JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic has the distinction of being the first pandemic to occur in the digital age. With the Internet harvesting large amounts of data from the general population in real-time, public databases such as Google Trends and the Baidu Index can be an expedient tool to assist public health efforts.

Objective:

To apply digital epidemiology to the current COVID-19 pandemic to determine utility in the providing adjunctive epidemiologic information on outbreaks of this disease and evaluate this methodology in the case of future pandemics.

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. 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.

Results:

Worldwide search terms for shortness of breath, anosmia, dysgeusia/ageusia, headache, chest pain, and sneezing had strong correlations (r>.6, P<.001) to both new daily confirmed cases and deaths from COVID-19. COVID-19 searches predated RW confirmed cases by 12 days (r=.85±.10, P<.001). Searches for symptoms of diarrhea, fever, shortness of breath, cough, nasal obstruction, and rhinorrhea all had a negative lag of greater than one week compared to new daily cases; whereas, 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.

Conclusions:

This study demonstrates the utility of digital epidemiology in providing helpful surveillance data of disease outbreaks like COVID-19. While 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.