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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.9
- 수록면
- 1 - 14 (14page)
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Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.
목차
Abstract
1. Introduction
2. Materials and Methods
3. Results
4. Discussions
5. Conclusions
References
참고문헌 (34)
참고문헌 신청
Ensheng Dong / 2020 / An interactive web-based dashboard to track COVID-19 in real time / The Lancet Infectious Diseases 20(5):533~534
A. Kapoor / 2020 / Examining covid-19 forecasting using spatio-temporal graph neural networks / arXiv preprint arXiv:2007.03113
I. COVID / 2020 / Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months / MedRxiv
J. Durbin / 2012 / Time series analysis by state space methods / Oxford university press
Zifeng Yang / 2020 / Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions / Journal of Thoracic Disease 12(3):165~174
A. Kapoor / 2020 / Examining covid-19 forecasting using spatio-temporal graph neural networks / arXiv preprint arXiv:2007.03113
I. COVID / 2020 / Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months / MedRxiv
J. Durbin / 2012 / Time series analysis by state space methods / Oxford university press
Zifeng Yang / 2020 / Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions / Journal of Thoracic Disease 12(3):165~174
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