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. 2020 Sep 27;17(19):7080.
doi: 10.3390/ijerph17197080.

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Abdelrahman E E Eltoukhy  1 , Ibrahim Abdelfadeel Shaban  2 , Felix T S Chan  3 , Mohammad A M Abdel-Aal  1
Affiliations

Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

Abdelrahman E E Eltoukhy et al. Int J Environ Res Public Health. .

Abstract

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

Keywords: COVID-19; data analytics; neural network; pandemic.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Types of documents published on COVID-19 disease (from Scopus Database).
Figure 2
Figure 2
Classifications of COVID-19 publications based on the subject area (Scopus database).
Figure 3
Figure 3
Structure of neural network.
Figure 4
Figure 4
Main effect plot for S/N ratio while using the NARX neural network-based algorithm.
Figure 5
Figure 5
Future prediction of COVID-19 cases in European countries.
Figure 5
Figure 5
Future prediction of COVID-19 cases in European countries.
Figure 6
Figure 6
Future prediction of COVID-19 cases in North and South American countries.
Figure 6
Figure 6
Future prediction of COVID-19 cases in North and South American countries.
Figure 7
Figure 7
Future prediction of COVID-19 cases in Asian countries.
Figure 7
Figure 7
Future prediction of COVID-19 cases in Asian countries.
Figure 8
Figure 8
Future prediction of COVID-19 cases in African countries.
Figure 8
Figure 8
Future prediction of COVID-19 cases in African countries.
See this image and copyright information in PMC

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