ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound

Abstract

Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.

Weather variability and transmissibility of COVID-19: a time series analysis based on effective reproductive number

“…Two other studies used time series Auto Regressive Integrated Moving Average (ARIMA) model and machine learning as new approaches to assess the association between weather and COVID-19 cases (Malki, Atlam, Ewis, et al, 2020;Malki, Atlam, Hassanien, et al, 2020). However, ARIMA model requires a relatively long time period and stationarity of time series data (Chintalapudi, Battineni, & Amenta, 2020).…”

Association between COVID-19 incidence and outcome and national development levels: An ecologic analysis

“…A study using the ARIMA model predicts that countries with the highest number of COVID-19 cases will appear between December 2020 and April 2021. In addition, the study predicts that if current preventive measures are completely relaxed, a second pandemic rebound could occur within a year [36]. We must consider the current uncertainty of the COVID-19 pandemic, as well as an increasingly interconnected and complex world that requires a flexible and resilient response to future unexpected events and circumstances.…”

Predictive analysis of the number of human brucellosis cases in Xinjiang, China

“…In the fields of prediction, we often encounter non-stationary time series with seasonality, trend and randomness. The SARIMA model is very suitable for predicting and analyzing this kind of time series 28 . The SARIMA model is derived from the autoregressive integrated moving average model (ARIMA) created by Box and Jenkins.…”