• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.14-25
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• 2021

With the spread of COVID-19 and the government policy of social distancing, the demand for subways and buses is decreasing, whereas the demand for public bicycles and personal transportation is increasing. Hence, research is needed to understand the characteristics of this phenomenon and to prove the statistical reliability of the correlation between the subway and shared bicycle demands. In this study, the correlation between the number of confirmed COVID-19 cases and the replacement rate of subway and public bicycle demands was examined, but the statistical significance was not significant. However, during the period of September to December 2020, in which the number of confirmed COVID-19 cases in Seoul started to increase rapidly, there was a correlation between the number of confirmed COVID-19 cases and the replacement ratio. If the number of confirmed COVID-19 cases increases by more than a certain number, public bicycles are expected to play a significant role as alternates to the subways. It is expected that the role of public bicycles will increase, and that it is possible to suggest the direction of transportation operation and policy establishment for the continuation of COVID-19 countermeasures in field demonstration after elementary technology development. It is also expected that this study will suggest a direction for future development and policymaking.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.58-63
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• 2022

The daily number of confirmed cases of Coronavirus disease 2019(COVID-19) ranges between 1,000 and 2,000. Despite higher vaccination rates, the number of confirmed cases continues to increase. The Mu variant of COVID-19 reported in some countries by WHO has been identified in Korea. In this study, we predicted the number of confirmed COVID-19 cases in Korea using the SARIMA for the Covid-19 prevention strategy. Trends and seasonality were observed in the data, and the ADF Test and KPSS Test was used accordingly. Order determination of the SARIMA(p,d,q)(P, D, Q, S) model helped in extracting the values of p, d, q, P, D, and Q parameters. After deducing the p and q parameters using ACF and PACF, the data were transformed and schematized into stationary forms through difference, log transformation, and seasonality removal. If seasonality appears, first determine S, then SARIMA P, D, Q, and finally determine ARIMA p, d, q using ACF and PACF for the order excluding seasonality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.469-482
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• 2022

The purpose of this study is to analyze regional variation factors of hypertension treatment rate in COVID-19 based on the analysis results based on ecological methodology. To this end, data suitable for ecological analysis were collected from the Korea Centers for Disease Control and Prevention's regional health statistics, local government COVID-19 confirmed cases, National Health Insurance Corporation, Health Insurance Review and Assessment Service's welfare statistics, and Korea Transport Institute's traffic access index. Descriptive statistics and correlation analysis were conducted using SPSS Statistics 23 for regional variation and related factors in hypertension treatment rate, and geographical weighted regression analysis was conducted using Arc GIS for regional variation factors. As a result of the study, the overall explanatory power of the calculated geo-weighted regression model was 27.6%, distributed from 23.1% to 33.4% by region. As factors affecting the treatment rate of hypertension, the higher the rate of basic living security medical benefits, diabetes treatment rate, and health institutions per 100,000 population, the higher the rate of hypertension treatment, the lower the number of COVID-19 confirmed patients, the lower the rate of physical activity, and the alcohol consumption. Percentage of alcohol consumption decreased due to COVID-19 pandemic. It was analyzed that the lower the ratio, the higher the treatment rate for hypertension. Based on these results, the analysis of regional variables in the treatment rate of hypertension in COVID-19 can be expected to be effective in managing the treatment rate of hypertension, and furthermore, it is expected to be used to establish community-centered health promotion policies.