BACKGROUND: Infodemiology, i.e. information epidemiology, uses Web-based data in order to inform public health and policy. Infodemiology metrics have been widely and successfully employed in order to assess and forecast epidemics and outbreaks. OBJECTIVE: In light of the recent COVID-19 pandemic that started in Wuhan, China, in 2019, in this report online search traffic data from Google are used aiming at tracking the spread of the new Coronavirus. METHODS: Time-series from Google Trends from January to March 2020 on the topic of "Coronavirus" are retrieved and correlated with official data on COVID-19 cases and deaths in the European countries that have been affected the most; Italy (at national and regional level), Spain, France, Germany, and the UK. RESULTS: Statistically significant correlations are observed between the online interest and COVID-19 cases and deaths. Furthermore, a critical point after which the Pearson correlation coefficient starts declining (even if it is still is statistically significant) is identified, indicating that this method is most efficient in regions or countries that have not peaked in COVID-19 cases yet. CONCLUSIONS: In the past, infodemiology metrics in general and data from Google Trends in specific, have been shown to be useful in tracking and forecasting outbreaks, epidemics and pandemics, as, for example, in the cases of MERS, Ebola, measles, and Zika. With the COVID-19 pandemic still at the beginning, it is essential to explore and combine new methods of disease surveillance, in order to assist with the preparedness of the respective health care systems at regional level.
year ⏰ 2020
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