Predictability of COVID-19 Infections Based on Deep Learning and Historical Data

Abstract

The COVID-19 disease has spread worldwide since 2020, causing a high number of deaths as well as infections, and impacting economic, social and health systems. Understanding its dynamics may facilitate a better understanding of its behavior, reducing the impact of similar diseases in the future. Classical modeling techniques have failed in predicting the behavior of this disease, since they have been unable to capture hidden features in the data collected about the disease. The present research benefits from the high capacity of modern computers and new trends in artificial intelligence (AI), specifically three deep learning (DL) neural networks: recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM). We thus modelled daily new infections of COVID-19 in four countries (Saudi Arabia, Egypt, Italy, and India) that vary in their climates, cultures, populations, and health systems. The results show that a simple-structure RNN algorithm is better at predicting daily new infections and that DL techniques have promising potential in disease modeling and can be used efficiently even in the case of limited datasets.

Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia

“…However, those models failed in predicting the probable end date of the disease. The work in [ 41 ] provided performances worst than those obtained in this study although it used sophisticated deep learning techniques shown to be data demanding.…”

The Assessment of Digitalisation Among Malaysian Public Listed Companies in Consumer Product and Services Industries Using Business Process Management in the Pre- and Post-COVID-19 Situations

“…In the present study, the importance of digitalization was discussed and identified as one of the factors that would contribute to a company's success and sustainability. Before the emergence of COVID-19, digitalization was viewed as part of business innovation and most of the firms made voluntary investment towards it (Zreiq et al, 2022;Lu & Wang, 2020). Subsequently, the sudden presence of the COVID-19 is seen as the major contributor for companies to take digitalization transformation in their operations due to massive loss suffered by businesses (WHO, 2020;McKibbin & Fernando, 2020).…”

“…Digitalisation can be defined as "the sociotechnical process of leveraging digitised products or systems to develop new organisational procedures, business models or commercial offerings" (Hendriarto, 2021). The changes of business operation model towards digitalisation will create new opportunities in generating revenue, new customers and ease business activities to cope with the current competitive business environment (Martens & Zscheischler, 2022;Zreiq et al, 2022;Wandaogo, 2022).…”

A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang

“…Particularly, RNN is a sub-class of artificial neural network using hidden variables as a memory to capture temporal dependencies between system and control variables, which is more suitable for handling time series data [ 28 ]. So it is widely used to predict the incidence of various diseases, such as hepatitis [ 29 ], hands-foot-and-mouth disease [ 30 ], COVID-19 [ 31 , 32 ], dengue fever [ 33 ]. For example, Xia et al .…”

“…[ 31 ] constructed RNN model to forecast the counts of newly infected COVID-19 individuals, losses, and cures. In [ 32 ], RNN model was confirms to have a better predicting performance compared with LSTM and GRU models. Vicente et al .…”