DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Jae-Gil | - |
dc.contributor.advisor | 이재길 | - |
dc.contributor.author | Kim, Doyoung | - |
dc.date.accessioned | 2023-06-26T19:32:10Z | - |
dc.date.available | 2023-06-26T19:32:10Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=997800&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309656 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 지식서비스공학대학원, 2022.2,[iv, 41 p. :] | - |
dc.description.abstract | Assessing the impact of the COVID-19 crisis on economies is fundamental to tailor the responses of the governments to recover from the crisis. In this paper, we present a novel approach to assessing the economic impact with a large-scale credit card transaction dataset at a fine granularity. For this purpose, we develop a fine-grained economic-epidemiological modeling framework COVID-EENet, which is featured with a two-level deep neural network. In support of the fine-grained EEM, COVIDEENet learns the impact of nearby mass infection cases on the changes of local economies in each district. Through the experiments using the nationwide dataset, given a set of active mass infection cases, COVIDEENet is shown to precisely predict the sales changes in two or four weeks for each district and business category. Therefore, policymakers can be informed of the predictive impact to put in the most effective mitigation measures. Overall, we believe that our work opens a new perspective of using financial data to recover from the economic crisis. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.title | COVID-EENet: predicting fine-grained impact of COVID-19 on local economies | - |
dc.title.alternative | COVID-EENet: 지역 경제에 대한 COVID-19의 세부적 영향 예측 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :지식서비스공학대학원, | - |
dc.contributor.alternativeauthor | 김도영 | - |
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