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IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

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Paper Title: Covid-19: Time Series Analysis
Authors Name: Er. Gaurisha Gupta , Dr. Rahul Malhotra , Er. Kamal Kumar
Unique Id: IJSDR2012017
Published In: Volume 5 Issue 12, December-2020
Abstract: Since December end 2019, an outbreak of a novel coronavirus disease (COVID-19; previously known as 2019-nCoV) was reported in Wuhan, China, which has subsequently affected 210 countries worldwide. In general, Coronavirus is an acute resolved disease, but it can be deadly also, with a 6.5% case fatality rate. Various diseases onset might cause death due to massive damage and progressive respiratory failure. As of 26 September 2020, data from the World Health Organization (WHO) have shown that more than 5.9 Million confirmed cases have been identified in 210 countries/regions. On 30 January 2020, the World Health Organization declared that COVID-19 as the sixth public health emergency of international concern. In such a circumstance, Artificial Intelligence and machine learning can assume an immense job in foreseeing a flare-up and limiting or slowing down its spread. In this thesis, our objective is how Artificial Intelligence and Machine Learning can play a enormous role in predicting an outbreak and also minimizing or impede its spread. With machine learning, We used the ARIMA model to the time series data of confirmed COVID-19 cases in India. Autocorrelation function (ACF) graph and partial autocorrelation (PACF) graph is used to find the initial parameters of ARIMA models. These ARIMA models are then tested for variance in normality and stationery through the collection of data. With this model, we try to learn more from the past than from what we think the mechanism [of transmission] is. With mechanism approaches, people try to build models that are based on an understanding of how epidemics spread. Machine learning (Time Series) algorithms to track it, and quickly realized that lending the use of our technology to the global public is the minimum we can do to help during this very difficult time. In this thesis, We also focus on Artificial Intelligence which is good at combing through the collection of data to find connections that make it easier to determine which kinds of treatments could work or which experiments to pursue next. In addition, this thesis will also define how AI also can help in healthcare technologies to detect and fight against coronavirus.
Keywords: Autocorrelation function (ACF), Partial autocorrelation (PACF), Autoregressive Integrated Moving-average method.
Cite Article: "Covid-19: Time Series Analysis", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 12, page no.122 - 131, December-2020, Available :http://www.ijsdr.org/papers/IJSDR2012017.pdf
Downloads: 000336256
Publication Details: Published Paper ID: IJSDR2012017
Registration ID:192773
Published In: Volume 5 Issue 12, December-2020
DOI (Digital Object Identifier):
Page No: 122 - 131
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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