysis helps validate the models and provides insights into the factors that influence the spread of the disease.

In addition to the theoretical and computational aspects, the book also discusses the policy implications of COVID-19 spread modeling. The authors explore how the models can be used to inform decision-making processes and guide public health interventions. They discuss the challenges associated with modeling and forecasting the spread of COVID-19 and offer recommendations for improving the accuracy and reliability of the models.

Overall, this book serves as a valuable resource for researchers, policymakers, and healthcare professionals interested in understanding the dynamics of COVID-19 spread. It provides a comprehensive and up-to-date treatment of the subject, covering both theoretical foundations and practical applications. By leveraging stochastics nonlocal differential and integral operators with singular and non-singular kernels, the book offers a powerful tool for modeling and predicting the spread of COVID-19 worldwide.

Weight: 992g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811907289
Edition number: 1st ed. 2022