COVID-19 Surveiller: toward a robust and effective pandemic surveillance system based on social media mining

Abstract

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/ . This article is part of the theme issue ‘Data science approachs to infectious disease surveillance’.

A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK

“…Other studies have focused on the US to ascertain case estimations of COVID-19, with similar results to our study in the UK. 13 , 15 , 16 To the best of our knowledge, our study is the first to examine the use of social media data in the UK. Other UK studies have focused on online search behavior 17 or online apps.…”

Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks

“…Social [21] To monitor the COVID-19 disease. This system is based on a hybridization of tweet mining and deep learning techniques.…”

Data science approaches to infectious disease surveillance

“…Social media has been recognized as a major data source for the surveillance of infectious diseases and public health events. Wei Wang and colleagues developed the COVID-19 Surveiller, a web-based COVID-19 surveillance system [ 12 ]. COVID-19 Surveiller features a dynamic graph neural network model to forecast the trends and identify the high-risk events of COVID-19 by analysing streaming Twitter data.…”