COVID-19 prediction using LSTM algorithm: GCC case study.

Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from January 22, 2020 to January 25, 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA.

Effect of mutation and vaccination on spread, severity, and mortality of COVID-19 disease.

Coronavirus disease 2019 (COVID-19) has had different waves within the same country. The spread rate and severity showed different properties within the COVID-19 different waves. The present work aims to compare the spread and the severity of the different waves using the available data of confirmed COVID-19 cases and death cases. Real-data sets collected from the Johns Hopkins University Center for Systems Science were used to perform a comparative study between COVID-19 different waves in 12 countries with the highest total performed tests for severe acute respiratory syndrome coronavirus 2 detection in the world (Italy, Brazil, Japan, Germany, Spain, India, USA, UAE, Poland, Colombia, Turkey, and Switzerland). The total number of confirmed cases and death cases in different waves of COVID-19 were compared to that of the previous one for equivalent periods. The total number of death cases in each wave was presented as a percentage of the total number of confirmed cases for the same periods. In all the selected 12 countries, Wave 2 had a much higher number of confirmed cases than that in Wave 1. However, the death cases increase was not comparable with that of the confirmed cases to the extent that some countries had lower death cases than in Wave 1, UAE, and Spain. The death cases as a percentage of the total number of confirmed cases in Wave 1 were much higher than that in Wave 2. Some countries have had Waves 3 and 4. Waves 3 and 4 have had lower confirmed cases than Wave 2, however, the death cases were variable in different countries. The death cases in Waves 3 and 4 were similar to or higher than Wave 2 in most countries. Wave 2 of COVID-19 had a much higher spread rate but much lower severity resulting in a lower death rate in Wave 2 compared with that of the first wave. Waves 3 and 4 have had lower confirmed cases than Wave 2; that could be due to the presence of appropriate treatment and vaccination. However, that was not reflected in the death cases, which were similar to or higher than Wave 2 in most countries. Further studies are needed to explain these findings.

A study of the possible factors affecting COVID-19 spread, severity and mortality and the effect of social distancing on these factors: Machine learning forecasting model.

BACKGROUNDS: SARS-CoV-2 is affecting different countries all over the world, with significant variation in infection-rate and death-ratio. We have previously shown a presence of a possible relationship between different variables including the Bacillus Calmette-Guérin (BCG) vaccine, average age, gender, and malaria treatment, and the rate of spread, severity and mortality of COVID-19 disease. This paper focuses on developing machine learning models for this relationship. METHODS: We have used real-datasets collected from the Johns Hopkins University Center for Systems Science and Engineering and the European Centre for Disease Prevention and Control to develop a model from China data as the baseline country. From this model, we predicted and forecasted different countries' daily confirmed-cases and daily death-cases and examined if there was any possible effect of the variables mentioned above. RESULTS: The model was trained based on China data as a baseline model for daily confirmed-cases and daily death-cases. This machine learning application succeeded in modelling and forecasting daily confirmed-cases and daily death-cases. The modelling and forecasting of viral spread resulted in four different regions; these regions were dependent on the malarial treatments, BCG vaccination, weather conditions, and average age. However, the lack of social distancing resulted in variation in the effect of these factors, for example, double-humped spread and mortality cases curves and sudden increases in the spread and mortality cases in different countries. The process of machine learning for time-series prediction and forecasting, especially in the pandemic COVID-19 domain, proved usefulness in modelling and forecasting the end status of the virus spreading based on specific regional and health support variables. CONCLUSION: From the experimental results, we confirm that COVID-19 has a very low spread in the African countries with all the four variables (average young age, hot weather, BCG vaccine and malaria treatment); a very high spread in European countries and the USA with no variable (old people, cold weather, no BCG vaccine and no malaria). The effect of the variables could be on the spread or the severity to the extent that the infected subject might not have symptoms or the case is mild and can be missed as a confirmed-case. Social distancing decreases the effect of these factors.

Bacillus Calmette-Guérin vaccine, antimalarial, age and gender relation to COVID-19 spread and mortality.

COVID-19 is affecting different countries all over the world with great variation in infection rate and death ratio. Some reports suggested a relation between the Bacillus Calmette-Guérin (BCG) vaccine and the malaria treatment to the prevention of SARS-CoV-2 infection. Some reports related infant's lower susceptibility to the COVID-19. Some other reports a higher risk in males compared to females in such COVID-19 pandemic. Also, some other reports claimed the possible use of chloroquine and hydroxychloroquine as prophylactic in such a pandemic. The present commentary is to discuss the possible relation between those factors and SARS-CoV-2 infection.