Introduction
The current Coronavirus (COVID-19) pandemic has become an Internet phenomenon, leading newsfeeds and trending on news forums globally. Understandably, there is widespread public interest, which is being met by blanket media coverage of an unprecedented nature. The Internet is now the favoured first port of call for those seeking healthcare information (Diaz et al., 2002; Andreassen et al., 2007). Therefore, such digital information is likely to be playing a key role in public communication during the current crisis.
Data generated though such Internet searching has long been known to be useful for disease monitoring and surveillance (Brownstein et al., 2009; Eysenbach, 2011; Anema et al., 2014; Mavragani et al., 2018). Resources, such as Google Trends, which provide data on the volumes of Internet searching upon specific topics, have been identified as being potentially useful sources of real time data (Carneiro and Mylonakis, 2009; Nuti et al., 2014). Such data sources may possibly reflect disease occurrence quicker and more accurately than traditional, but slower, disease monitoring through official channels. Studies examining the relationship between Internet searching and disease occurrence have become commonplace (Carneiro and Mylonakis, 2009; Mavragani et al., 2018). However, as recently highlighted, although many studies describe relationships and seek correlations, few studies use such data to its full potential utilising it in disease forecasting and modelling (Mavragani et al., 2018). Additionally, whether relationships between Internet search data and disease occurrence occur across national boundaries is rarely examined; typically such studies examine such relationships within only a single national country.
Thus, here the aim was not only to examine whether such correlations between Google Trends data and COVID-19 cases occurred, but also to utilise such data in modelling; could such data enhance traditionally based models using reported case numbers? Additionally, were such relationships apparent, and model enhancement occur, across a wider geographical range than a single nation? Coronavirus is a pan-European problem, with epidemics developing almost simultaneously across many countries. This situation provides a unique opportunity to examine whether such data can enhance modelling across multiple countries, continent wide.