Data_Sheet_2_Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19.DOCX

ObjectivesThe internet data is an essential tool for reflecting public attention to hot issues. This study aimed to use the Baidu Index (BDI) and Sina Micro Index (SMI) to confirm correlation between COVID-19 case data and Chinese online data (public attention). This could verify the effect of online data on early warning of public health events, which will enable us to respond in a more timely and effective manner.
MethodsSpearman correlation was used to check the consistency of BDI and SMI. Time lag cross-correlation analysis of BDI, SMI and six case-related indicators and multiple linear regression prediction were performed to explore the correlation between public concern and the actual epidemic.
ResultsThe public's usage trend of the Baidu search engine and Sina Weibo was consistent during the COVID-19 outbreak. BDI, SMI and COVID-19 indicators had significant advance or lag effects, among which SMI and six indicators all had advance effects while BDI only had advance effects with new confirmed cases and new death cases. But compared with the SMI, the BDI was more closely related to the epidemic severity. Notably, the prediction model constructed by BDI and SMI can well fit new confirmed cases and new death cases.
ConclusionsThe confirmed associations between the public's attention to the outbreak of COVID and the trend of epidemic outbreaks implied valuable insights into effective mechanisms of crisis response. In response to public health emergencies, people can through the information recommendation functions of social media and search engines (such as Weibo hot search and Baidu homepage recommendation) to raise awareness of available disease prevention and treatment, health services, and policy change.

研究目标：互联网数据是反映公众对热点议题关注度的核心工具。本研究借助百度指数（Baidu Index, BDI）与新浪微指数（Sina Micro Index, SMI），旨在验证新冠（COVID-19）疫情病例数据与中国网民线上关注度数据的相关性，以此证实线上数据在公共卫生事件预警中的应用价值，为更及时高效的公共卫生应急响应提供支撑。
研究方法：采用斯皮尔曼相关性分析（Spearman correlation）检验百度指数与新浪微指数的一致性；通过时滞互相关分析（Time lag cross-correlation analysis）对百度指数、新浪微指数与六项疫情相关指标进行关联分析，并开展多元线性回归预测（multiple linear regression prediction），以探究公众关注度与实际疫情态势的内在联系。
研究结果：新冠疫情暴发期间，公众使用百度搜索引擎与新浪微博的行为趋势具有高度一致性。百度指数、新浪微指数与新冠疫情指标间存在显著的提前或滞后关联效应：其中新浪微指数与六项疫情指标均呈现提前预警效应，而百度指数仅在新增确诊病例、新增死亡病例维度上表现出提前效应；但相较于新浪微指数，百度指数与疫情严重程度的相关性更为紧密。值得注意的是，基于百度指数与新浪微指数构建的预测模型可较好拟合新增确诊病例与新增死亡病例的变化趋势。
研究结论：公众对新冠疫情的关注度与疫情暴发态势间存在明确关联，这为公共卫生危机响应的有效机制提供了极具价值的参考视角。针对公共卫生突发事件，可依托社交媒体与搜索引擎的信息推荐功能（如新浪微博热搜、百度首页推荐），提升公众对疾病防治、医疗服务及政策调整相关信息的认知水平。