ABSTRACT
Recent studies regarding COVID-19 show a growing tendency to talk about the COVID-19 Pandemic on online channels. With the recent release of the Pfizer vaccine of COVID-19, people keep posting many rumors regarding the safety concerns of the Vaccine, especially among older people. Due to the rapid spread of the COVID-19 virus and the worldwide Pandemic developed, the rush to develop the COVID-19 Vaccine has become an alarming priority in health care services worldwide. In this research work, we have systematically evaluated people's views towards the COVID-19 Vaccine, and shreds of evidence are supported empirically. The study mainly focuses on the empirical evidence and intensive discussions on what is currently known about the mechanism of action, efficacy, and toxicity of the most promising vaccines (Moderna), (Pfizer/BioNtech), (Astrazenac/Oxford), and (Sputnik V) against COVID-19. Our study's primary objective is to provide an analysis of the questionnaire regarding people's opinions, preferences, and acceptance of the COVID-19 vaccines. We have created an online questionnaire using a google form to collect data from various countries supposed to employ COVID-19 vaccines. The questionnaires were distributed to people in many Arab and foreign countries such as Egypt, Saudi Arabia, India, England, China, and Japan. A total of 516 responses were returned and analyzed using statistical, and Seasonal Autoregressive Integrated Moving Average (SARIMA) approaches. The SARIMA model is used to predict the total number of vaccines in the next few days. To attain the most accurate forecast and prediction, the SARIMA model parameters are investigated with a grid search method. Finally, the combination of the parameters (1, 0, 1) x (1, 0, 0, 1) is considered to be the best SAR-IMA model because it has the lowest AIC values of - 4100.11 and the best Correlation coefficients of 0.984. (C) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ABSTRACT
The world has recently been plagued by the pandemic of Corona Virus Disease 2019 (COVID-19). Since it is reported in Wuhan city of China, on the 8th of December 2019, the COVID-19 invaded every country around the world. As of October 24th, 2020, a total of 42,549,383 confirmed cases of COVID-19 were officially announced and the death toll was 1,150,163. Globally, huge volumes of datasets are generated regarding COVID-19 pandemic to open new research arena for machine learning and artificial intelligence researchers. In this work, an integration of data warehouse with deep learning approach, namely LSTM model, is introduced to predict the spread of the COVID-19 in selected countries. We present the design and development of COVID-warehouse, a data warehouse that integrates and stores the COVID-19 data made available daily by different countries. The basic idea of the framework is to use a COVID19 time-series dataset for analysis by machine learning models to make forecasting of future trend based on present values. Ultimately, the proposed prediction model can be applied to predict for other countries as the nature of the virus is the same everywhere. In terms of R2 metric, the experimental results of the decision tree model outperforms other models for recovery cases compared with confirmed and death cases. Recovery cases have a R2 of 0.996011, death cases have a R2 of 0.993124 and confirmed cases have a R2 of 0.991676. Finally, our results emphasize the importance of enforcing the public health advice of social distancing as well as applying the infection control measures to combat COVID-19 before it becomes too late. © 2005 - ongoing JATIT & LLS