Estimating the number of severe COVID-19 cases and COVID-19-related deaths averted by a nationwide vaccination campaign in Republic of Korea

Objectives The Korea Disease Control and Prevention Agency promotes vaccination by regularly providing information on its benefits for reducing the severity of coronavirus disease 2019 (COVID-19). This study aimed to analyze the number of averted severe COVID-19 cases and COVID-19-related deaths by age group and quantify the impact of Republic of Korea’s nationwide vaccination campaign. Methods We analyzed an integrated database from the beginning of the vaccination campaign on February 26, 2021 to October 15, 2022. We estimated the cumulative number of severe cases and COVID-19-related deaths over time by comparing observed and estimated cases among unvaccinated and vaccinated groups using statistical modeling. We compared daily age-adjusted rates of severe cases and deaths in the unvaccinated group to those in the vaccinated group and calculated the susceptible population and proportion of vaccinated people by age. Results There were 23,793 severe cases and 25,441 deaths related to COVID-19. We estimated that 119,579 (95% confidence interval [CI], 118,901–120,257) severe COVID-19 cases and 137,636 (95% CI, 136,909–138,363) COVID-19-related deaths would have occurred if vaccination had not been performed. Therefore, 95,786 (95% CI, 94,659–96,913) severe cases and 112,195 (95% CI, 110,870–113,520) deaths were prevented as a result of the vaccination campaign. Conclusion We found that, if the nationwide COVID-19 vaccination campaign had not been implemented, the number of severe cases and deaths would have been at least 4 times higher. These findings suggest that Republic of Korea’s nationwide vaccination campaign reduced the number of severe cases and COVID-19 deaths

Predicting Infectious Disease Using Deep Learning and Big Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study’s models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society

Predicting Infectious Disease Using Deep Learning and Big Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study’s models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society

PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network

Abstract In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality. The model applies interpolation to air quality and weather data and then uses a Convolutional Neural Network (CNN) to predict PM concentrations. The interpolation transforms the irregular spatial data into an equally spaced grid, which the model requires. This combination creates the interpolated CNN (ICNN) model that we use to predict PM10 and PM2.5 concentrations. The PM10 and PM2.5 evaluation results show an effective prediction performance with an R-squared higher than 0.97 and a root mean square error (RMSE) of approximately 16% of the standard deviation. Furthermore, both PM10 and PM2.5 prediction models forecast high concentrations with high reliability, with a probability of detection higher than 0.90 and a critical success index exceeding 0.85. The proposed ICNN prediction model achieves a high prediction performance using spatio-temporal information and presents a new direction in the prediction field

Fundamental Properties and Clinical Application of 3D-Printed Bioglass Porcelain Fused to Metal Dental Restoration

The purpose of this study is to evaluate the mechanical properties and clinical fitness of 3D-printed bioglass porcelain fused to metal (PFM) dental crowns. To evaluate the mechanical properties, tensile strength, Vickers microhardness, shear bond strength, and surface roughness tests of the SLM printed Co-Cr alloy was conducted. A right mandibular 1st molar tooth was prepared for a single dental crown (n = 10). For a three-unit metal crown and bridge, the right mandibular first premolar and first molar were prepared. Bioglass porcelain was fired to fabricate PFM dental restorations. A clinical gap was observed and measured during each of the four times porcelain was fired. A statistical analysis was conducted. The SLM technique showed the largest statistically significant tensile strength and a 0.2% yield strength value. The milling technique had the lowest statistically significant compressive strength value. The shear bond strength and surface roughness showed no statistically significant difference between the fabricated method. There was a statistically significant change in marginal discrepancy according to the porcelain firing step. The casting technique showed the greatest statistically significant margin discrepancy value. The SLM method showed better fitness than the traditional casting method and showed better mechanical properties as a dental material

Nonpolar Surface Modification Using Fatty Acids and Its Effect on Calcite from Mineral Carbonation of Desulfurized Gypsum

CaCO3 is often used as an additive in many industries. However, additional functions are required to expand its applicability. This entails modification of its physicochemical properties. Accordingly, in this study, a particle surface modification treatment was performed on CaCO3 produced from desulfurized gypsum for a range of industrial applications. In the experiment, fatty acids were used to modify the CaCO3 surface, and the scale of the modification effect was based on the degree of change associated with a polar surface taking on nonpolar surface properties. In the preliminary modification experiment, stearic acid was dissolved in 2-propanol or chloroform, and the extent of the reaction and the active ratio were measured according to the stearic acid concentration. The results showed that the effective active ratio, considering the activity to unit adsorption, was higher in 2-propanol than in chloroform. Consequently, the modification solvent used in the experiment changed the CaCO3 surface from a hydrophilic, polarized form to a hydrophobic, nonpolarized form. These results will also allow the CaCO3 produced to be used as a filler in a range of chemical industries

Leaching of Metal Ions from Blast Furnace Slag by Using Aqua Regia for CO2 Mineralization

Blast furnace slag (BFS) was selected as the source of Ca for CO2 mineralization purposes to store CO2 as CaCO3. BFS was dissolved using aqua regia (AR) for leaching metal ions for CO2 mineralization and rejecting metal ions that were not useful to obtain pure CaCO3 (as confirmed by XRD analysis). The AR concentration, as well as the weight of BFS in an AR solution, was varied. Increasing the AR concentration resulted in increased metal ion leaching efficiencies. An optimum concentration of 20% AR was required for completely leaching Ca and Mg for a chemical reaction with CO2 and for suppressing the leaching of impurities for the production of high-purity carbonate minerals. Increasing the liquid-to-solid ratio (L/S) resulted in the increased leaching of all metal ions. An optimum L/S of 0.3/0.03 (=10) was required for completely leaching alkaline-earth metal ions for CO2 mineralization and for retaining other metal ions in the filtered residue. Moreover, the filtrate obtained using 20% AR and an L/S of 0.3/0.03 was utilized as Ca sources for forming carbonate minerals by CO2 mineralization, affording CaCO3. The results obtained herein demonstrated the feasibility of the use of AR, as well as increasing pH, for the storage of CO2 as high-purity CaCO3