2021
DOI: 10.48550/arxiv.2108.03670
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#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs

Yichao Zhou,
Jyun-yu Jiang,
Xiusi Chen
et al.

Abstract: COVID-19 has caused lasting damage to almost every domain in public health, society, and economy. To monitor the pandemic trend, existing studies rely on the aggregation of traditional statistical models and epidemic spread theory. In other words, historical statistics of COVID-19, as well as the population mobility data, become the essential knowledge for monitoring the pandemic trend. However, these solutions can barely provide precise prediction and satisfactory explanations on the long-term disease surveil… Show more

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Cited by 1 publication
(1 citation statement)
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References 52 publications
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“…The result shows that the deep learning model surpasses other typical existing machine learning methods for disaster prediction from tweets. Zhou et al (2021) proposed a novel framework called Social Media enhAnced pandemic suRveillance Technique (SMART) to predict COVID-19 confirmed cases and fatalities. The approach consists of two parts; where firstly, heterogeneous knowledge graphs are constructed based on the extracted events.…”
Section: Event Predictionmentioning
confidence: 99%
“…The result shows that the deep learning model surpasses other typical existing machine learning methods for disaster prediction from tweets. Zhou et al (2021) proposed a novel framework called Social Media enhAnced pandemic suRveillance Technique (SMART) to predict COVID-19 confirmed cases and fatalities. The approach consists of two parts; where firstly, heterogeneous knowledge graphs are constructed based on the extracted events.…”
Section: Event Predictionmentioning
confidence: 99%