ABSTRACT
In this study, an attempt has been made to formulate a SEIR-type mathematical model for the dynamics of SARS-CoV-2 (COVID-19) pandemic by incorporating an additional asymptomatic compartment. The formulated model contains a system of ordinary differential equations. The basic reproduction number R (0) is obtained by applying the next-generation matrix method. For India, daily death cases and cumulative COVID-19 death cases were used to fit the model from March 20, 2020 to May 10, 2020. A tool MATLAB is used for numerical simulation and validation of analytical results. It is found that the coronavirus infection rate depends on the compactness of the population i.e., the infection rate in a particular region either increases or decreases depending on the population density of the region. The infection dynamics of coronavirus are also reduced when masks are worn and social distance is maintained.
ABSTRACT
Predictable and unexpected events have long threatened the continuity and profitability of supply chains, especially multinational ones. Human-made catastrophes and natural tragedies can cause supply chain interruptions. Several events, such as the earthquakes in Gujarat (2001). The COVID-19 epidemic has thrown supply and demand into disarray, and most businesses have yet to devise a strategy for enhancing their resilience and recovery. Industry 4.0 refers to a series of principles, enabling technologies, and methods to make manufacturing systems more evolving, autonomous, adaptable, and precise. The Proposed hypothesis is that the Impact of Industry 4.0 practices positively impacts Supply Chain Resilience, and different Artificial Intelligence techniques are incorporated have a significant effect on Supply chain resilience. The study's findings indicate that industry 4.0 technologies, i.e., artificial intelligence have a substantial importance on the supply chain and its resilience. © The Electrochemical Society
ABSTRACT
In this work an attempt has been made for basic mathematical modeling and analysis of COVID- 19 pandemic based upon the relation between the infection rate and recovery rate of the populations. The case study analysis of four states in INDIA, i.e., Maharashtra, Tamil Nadu, Andhra Pradesh and Uttar Pradesh relevant to the nature of the model has been done and shown in the later part of the manuscript. We assumed that the total active cases of COVID- 19 pandemic, either increases or decreases exponentially. The pre-existing data has been taken from MoHFW, INDIA for four highly infected states of INDIA. After suitably fitting this real data on the proposed model, we analyzed the effect and tried to predict behavior of COVID- 19 pandemic in the abovementioned states in near future with the help of a mathematical tool MATHEMATICA. © 2022 Author(s).