Covid-19: Time Series Analysis
Er. Gaurisha Gupta
, Dr. Rahul Malhotra , Er. Kamal Kumar
Autocorrelation function (ACF), Partial autocorrelation (PACF), Autoregressive Integrated Moving-average method.
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.
"Covid-19: Time Series Analysis", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 12, page no.122 - 131, December-2020, Available :https://ijsdr.org/papers/IJSDR2012017.pdf
Volume 5
Issue 12,
December-2020
Pages : 122 - 131
Paper Reg. ID: IJSDR_192773
Published Paper Id: IJSDR2012017
Downloads: 000347042
Research Area: Engineering
Country: PANIPAT, Haryana, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
