[논문]검색어 빈도 데이터를 반영한 코로나 19 확진자수 예측 딥러닝 모델

정성욱

doi:10.3745/ktsde.2023.12.9.387

검색어 빈도 데이터를 반영한 코로나 19 확진자수 예측 딥러닝 모델
Predicting the Number of Confirmed COVID-19 Cases Using Deep Learning Models with Search Term Frequency Data 원문보기

정보처리학회논문지. KIPS transactions on software and data engineering. 소프트웨어 및 데이터 공학, v.12 no.9, 2023년, pp.387 - 398

정성욱 (서울대학교 언론정보연구소, 성균관대학교)

초록
AI-Helper

코로나 19 유행은 인류 생활 방식과 패턴에 큰 영향을 주었다. 코로나 19는 침 방울(비말)은 물론 공기를 통해서도 감염되기 때문에 가능한 대면 접촉을 피하고 많은 사람이 가까이 모이는 장소는 피할 것을 권고하고 있다. 코로나 19 환자와 접촉했거나 코로나 19 환자가 발생한 장소에 있었던 사람이 코로나 19에 감염되었을 것을 염려한다면 구글에서 코로나 19 증상을 찾아볼 것이라고 충분히 예상해 볼 수 있다. 본 연구에서는 과거 독감 감시와 관리에 중요 역할을 했었던 구글 트렌드(Google Trends)를 다시 소환하고 코로나 19 확진자수 데이터와 결합하여 미래의 코로나 19 확진자수를 예측할 수 있을지 딥러닝 모델(DNN & LSTM)을 사용한 탐색적 데이터 분석을 실시하였다. 특히 이 연구에 사용된 검색어 빈도 데이터는 공개적으로 사용할 수 있으며 사생활 침해의 우려도 없다. 심층 신경망 모델(DNN model)이 적용되었을 때 한국에서 가장 많은 인구가 사는 서울(960만 명)과 두 번째로 인구가 많은 부산(340만 명)에서는 검색어 빈도 데이터를 포함하여 예측했을 때 더 낮은 오류율을 기록했다. 이와 같은 분석 결과는 검색어 빈도 데이터가 일정 규모 이상의 인구수를 가진 도시에서 중요한 역할을 할 수 있다는 것을 보여주는 것이다. 우리는 이와 같은 예측이 더 강력한 예방 조치의 실행이나 해제 같은 정책을 결정하는데 근거 자료로 충분히 사용될 수 있을 것으로 믿는다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (10)

그림 Fig. 1. Prediction of the Number of Confirmed Cases 5 Days After January 27 and Time Step
표 Table 1. Input Data Sample
그림 Fig. 2. The Learning Process of the DNN Model Used in This Study
그림 Fig. 3. RNN Model Architecture Used in This Study. Two Features ([x_a, x_b]_n) Come in. Then, They Pass Through One Recurrent Layer and Five Feed Forward Layers. Subsequently, the Number of Confirmed Cases (y_n) is Generated as an Output Value
표 Table 2. Error Rate When Predicting the Number of Confirmed Cases by Applying the DNN Model
그림 Fig. 4. A Graph of the Actual Number of Confirmed Cases(Blue Line), a Graph of the Number of Confirmed Cases Predicted Using the 'Coronics Alone' Set(Gray Line), and a Graph of the Number of Confirmed Cases Predicted Using the 'Searches Addition' Set(Orange Line) in Six Cities in February 2021(with DNN Model).
표 Table 3. Population by Metropolitan City in S. Korea (Unit: ten thousand)
그림 Fig 5. A Graph of the Actual Number of Confirmed Cases(Blue Line), a Graph of the Number of Confirmed Cases Predicted Using the 'Coronics Alone' Set(Gray Line), and a Graph of the Number of Confirmed Cases Predicted Using the 'Searches Addition' Set(Orange Line) in Six Cities in February 2021(with DNN Model)
표 Table 4. Error Rate When Predicting the Number of Confirmed Cases by Applying the LSTM Model
표 Table 5. Error Rate When Predicting the Number of Confirmed Cases by Applying Mathematical Modeling

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표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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