Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning

• Published in: Journal of virological methods Vol. 301; p. 114433
• Main Authors: Chyon, Fuad Ahmed, Suman, Md. Nazmul Hasan, Fahim, Md. Rafiul Islam, Ahmmed, Md. Sazol
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2022
• Subjects: ARIMA model; COVID-19; Data Analysis; Humans; Machine Learning; Models, Statistical; Protocols; SARS-CoV-2; Time Factors; Time Series Analysis; COVID-19; Data Analysis; ARIMA model; Machine Learning; Time Series Analysis
• ISSN: 0166-0934; 1879-0984
• DOI: 10.1016/j.jviromet.2021.114433

•The purpose of this study is to find the future forecast scenario of the confirmed case-patients.•We are using the ARIMA time series model for our prediction as this model has great accuracy in the short time series dataset.•Performing the ARIMA model for time series dataset of confirmed case of COVID-19 patients globally and find the predicted value. The spread of a respiratory syndrome known as Coronavirus Disease 2019 (COVID-19) quickly took on pandemic proportions, affecting over 192 countries. An emergency of the health system was obligated for the response to this epidemic. Although containment measures in China reduced new cases by more than 90 %, the levels of reduction were not the same in other countries. So, the question that arises is: what the world will see this pandemic, and how many patients can be affected? The response would be helpful and supportive of the authority and the community to prepare for the coming days. In this study, the Autoregressive Integrated Moving Average (ARIMA) model was employed to analyze the temporal dynamics of the worldwide spread of COVID-19 in the time window from January 22, 2020 to April 7, 2020. The cumulative number of confirmed Covid-19-affected patients forecasted over the three months was between 9,189,262 – 14,906,483 worldwide. This prediction value of Covid 19-affected patients will be valid only if the situation remains unchanged, and the epidemic spreads according to the previous nature worldwide in these three months.