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The novel coronavirus outbreak was first reported in late December 2019 and more than 7 million people were infected with this disease and over 0.40 million worldwide lost their lives. The first case was diagnosed on 30 January 2020 in India and the figure crossed 0.24 million as of 6 June 2020. This paper presents a detailed study of recently developed forecasting models and predicts the number of confirmed, recovered, and death cases in India caused by COVID-19. The correlation coefficients and multiple linear regression applied for prediction and autocorrelation and autoregression have been used to improve the accuracy. The predicted number of cases shows a good agreement with 0.9992 R-squared score to the actual values. The finding suggests that lockdown and social distancing are two important factors that can help to suppress the increasing spread rate of COVID-19.

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The Internet of things (IoT) connects multiple devices worldwide. It is a growing field in the healthcare system such as health monitoring and tracking, fitness program, and remote medical assistance. With the advent of IoT based technologies in healthcare, it can alleviate the pressure on healthcare systems and can reduce the healthcare cost, and increase the computing and processing speed. Cloud computing was introduced to manage larger and complex healthcare data in the IoT environment. Cloud computing uses centralized cloud data centers. The central server manages the data for all the IoT devices. The integration of IoT with the cloud has some major issues such as latency, bandwidth overuse, real-time response delays, protection, and privacy. So the concept of edge computing and fog computing came into existence to overcome these issues. This paper review the IoT-Fog-based system model architectures, similar paradigm, issues, and difficulties in the area of cloud computing and finally, the performance of some of these proposed systems is assessed using the iFogSim simulator.