• East Asian Economic Review
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• v.28 no.1
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• pp.69-94
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• 2024

The Covid-19 pandemic has caused unprecedented disruptions across industries worldwide. This paper aims to analyze the economic impact of the pandemic on the sales performance of basic commercial areas in Seoul, Korea. Using a regression analysis with credit card transaction data, the study underscores the critical nature of determining the reference point for comparison. Firstly, in comparison to the revenue in the same quarter before the onset of the pandemic, a significant decrease in revenue was observed across most categories during the pandemic periods. Secondly, when compared to the counterfactual revenue in the same period, extrapolated by an exponential smoothing forecasting, the overall revenue decrease during the periods was less pronounced, except in a few categories. Interestingly, certain categories appeared to witness marginal increases in sales after the pandemic. The paper discusses some policy implications of these findings.

• East Asian mathematical journal
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• v.40 no.1
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• pp.119-125
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• 2024

Predicting the number of ICU patients holds significant importance, serving as a critical aspect in efficiently allocating resources, ensuring high-quality care for critically ill individuals, and implementing effective public health strategies to mitigate the impact of diseases. This research focuses on estimating ICU patient numbers through the development of a simple mathematical model. Utilizing data on confirmed COVID-19 cases and deaths, this model becomes a valuable tool for predicting and managing ICU resource requirements during the ongoing pandemic. By incorporating historical data on infected individuals and fatalities from previous weeks, we establish a straightforward equation. We found the substantial impact of the delay in infected individuals, particularly those occurring more than five weeks earlier, on the accuracy of ICU predictions. Proactively preparing for potential surges in severe cases becomes feasible by forecasting the demand for intensive care beds, ultimately improving patient outcomes and preventing excessive strain on medical facilities.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.387-398
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• 2023

The COVID-19 outbreak has significantly impacted human lifestyles and patterns. It was recommended to avoid face-to-face contact and over-crowded indoor places as much as possible as COVID-19 spreads through air, as well as through droplets or aerosols. Therefore, if a person who has contacted a COVID-19 patient or was at the place where the COVID-19 patient occurred is concerned that he/she may have been infected with COVID-19, it can be fully expected that he/she will search for COVID-19 symptoms on Google. In this study, an exploratory data analysis using deep learning models(DNN & LSTM) was conducted to see if we could predict the number of confirmed COVID-19 cases by summoning Google Trends, which played a major role in surveillance and management of influenza, again and combining it with data on the number of confirmed COVID-19 cases. In particular, search term frequency data used in this study are available publicly and do not invade privacy. When the deep neural network model was applied, Seoul (9.6 million) with the largest population in South Korea and Busan (3.4 million) with the second largest population recorded lower error rates when forecasting including search term frequency data. These analysis results demonstrate that search term frequency data plays an important role in cities with a population above a certain size. We also hope that these predictions can be used as evidentiary materials to decide policies, such as the deregulation or implementation of stronger preventive measures.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.116-125
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• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.

• Journal of Preventive Medicine and Public Health
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• v.53 no.6
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• pp.387-396
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• 2020

Objectives: The lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available. Methods: We constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization's COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks. Results: Over the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively. Conclusions: Republic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.59-73
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• 2021

There are many lives lost due traffic accidents, and which have not decreased despite advances in technology. In order to prevent traffic accidents, it is necessary to accurately forecast how they will change in the future. Until now, traffic accident-frequency forecasting has not been a major research field, but has been analyzed microscopically by traditional methods, mainly based on statistics over a previous period of time. Despite the recent introduction of AI to the traffic accident field, the focus is mainly on forecasting traffic flow. This study converts into time series data the records from 1,339,587 traffic accidents that occurred in Korea from 2014 to 2019, and uses the AI algorithm to forecast the frequency of traffic accidents based on driver's age and time of day. In addition, the forecast values and the actual values were compared and verified based on changes in the traffic environment due to COVID-19. In the future, these research results are expected to lead to improvements in policies that prevent traffic accidents.

• Asian Journal of Innovation and Policy
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• v.9 no.2
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• pp.133-144
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• 2020

Korean economy has experienced a very rapid growth largely due to the change of the innovation system since the last half century. The recent outbreak of COVID-19 impacts the global economy as well as Korea's innovation system. In order to understand the influence of the shock to the Korean technological system, we have forecast the future of the system combining qualitative and quantitative techniques such as expert panel, cross impact analysis, and scenario planning. According to the results, we have identified 39 driving forces influencing the change of Korea's technological innovation system. Four scenarios have been suggested based on the predetermined factors and core uncertainties. In other words, uncertainties of emergence of the regions and global value chains generate four scenarios: regional growth, unstable hope, returning to the past, and regional conflicts. The 'regional growth' scenario is regarded as the most preferable, whereas the 'regional conflicts' scenario is unavoidable. In conclusion, we put forward some policy implications to boost the regional innovation system by exploiting the weakened global value chains in order to move on to the most preferable scenario away from the return to the past regime.

• The Journal of Bigdata
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• v.7 no.2
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• pp.195-204
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• 2022

The tourism industry is facing a crisis due to the recent COVID-19 pandemic, and it is vital to improving profitability to overcome it. In situations such as COVID-19, it would be more efficient to sell additional products other than guest rooms to customers who have visited to increase the unit price rather than adopting an aggressive sales strategy to increase room occupancy to increase profits. Previous tourism studies have used machine learning techniques for demand forecasting, but there have been few studies on cross-selling forecasting. Also, in a broader sense, a resort is the same accommodation industry as a hotel. However, there is no study specialized in the resort industry, which is operated based on a membership system and has facilities suitable for lodging and cooking. Therefore, in this study, we propose a cross-selling prediction model using various machine learning techniques with an actual resort company's accommodation data. In addition, by applying the explainable artificial intelligence XAI(eXplainable AI) technique, we intend to interpret what factors affect cross-selling and confirm how they affect cross-selling through empirical analysis.

• Convergence Security Journal
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• v.20 no.3
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• pp.99-108
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• 2020

The COVID-19 pandemic have been spreading continuously across the world, hence it is difficult to expect coming to an end in a short period of time. The use of digital technology has shown tangible results in suppressing COVID-19, but raised privacy and data-protection concerns. In the context of the global efforts to deal with the coronavirus pandemic, various digital technologies are taking on a role in surveillance, monitoring, and forecasting. Also the Korea government manages Corona crisis based on legal basis. In this paper, Korea and each country's response to the Corona 19 pandemic are shown, and suggests ways to protect personal information while using digital tools. Large-scale data collection and processing is essential for the suppression of pandemic, but it should be limited to the level and scope required privacy. Also international cooperation, temporary and transparent use of personal information, the corresponding legal basis are necessary. As the use of data and algorithms is expected to continue to increase, technical and institutional efforts to reinforce privacy protection must continue.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.187-194
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• 2022

LNG is known as the transitional energy source for the future eco-friendly, attracting enormous market attention due to global eco-friendly regulations, Covid-19 Pandemic, Russia-Ukraine War. In addition, since new LNG suppliers such as the U.S. and Australia are also diversifying, the LNG spot market is expected to grow. On the other hand, research on the LNG transportation market has been marginalized. Therefore, this study attempted to predict short-term LNG 160K spot rates and compared the prediction performance between artificial neural networks and the ARIMA model. As a result of this paper, while it was difficult to determine the superiority and superiority of ARIMA and artificial neural networks, considering the relative free of ANN's contraints, we confirmed the feasibility of ANN in LNG 160K spot rate prediction. This study has academic significance as the first attempt to apply an artificial neural network to forecasting LNG 160K spot rates and are expected to contribute significantly in practice in that they can improve the quality of short-term investment decisions by market participants by increasing the accuracy of short-term prediction.