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Abstract
COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea, Japan, Saudi Arabia, and Pakistan. Significant environmental and non-environmental features were taken as the input dataset, and confirmed COVID-19 cases were taken as the output dataset. A correlation analysis was done to identify patterns in the cases related to fluctuations in the associated variables. The results of this study established that the population and air quality index of a region had a statistically significant influence on the cases. However, age and the human development index had a negative influence on the cases. The proposed SSLPNN-based classification model performed well when predicting the classes of confirmed cases. During training, the binary classification model was highly accurate, with a Root Mean Square Error (RMSE) of 0.91. Likewise, the results of the regression analysis using the GPR technique with Matern 5/2 were highly accurate (RMSE = 0.95239) when predicting the number of confirmed COVID-19 cases in an area. However, dynamic management has occupied a core place in studies on the sustainable development of public health but dynamic management depends on proactive strategies based on statistically verified approaches, like Artificial Intelligence (AI). In this study, an SSLPNN model has been trained to fit public health associated data into an appropriate class, allowing GPR to predict the number of confirmed COVID-19 cases in an area based on the given values of selected parameters. Therefore, this tool can help authorities in different ecological settings effectively manage COVID-19.
Highlights
In December 2019, an outbreak of pneumonia of unknown etiology was noticed in Wuhan City, China, which later spread across the globe
The obtained results showed that Shallow SingleLayer Perceptron Neural Network (SSLPNN) could be used to further develop a pattern recognition model based on the selected parameters
The results of our analysis indicate that the SSLPNN algorithm performed excellently, predicting the classes of the number of COVID-19 cases with an accuracy of 99.09% during training and an accuracy of 99.04% during testing, as shown in Tab. 3
Summary
In December 2019, an outbreak of pneumonia of unknown etiology was noticed in Wuhan City, China, which later spread across the globe. In January 2020, the cause of this pneumonia-like disease was confirmed to be a novel coronavirus known as SARS-CoV-2 [1]. This virus belongs to Coronaviridae, a large family of enveloped single-stranded RNA viruses [2]. Coronaviruses are well known to cause a variety of diseases, from the common cold to significant epidemics, like severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) [1–3]. Asymptomatic individuals infected with COVID-19 can spread the disease throughout their communities [5]. Asymptomatic carriers can play a significant role in related viral infections, such as rhinovirus and the influenza virus [6,7]. Molecular testing is the most reliable diagnostic test for COVID-19
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