Open Access

ARTICLE

Analysis and Forecasting COVID-19 Outbreak in Pakistan Using Decomposition and Ensemble Model

Xiaoli Qiang¹, Muhammad Aamir^2,*, Muhammad Naeem², Shaukat Ali³, Adnan Aslam⁴, Zehui Shao¹
1 Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
2 Department of Statistics, Abdul Wali Khan University, Mardan, 23200, Pakistan
3 Department of English, University of Malakand, Chakdara, 188800, Pakistan
4 Department of Natural Sciences and Humanities, University of Engineering and Technology, Lahore, 54000, Pakistan
* Corresponding Author: Muhammad Aamir. Email:
(This article belongs to this Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)

Computers, Materials & Continua 2021, 68(1), 841-856. https://doi.org/10.32604/cmc.2021.012540

Received 30 August 2020; Accepted 16 January 2021; Issue published 22 March 2021

Abstract

COVID-19 has caused severe health complications and produced a substantial adverse economic impact around the world. Forecasting the trend of COVID-19 infections could help in executing policies to effectively reduce the number of new cases. In this study, we apply the decomposition and ensemble model to forecast COVID-19 confirmed cases, deaths, and recoveries in Pakistan for the upcoming month until the end of July. For the decomposition of data, the Ensemble Empirical Mode Decomposition (EEMD) technique is applied. EEMD decomposes the data into small components, called Intrinsic Mode Functions (IMFs). For individual IMFs modelling, we use the Autoregressive Integrated Moving Average (ARIMA) model. The data used in this study is obtained from the official website of Pakistan that is publicly available and designated for COVID-19 outbreak with daily updates. Our analyses reveal that the number of recoveries, new cases, and deaths are increasing in Pakistan exponentially. Based on the selected EEMD-ARIMA model, the new confirmed cases are expected to rise from 213,470 to 311,454 by 31 July 2020, which is an increase of almost 1.46 times with a 95% prediction interval of 246,529 to 376,379. The 95% prediction interval for recovery is 162,414 to 224,579, with an increase of almost two times in total from 100802 to 193495 by 31 July 2020. On the other hand, the deaths are expected to increase from 4395 to 6751, which is almost 1.54 times, with a 95% prediction interval of 5617 to 7885. Thus, the COVID-19 forecasting results of Pakistan are alarming for the next month until 31 July 2020. They also confirm that the EEMD-ARIMA model is useful for the short-term forecasting of COVID-19, and that it is capable of keeping track of the real COVID-19 data in nearly all scenarios. The decomposition and ensemble strategy can be useful to help decision-makers in developing short-term strategies about the current number of disease occurrences until an appropriate vaccine is developed.

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Keywords

Analysis; ARIMA; COVID-19; decomposition and ensemble model; forecasting

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Citations