Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul 29:10:922795.
doi: 10.3389/fpubh.2022.922795. eCollection 2022.

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model

Dost Muhammad Khan  1 , Muhammad Ali  1 , Nadeem Iqbal  2   3 , Umair Khalil  1 , Hassan M Aljohani  4 , Amirah Saeed Alharthi  4 , Ahmed Z Afify  5
Affiliations

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model

Dost Muhammad Khan et al. Front Public Health. .

Abstract

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky-Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19.

Keywords: ARIMA; COVID-19; augmented Dicky-Fuller test; ensemble empirical mode decomposition; error trend seasonal model; prediction.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
NNAR model with p autoregressive terms as inputs and one hidden layer with k nodes.
Figure 2
Figure 2
Flowchart of the proposed hybrid EEMD-ETS model. Where “DS” means denoised signal.
Figure 3
Figure 3
Daily confirmed cases: time window from 23 February 2020 to 14 November 2020.
Figure 4
Figure 4
Daily deaths: time window from 23 February 2020 to 14 November 2020.
Figure 5
Figure 5
Actual and predicted 7 days daily confirmed cases and deaths for Italy from COVID-19.
Figure 6
Figure 6
Actual and predicted 7 days daily confirmed cases and daily deaths in France from COVID-19.
Figure 7
Figure 7
Actual and predicted 7 days daily confirmed cases and daily deaths from COVID-19 in Germany.
Figure 8
Figure 8
Actual and predicted 7 days daily confirmed cases and daily deaths from COVID-19 in the UK.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. . Early Transmission Dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med. (2020) 382:1199–207. 10.1056/NEJMoa2001316 - DOI - PMC - PubMed
    1. World Health Organization . Available online at: https://covid19.who.int/table.
    1. Lai D. Monitoring the SARS epidemic in China: a time series analysis. J Data Sci. (2005) 3:279–93. 10.6339/JDS.2005.03(3)0.229 - DOI
    1. Wagenaar BH, Augusto O, Beste J, Toomay SJ, Wickett E, Dunbar N, et al. . The 2014–2015 Ebola virus disease outbreak and primary healthcare delivery in Liberia: time-series analyses for 2010 2016. PLoS Med. (2018) 15:e1002508. 10.1371/journal.pmed.1002508 - DOI - PMC - PubMed
    1. Earnest A, Tan SB, Wilder-Smith A, Machin D. Comparing statistical models to predict dengue fever notifications. Comput Math Methods Med. (2012) 2012:758674. 10.1155/2012/758674 - DOI - PMC - PubMed

Publication types