![]() In 2010, Zhou and Shen reported that Baidu search queries and news articles were 10-40 days ahead of official epidemiology for several infectious diseases in China. In a recent systematic review, Mavragani et al identified over 100 peer-reviewed papers studying health-related phenomena using GT data, demonstrating trending in search volumes with time related to the population’s increased use of the internet search engines in seeking information regarding their health. Google Trends (GT) and the Baidu Index (BI) are examples of Big Data surveillance tools that were developed to help researchers analyze temporal and geographical trends in online search terms or topics through the Google and Baidu search engines, respectively. With online search engines harvesting large amounts of data from the general population in real time and providing the information publicly, interest has risen in the potential for public health use of these data during impending outbreaks. His work in the field has led to the concept of information epidemiology, which has been termed infodemiology. In 2003, Eysenbach discussed the use of population health tools and technologies, including the internet, during the 2002-2004 SARS outbreak. The zoonotic infections influenza H5H1 in 1997 and severe acute respiratory syndrome (SARS) in 2002 led to significant interests in using advances in technology and data harvesting to assist in disease prediction, surveillance, and mitigation. The coronavirus disease (COVID-19) is the most recent pandemic to occur in the digital age. JMIR Public Health Surveill 2020 6(2):e19702 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. 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.Ĭonclusions: This study demonstrates the utility of digital epidemiology in providing helpful surveillance data of disease outbreaks like 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). Results: 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. Chronologic patterns were assessed in relation to disease patterns, significant events, and media reports. These data were compared to real-world confirmed cases and deaths of COVID-19. 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. Methods: 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. Objective: 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. 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. ![]()
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