ABSTRACT
To better understand data and its possible consequences, mathematical models are the must. For COVID-19 outbreak, it helps predict and therefore, policies are guidelines can be designed accordingly. In this study, we define the practical prediction model for COVID 19 by considering the different essentials such as the total number of cases, recovery cases and death cases. The special grading for countries involves the government policies as well as the involvement of the society intended for controlling COVID 19. We investigate trend lines for the data with the help of correlation coefficients and coefficient of determination. The linear and the second-degree equations help to make predictions of active patients of COVID 19 in the future. The study of existing data patterns is done and is used to predict the spread of COVID in the world. This analysis assists us to decide the futuristic guidelines, requirements, and policies for governing the spread of COVID 19.
ABSTRACT
The epidemic of coronavirus disease 2019 (COVID-19) has created a public health problem that deeply impacted our environment and our daily lives. A global epidemic is a worst-case scenario in the world of infectious diseases. The new coronavirus, called COVID-19, is a zoonotic disease originating in China’s Wuhan Province and spreading like wildfires killing people and destroying the global economy. This viral strain continues to be managed by gigantic effort. This has motivated us to analyse the impact and provide solutions. A detailed review of the literature is done on various sectors of society and COVID-19 pandemic impact, providing various solutions using big data and AI, measures to be taken over the impact caused by COVID-19. We found several useful measures over various sectors of society that help prevent and manage COVID-19 epidemic. The available technologies such as artificial intelligence and big data could also help detect and diagnose COVID-19 and other related problems and symptoms. This article, though COVID-19 is still ravaging nations worldwide, is an attempt to summarise the effects of COVID-19 and inspire intellectuals to consider how quickly a nanometer can almost bring down global superpowers. This article is providing various solutions using big data and AI, measures to be taken over the impact caused by COVID-19. © 2022, Springer Nature Switzerland AG.
ABSTRACT
Rough set theory is a new mathematical or set-theoretical practice to study inadequate knowledge. There are many use cases in the real world where there is a lack of crisp knowledge. In view of this, many Scientists have been attempted to address anomalies associated with imperfect knowledge for a long time. In recent times, computer and mathematics researchers have been trying to resolve this decisive issue, mainly in artificial intelligence province. The COVID-19 pandemic encroaches the harmony of the whole world. Many patients of COVID-19 have different symptoms, so it is very difficult to carry out the symptoms-based prediction COVID-19. However, the rough set theory approach help to minimize the number of attributes from the underlined decision table. This work defines the decision table having patients and symptoms of the COVID-19 in the rows and columns respectively. By studying data indiscernibility, elementary sets are specified for each attribute. Moreover, lower approximation, upper approximation, class of rough sets and accuracy of approximation are defined for different individual or group symptoms. This proposed work investigates whether particular symptoms belong to the decision set or not and also the accuracy of observations is calculated and analyzed. The probability of having COVID-19 is defined by considering the different sets of attributes. The main objective of this work is to minimize the number of symptoms of COVID-19 by rough set theory approach for better decision making. This symptoms-based prediction could help us while checking patients and decision-makers could be benefited while making policies and guidelines. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.