ARIMA Models for Predicting the End of COVID-19 Pandemic and the Risk of a 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 topics 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?. This research developed a predictive model that can estimate the expected period of time that the virus can possibly stopped and the risk of a second rebound of COVID-19 pandemic. Therefore, this study considered SARIMA model to predict the spread of the virus on several selected countries and is used for pandemic life cycle and end date predictions. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The advantages of this study are that it helps the governments in making decisions and planning now for the future, reduces anxiety and prepares the mentality of people for the next phases of the pandemic. The most striking finding to emerge from this experimental and simulation study is that the proposed algorithm show that the expected COVID-19 infections for the top countries of highest number of confirmed case will slowdown in October, 2020. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time, if the current 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, what are ultimately required are the flexibility, robustness and resilience to cope up the unexpected future events and scenarios.

SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples’ view towards the vaccines

“…7.85 billion and the number of people completed vaccination is 3,289,529,390 [51] . To attain the most accurate forecast, the parameters of the SARIMA model are investigated using a grid search approach [46] , [27] . Finally, the best SARIMA model’s parameters are estimated and used to forecast the total number of vaccination for the next 30 days.…”

“…As Novel Corona Pandemic COVID-19 reports a major warning to the international health systems, cooperation is required by all the countries to combat it. Zohair et al [4] , [27] , [28] , [29] , [30] , [31] , [27] proposed different regressor machine learning models for the COVID-19 Pandemic and it even started the second wave in many countries before a year has passed. Moreover, the models are utilized to estimate the effect of climate on the transmission of COVID-19 by extracting connections between the number of asserted cases and the atmosphere factors on explicit areas.…”

Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model

“…Another study conducted for forecasting different countries COVID-19 trend demonstrated stable condition for China, stationary trend for South Korea while Thailand showed controlled condition 18 . Based on a study, for predicting the end of COVID-19 by using this model expected that top countries COVID-19 infection will slow down by October, 2020 39 . In contrast, in Saudi Arabia and Nigeria this model forecasts highly increased of daily case with cumulative daily cases within one month 4041 .…”

Users opinion and emotion understanding in social media regarding COVID-19 vaccine

“…As Novel Corona Pandemic COVID-19 reports a major warning to the international population health, collaboration is needed by all the nations to combat it [ 24 ]. The media and other web-based media channels should morally introduce the pertinent and right reports to expand inspiration among the overall population as opposed to introducing one-sided data; such coverage may only serve to further divide people and cause fear [ 16 , 31 ].…”