ABSTRACT
The pandemic is seriously affecting individuals' wellbeing, occupations, economies, and practices. This pandemic has shaken the world dramatically and framed a moment to think about the future, incorporating our relationship with nature. Since the COVID-19 pandemic started, it's been relied upon to drive remarkable development in telehealth, especially for demonstrative patients, to stay at home and talk with specialists through virtual stations, helping with diminishing the spread of the disease to mass and the clinical staff on the ground zero. The novel coronavirus epidemic has changed our way of living, society, and human services framework. This study proposed the application of artificial intelligence to make its classification. The outcomes of the proposed systems are equated with pre-existing algorithms to highlight the benefits of test time minimization and classification error. Furthermore, this study tries to analyse corona time series data on the level of classification and found that the decision tree algorithm gives the best accuracy of approx. 100% with zero error and zero standard deviation with 7098 milliseconds. © 2022 IEEE.
ABSTRACT
The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting more than 200 countries and territories worldwide. As of September 30, 2020, it has caused a pandemic outbreak with more than 33 million confirmed infections, and more than 1 million reported deaths worldwide. Several statistical, machine learning, and hybrid models have previously been applied to forecast COVID-19 confirmed cases for profoundly affected countries. Future predictions of daily COVID-19 cases are useful for the effective allocation of healthcare resources and will act as an early-warning system for government policymakers. However, due to the presence of extreme uncertainty in these time series datasets, forecasting of COVID-19 confirmed cases has become a very challenging job. For univariate time series forecasting, there are various statistical and machine learning models available in the literature. Still, nowcasting and forecasting of COVID-19 cases are difficult due to insufficient input data, flaw in modeling assumptions, lack of epidemiological features, inadequate past evidence on effects of available interventions, and lack of transparency. This chapter focuses on assessing different short-term forecasting models that are popularly used to forecast the daily COVID-19 cases for various countries. This chapter provides strong empirical evidence that there is no universal method available that can accurately forecast pandemic data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.