Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
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Li Jia Chen | Mingzhi Zhang | Chi Pui Pang | Haoyu Chen | C. Pang | H. Chen | L. J. Chen | Cuilian Li | Xueyu Chen | Mingzhi Zhang | Cuilian Li | Xueyu Chen
[1] Thomas S. Higgins,et al. Correlations of Online Search Engine Trends With Coronavirus Disease (COVID-19) Incidence: Infodemiology Study , 2020, JMIR public health and surveillance.
[2] Jeremy Ginsberg,et al. Detecting influenza epidemics using search engine query data , 2009, Nature.
[3] O. E. Santangelo,et al. Digital epidemiology: assessment of measles infection through Google Trends mechanism in Italy. , 2019, Annali di igiene : medicina preventiva e di comunita.
[4] N. Wilson,et al. Interpreting Google flu trends data for pandemic H1N1 influenza: the New Zealand experience. , 2009, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.
[5] A. Bhagavathula,et al. COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study , 2020, JMIR public health and surveillance.
[6] Xiaoling Yuan,et al. Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model , 2020, medRxiv.
[7] Wagner Meira,et al. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level , 2017, PLoS neglected tropical diseases.
[8] Mauricio Santillana,et al. Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak , 2016, JMIR public health and surveillance.
[9] G. Leung,et al. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.
[10] Xiaoling Yuan,et al. Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model , 2020, Exploratory research and hypothesis in medicine.
[11] 中国疾病预防控制中心新型冠状病毒肺炎应急响应机制流行病学组. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China./ 新型冠状病毒肺炎流行病学特征分析 , 2020 .
[12] T. Mackey,et al. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study , 2020, JMIR public health and surveillance.
[13] Novel Coronavirus Pneumonia Emergency Response Epidemiol Team. [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. , 2020, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.
[14] Jisun An,et al. High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea , 2016, Scientific Reports.