Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detec
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Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection Shwet Ketu 1 & Pramod Kumar Mishra 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Virus based epidemic is one of the speedy and widely spread infectious disease which can affect the economy of the country as well as it is life-threatening too. So, there is a need to forecast the epidemic lifespan, which can help us in taking preventive measures and remedial action on time. These preventive measures and corrective action may consist of closing schools, closing malls, closing theaters, sealing of borders, suspension of public services, and suspension of traveling. Resuming such restrictions is depends upon the outbreak momentum and its decay rate. The accurate forecasting of the epidemic lifespan is one of the enormously essential and challenging tasks. It is a challenging task because the lack of knowledge about the novel virus-based diseases and its consequences with complicated societal-governmental factors can influence the widespread of this newly born disease. At this stage, any forecasting can play a vital role, and it will be reliable too. As we know, the novel virus-based diseases are in a growing phase, and we also do not have real-time data samples. Thus, the biggest challenge is to find out the machine learning-based best forecasting model, which could offer better forecasting with the limited training samples. In this paper, the Multi-Task Gaussian Process (MTGP) regression model with enhanced predictions of novel coronavirus (COVID-19) outbreak is proposed. The purpose of the proposed MTGP regression model is to predict the COVID-19 outbreak worldwide. It will help the countries in planning their preventive measures to reduce the overall impact of the speedy and widely spread infectious disease. The result of the proposed model has been compared with the other prediction model to find out its suitability and correctness. In subsequent analysis, the significance of IoT based devices in COVID-19 detection and prevention has been discussed. Keywords IoT . Machine learning . Novel coronavirus (COVID-19) . HealthCare . Virus
1 Introduction In the last two decades, we have seen various epidemics conditions. These conditions were started from SARS in 2002 and followed by SWINE FLU in 2009, after that EBOLA in 2013, then MARS in 2014, and now COVID-19 in 2019 [1–5]. These epidemic encounters can cause severe human and economic losses. If we talk about the latest COVID-19 disease, which came into the picture in late December of 2019 from china. The first case of this virus came from Wuhan city of China, which was new and never seen before. Initially, it was
* Shwet Ketu [email protected] Pramod Kumar Mishra [email protected] 1
Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India
known as the Wuhan virus, and after that, it was coded as COVID-19 or novel coronavirus or 2019-nCov [6]. According to the hypothe
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