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Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index

机译:使用百度搜索值监测和预测中国Covid-19的确认案例: - 来自百度指数的证据

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BACKGROUND:New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.METHODS:We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.RESULTS:Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: r s =0.705, p=9.623×?10 -?6 ; cough: r s =0.592, p=4.485×?10 -?4 ; fatigue: r s =0.629, p=1.494×?10 -?4 ; sputum production: r s =0.648, p=8.206×?10 -?5 ; shortness of breath: r s =0.656, p=6.182×?10 -5 ). The average search-to-confirmed interval (STCI) was 19.8?days in China. The daily Baidu Index value's optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0?days for fever.CONCLUSION:The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.
机译:背景:2019年新的冠状病毒疾病(Covid-19)对人类生命构成了严重的威胁,并造成了全球大流行。目前的研究旨在探讨搜索引擎查询模式是否可以作为监控Covid-19爆发的潜在工具。方法:我们收集了2020年1月11日至4月22日之间的Covid-19确认案件的数量2020年,来自约翰霍普金斯大学(JHU)的系统科学与工程中心(CSSE)。从百度指数中检索了Covid-19(例如,发烧,咳嗽,疲劳)最常见的症状的搜索指标值。 Spearman的相关性分析用于分析每个Covid-19相关症状的百度指数值与确诊病例数之间的关联。还分析了34个省/地区之间的区域分布。结果:每日确认病例的日常增长和每种Covid-19相关症状的百度指数值呈现出爆发期间的强大阳性相关性(发烧:Rs = 0.705,P = 9.623× ?10 - 咳嗽;咳嗽:Rs = 0.592,P = 4.485×10 - ?4;疲劳:Rs = 0.629,P = 1.494×10 - 2;痰液产生:Rs = 0.648,P = 8.206×? 10 - ?5;呼吸急促:Rs = 0.656,p = 6.182×10 -5)。平均搜索到确认的时间间隔(STCI)是19.8天在中国。每日百度指数价值的最佳时间滞后是咳嗽的4天,疲劳2天,痰生产3天,呼吸急促1天,和发烧0.天的日子。结论:Covid-19相关的日子百度搜索引擎的症状与确认案例的数量显着相关。由于百度搜索引擎可以反映公众对病毒的大流行和区域流行病的关注,因此有关部门需要更多地关注高查康复-19相关症状的地区,采取预防措施,以防止这些可能受感染者进一步传播。

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