Department of Otolaryngology-Head and Neck Surgery and Communicative Disorders, University of Louisville, Louisville, KY, United States.
Rhinology, Sinus & Skull Base, Kentuckiana Ear Nose Throat, Louisville, KY, United States.
JMIR Public Health Surveill. 2020 May 21;6(2):e19702. doi: 10.2196/19702.
The coronavirus disease (COVID-19) is the latest pandemic of the digital age. With the internet harvesting large amounts of data from the general population in real time, public databases such as Google Trends (GT) and the Baidu Index (BI) can be an expedient tool to assist public health efforts.
The aim of this study is to apply digital epidemiology to the current COVID-19 pandemic to determine the utility of providing adjunctive epidemiologic information on outbreaks of this disease and evaluate this methodology in the case of future pandemics.
An epidemiologic time series analysis of online search trends relating to the COVID-19 pandemic was performed from January 9, 2020, to April 6, 2020. BI was used to obtain online search data for China, while GT was used for worldwide data, the countries of Italy and Spain, and the US states of New York and Washington. These data were compared to real-world confirmed cases and deaths of COVID-19. Chronologic patterns were assessed in relation to disease patterns, significant events, and media reports.
Worldwide search terms for shortness of breath, anosmia, dysgeusia and ageusia, headache, chest pain, and sneezing had strong correlations (r>0.60, P<.001) to both new daily confirmed cases and deaths from COVID-19. GT COVID-19 (search term) and GT coronavirus (virus) searches predated real-world confirmed cases by 12 days (r=0.85, SD 0.10 and r=0.76, SD 0.09, respectively, P<.001). Searches for symptoms of diarrhea, fever, shortness of breath, cough, nasal obstruction, and rhinorrhea all had a negative lag greater than 1 week compared to new daily cases, while searches for anosmia and dysgeusia peaked worldwide and in China with positive lags of 5 days and 6 weeks, respectively, corresponding with widespread media coverage of these symptoms in COVID-19.
This study demonstrates the utility of digital epidemiology in providing helpful surveillance data of disease outbreaks like COVID-19. Although certain online search trends for this disease were influenced by media coverage, many search terms reflected clinical manifestations of the disease and showed strong correlations with real-world cases and deaths.
冠状病毒病（COVID-19）是数字时代的最新大流行疾病。随着互联网实时从普通人群中收集大量数据，谷歌趋势（GT）和百度指数（BI）等公共数据库可以成为辅助公共卫生工作的便捷工具。
本研究旨在将数字流行病学应用于当前的 COVID-19 大流行，以确定提供有关该疾病暴发的辅助流行病学信息的效用，并评估该方法在未来大流行中的应用。
对 2020 年 1 月 9 日至 2020 年 4 月 6 日期间与 COVID-19 大流行相关的在线搜索趋势进行了流行病学时间序列分析。使用 BI 获得中国的在线搜索数据，GT 用于全球数据、意大利和西班牙的数据以及美国纽约州和华盛顿州的数据。将这些数据与 COVID-19 的实际确诊病例和死亡人数进行了比较。评估疾病模式、重大事件和媒体报道与时间模式的关系。
全球范围内搜索呼吸急促、嗅觉丧失、味觉障碍和味觉丧失、头痛、胸痛和打喷嚏的搜索词与 COVID-19 的新日确诊病例和死亡人数有很强的相关性（r>0.60，P<.001）。GT COVID-19（搜索词）和 GT 冠状病毒（病毒）搜索比实际确诊病例提前了 12 天（r=0.85，SD 0.10 和 r=0.76，SD 0.09，P<.001）。搜索腹泻、发烧、呼吸急促、咳嗽、鼻塞和鼻漏等症状的时间滞后均大于 1 周，而全球和中国的嗅觉丧失和味觉障碍搜索均达到峰值，分别为 5 天和 6 周，这与 COVID-19 中这些症状的广泛媒体报道相对应。
本研究表明，数字流行病学在提供 COVID-19 等疾病暴发的有用监测数据方面具有实用性。虽然某些与该疾病相关的在线搜索趋势受到媒体报道的影响，但许多搜索词反映了疾病的临床表现，与实际病例和死亡人数有很强的相关性。
