COVID-19 Pandemic: Application of Machine Learning Time Series Analysis for Prediction of Human Future

Abstract

Purpose: Coronavirus disease is an irresistible infection caused by the respiratory disease Coronavirus 2 (SARSCoV-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. Prevention is the only cure for this disease. A study was carried out on Coronavirous to observe the number of cases, deaths and recovery cases worldwide within a specific time period of five 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 data set was taken from WHO "Data WHO Coronavirus COVID-19 cases and deaths-WHOCOVID-19-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 Winter 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 2020.

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.

Note: Funding: This research was not funded by any agency.

Conflict of Interest: The authors declare that they have no conflict of interest.

Keywords: COVID-19, SARS-CoV-2, WHO, forecasting techniques, ARIMA