A new data integration framework for Covid-19 social media information

被引:0
作者
Lauren Ansell
Luciana Dalla Valle
机构
[1] University of Plymouth,School of Engineering, Computing and Mathematics
来源
Scientific Reports | / 13卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
The Covid-19 pandemic presents a serious threat to people’s health, resulting in over 250 million confirmed cases and over 5 million deaths globally. To reduce the burden on national health care systems and to mitigate the effects of the outbreak, accurate modelling and forecasting methods for short- and long-term health demand are needed to inform government interventions aiming at curbing the pandemic. Current research on Covid-19 is typically based on a single source of information, specifically on structured historical pandemic data. Other studies are exclusively focused on unstructured online retrieved insights, such as data available from social media. However, the combined use of structured and unstructured information is still uncharted. This paper aims at filling this gap, by leveraging historical and social media information with a novel data integration methodology. The proposed approach is based on vine copulas, which allow us to exploit the dependencies between different sources of information. We apply the methodology to combine structured datasets retrieved from official sources and a big unstructured dataset of information collected from social media. The results show that the combined use of official and online generated information contributes to yield a more accurate assessment of the evolution of the Covid-19 pandemic, compared to the sole use of official data.
引用
收藏
相关论文
共 59 条
[1]  
Li L-Q(2020)Covid-19 patients’ clinical characteristics, discharge rate, and fatality rate of meta-analysis J. Med. Virol. 92 577-583
[2]  
Rahimi I(2021)Analysis and prediction of covid-19 using sir, seiqr and machine learning models: Australia, Italy and UK cases Information 12 109-1894
[3]  
Gandomi AH(2020)Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal BMJ 369 1328-e27
[4]  
Asteris PG(2020)Predictive mathematical models of the covid-19 pandemic: Underlying principles and value of projections JAMA 323 1893-158
[5]  
Chen F(2020)Retrospective analysis of the possibility of predicting the covid-19 outbreak from internet searches and social media data, China, 2020 Eurosurveillance 25 2000199-131
[6]  
Wynants L(2020)Characteristics and outcomes of a sample of patients with covid-19 identified through social media in Wuhan, China: Observational study J. Med. Internet Res. 22 e20108-1300
[7]  
Jewell NP(2020)Exploring urban spatial features of covid-19 transmission in Wuhan based on social media data ISPRS Int. J. Geo Inf. 9 402-90
[8]  
Lewnard JA(2020)Limited early warnings and public attention to coronavirus disease 2019 in China, January–February, 2020: A longitudinal cohort of randomly sampled weibo users Disaster Med. Public Health Prep. 14 e24-110
[9]  
Jewell BL(2020)Prediction of number of cases of 2019 novel coronavirus (covid-19) using social media search index Int. J. Environ. Res. Public Health 17 2365-231
[10]  
Li C(2020)A google–wikipedia–twitter model as a leading indicator of the numbers of coronavirus deaths Intell. Syst. Acc. Financ. Manag. 27 151-780