Real-time and Explainable Detection of Epidemics with Global News Data

Sungnyun Kim, Jaewoo Shin, Seongha Eom, Jihwan Oh, Se-Young Yun
Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, PMLR 184:73-90, 2022.

Abstract

Monitoring and detecting epidemics are essential for protecting humanity from extreme harm. However, it must be done in real time for accurate epidemic detection to use limited resources efficiently and save time preventing the spread. Nevertheless, previous studies have focused on predicting the number of confirmed cases after the disease has already spread or when the relevant data are provided. Moreover, it is difficult to give the reason for predictions made using existing methods. In this study, we investigated how to detect and alert infectious diseases that might develop into pandemics soon, even before the information about a specific disease is aggregated. We propose an explainable method to detect an epidemic. This method uses only global news data, which are easily accessible in real time. Hence, we convert the news data to a graph form and cluster the news themes to curate and extract relevant information. The experiments on previous epidemics, including COVID-19, show that our approach allows the explainable real-time prediction of an epidemic disease and guides decision-making for prevention. Code is available at https://github.com/sungnyun/Epidemics-Detection-GKG.

Cite this Paper

BibTeX
@InProceedings{pmlr-v184-kim22a, title = {Real-time and Explainable Detection of Epidemics with Global News Data}, author = {Kim, Sungnyun and Shin, Jaewoo and Eom, Seongha and Oh, Jihwan and Yun, Se-Young}, booktitle = {Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022}, pages = {73--90}, year = {2022}, editor = {Xu, Peng and Zhu, Tingting and Zhu, Pengkai and Clifton, David A. and Belgrave, Danielle and Zhang, Yuanting}, volume = {184}, series = {Proceedings of Machine Learning Research}, month = {22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v184/kim22a/kim22a.pdf}, url = {https://proceedings.mlr.press/v184/kim22a.html}, abstract = {Monitoring and detecting epidemics are essential for protecting humanity from extreme harm. However, it must be done in real time for accurate epidemic detection to use limited resources efficiently and save time preventing the spread. Nevertheless, previous studies have focused on predicting the number of confirmed cases after the disease has already spread or when the relevant data are provided. Moreover, it is difficult to give the reason for predictions made using existing methods. In this study, we investigated how to detect and alert infectious diseases that might develop into pandemics soon, even before the information about a specific disease is aggregated. We propose an explainable method to detect an epidemic. This method uses only global news data, which are easily accessible in real time. Hence, we convert the news data to a graph form and cluster the news themes to curate and extract relevant information. The experiments on previous epidemics, including COVID-19, show that our approach allows the explainable real-time prediction of an epidemic disease and guides decision-making for prevention. Code is available at https://github.com/sungnyun/Epidemics-Detection-GKG.} }
Endnote
%0 Conference Paper %T Real-time and Explainable Detection of Epidemics with Global News Data %A Sungnyun Kim %A Jaewoo Shin %A Seongha Eom %A Jihwan Oh %A Se-Young Yun %B Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022 %C Proceedings of Machine Learning Research %D 2022 %E Peng Xu %E Tingting Zhu %E Pengkai Zhu %E David A. Clifton %E Danielle Belgrave %E Yuanting Zhang %F pmlr-v184-kim22a %I PMLR %P 73--90 %U https://proceedings.mlr.press/v184/kim22a.html %V 184 %X Monitoring and detecting epidemics are essential for protecting humanity from extreme harm. However, it must be done in real time for accurate epidemic detection to use limited resources efficiently and save time preventing the spread. Nevertheless, previous studies have focused on predicting the number of confirmed cases after the disease has already spread or when the relevant data are provided. Moreover, it is difficult to give the reason for predictions made using existing methods. In this study, we investigated how to detect and alert infectious diseases that might develop into pandemics soon, even before the information about a specific disease is aggregated. We propose an explainable method to detect an epidemic. This method uses only global news data, which are easily accessible in real time. Hence, we convert the news data to a graph form and cluster the news themes to curate and extract relevant information. The experiments on previous epidemics, including COVID-19, show that our approach allows the explainable real-time prediction of an epidemic disease and guides decision-making for prevention. Code is available at https://github.com/sungnyun/Epidemics-Detection-GKG.
APA
Kim, S., Shin, J., Eom, S., Oh, J. & Yun, S.. (2022). Real-time and Explainable Detection of Epidemics with Global News Data. Proceedings of the 1st Workshop on Healthcare AI and COVID-19, ICML 2022, in Proceedings of Machine Learning Research 184:73-90 Available from https://proceedings.mlr.press/v184/kim22a.html.

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