Application of machine learning time series analysis for prediction COVID-19 pandemic
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ORIGINAL ARTICLE
Application of machine learning time series analysis for prediction COVID-19 pandemic Vikas Chaurasia 1
&
Saurabh Pal 1
Received: 12 June 2020 / Accepted: 13 October 2020 # Sociedade Brasileira de Engenharia Biomedica 2020
Abstract Purpose Coronavirus disease is an irresistible infection caused by the respiratory disease coronavirus 2 (SARS-CoV-2). It was first found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. During pandemic prevention, this can minimize the impact of the disease on individuals and groups. A study was carried out on coronavirus to observe the number of cases, deaths, and recovery cases worldwide within a specific time period of 5 months. Based on this data, this research paper will predict the future spread of this infectious disease in human society. Methods In our study, the dataset was taken from WHO “Data WHO Coronavirus Covid-19 cases and deaths-WHO-COVID19-global-data”. This dataset contains information about the observation date, provenance/state, country/region, and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt-Winters method and ARIMA, for comparison, and how these methods improve the Root mean square error score. Results The naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-fit model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2021. Conclusion This survey will support the government and experts in making arrangements for what is about to happen. Based on the findings of instantaneous model, these models can be adjusted to guide long time. Keywords COVID-19 . SARS-CoV-2 . WHO . Forecasting techniques . ARIMA
Introduction So far, coronavirus, which has killed millions of people throughout, is constantly taking people under its arrest. Washing hands, covering your face, isolating hygiene, and staying away from the community may be a way to prevent this communicable disease, but it is not enough (Nussbaumer-Streit et al. 2020). As per the World Health Organization (WHO), there are neither immunizations nor explicit antiviral medicines for COVID-19 (Q&A on corona viruses (COVID-19). World Health Organization * Saurabh Pal [email protected] Vikas Chaurasia [email protected] 1
Department of Computer Applications, VBS Purvanchal University, Jaunpur, India
(WHO) 2020). As like Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), coronaviruses are an enormous group of infections which may cause ailment in creatures or people. In people, a few coronaviruses are known to cause respiratory contaminations going from the basic vi
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