ABSTRACT
Methods of Mathematical Modeling: Infectious Diseases presents computational methods related to biological systems and their numerical treatment via mathematical tools and techniques. Edited by renowned experts in the field, Dr. Hari Mohan Srivastava, Dr. Dumitru Baleanu, and Dr. Harendra Singh, the book examines advanced numerical methods to provide global solutions for biological models. These results are important for medical professionals, biomedical engineers, mathematicians, scientists and researchers working on biological models with real-life applications. The authors deal with methods as well as applications, including stability analysis of biological models, bifurcation scenarios, chaotic dynamics, and non-linear differential equations arising in biology. The book focuses primarily on infectious disease modeling and computational modeling of other real-world medical issues, including COVID-19, smoking, cancer and diabetes. The book provides the solution of these models so as to provide actual remedies. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
The novel corona virus (COVID-19) has badly affected many countries (more than 180 countries including China) in the world. More than 90% of the global COVID-19 cases are currently outside China. The large, unanticipated number of COVID-19 cases has interrupted the healthcare system in many countries and created shortages for bed space in hospitals. Consequently, better estimation of COVID-19 infected people in Sri Lanka is vital for government to take suitable action. This paper investigates predictions on both the number of the first and the second waves of COVID-19 cases in Sri Lanka. First, to estimate the number of first wave of future COVID-19 cases, we develop a stochastic forecasting model and present a solution technique for the model. Then, another solution method is proposed to the two existing models (SIR model and Logistic growth model) for the prediction on the second wave of COVID-19 cases. Finally, the proposed model and solution approaches are validated by secondary data obtained from the Epidemiology Unit, Ministry of Health, Sri Lanka. A comparative assessment on actual values of COVID-19 cases shows promising performance of our developed stochastic model and proposed solution techniques. So, our new finding would definitely be benefited to practitioners, academics and decision makers, especially the government of Sri Lanka that deals with such type of decision making. © 2022 the Author(s), licensee AIMS Press.
ABSTRACT
In this paper, we construct and develop several second-order parametric duality models for a semiinfinite discrete minmax fractional programming problem based on the next-generation Hanson-Antczak-type generalized second-order invexities. As a matter of fact, the next generation semiinfinite fractional/semiinfinite programming to Mechanical Engineering, Robotics and Robotic Engineering, Health Care and Medical Sciences, and beyond, have a great potential for success against COVID-19 for robust virus-testing and effective medical treatment for all. There is a real urgency for all health scientists and medical doctors, robotic engineers and mechanical engineers to combat the COVID-19 in three stages: Preventive measures (Social distancing, face mask, and lockdown) are not good enough;Anti-body stage for some reasons is not sufficiently clear;and good diet with strict health discipline optimism is moving up. © 2021, International Publications. All rights reserved.
ABSTRACT
In this work, power-series solutions of compartmental epidemiological models are used to provide alternate methods to solve the corresponding systems of nonlinear differential equations. A simple and classical SIR compartmental model is considered to reveal clearly the idea of our approach. Moreover, a SAIRP compartmental model is also analyzed by using the same methodology, previously applied to the COVID-19 pandemic. Numerical experiments are performed to show the accuracy of this approach.
Subject(s)
COVID-19 , Pandemics , Humans , SARS-CoV-2ABSTRACT
The main purpose of this work is to study the dynamics of a fractional-order Covid-19 model. An efficient computational method, which is based on the discretization of the domain and memory principle, is proposed to solve this fractional-order corona model numerically and the stability of the proposed method is also discussed. Efficiency of the proposed method is shown by listing the CPU time. It is shown that this method will work also for long-time behaviour. Numerical results and illustrative graphical simulation are given. The proposed discretization technique involves low computational cost.