Modelling incident case numbers
Extending standard models with GTD improved model quality (AIC without GTD: 6041.446; with GTD: 6038.929). To validate results, the same model fitting with data describing mostly the decreasing phase of the epidemic was performed. These findings may confirm the results of the previous modelling; model quality was slightly better with the addition of GTD data (AIC without GTD: 13101.59; with GTD: 13100.27). Detailed results are available as Supplementary material (S1).
Examination of social media use and its relationship to disease incidence is now commonplace. across multiple countries.
Crosscorrelations showed a clear relationship between GTD and reported case incidence across a number of  European countries. The quality of time series modelling, as indicated by AIC values, was also enhanced by the addition of GTD. This suggests that such data could be of real utility in disease modelling and possibly forecasting across country boundaries. This could be of potential utility where traditional disease surveillance is challenging.
Country specific factors, possibly differences in testing and case reporting probably plays a critical role. Reported case numbers may not truly reflect disease occurrence, possibly only how vigorous testing regimes are. This was mitigated by examining increasing and decreasing phases of the epidemic separately; reported case numbers are likely to be more reliable at the beginning of an epidemic when the majority of cases can be identified.
A recent review highlighted the fact that although the number of studies examining the relationship between Internet searching and disease occurrence is growing, few such studies go beyond data description and use such data in disease modelling and forecasting (Mavragani et al., 2018). This is demonstrated by previous studies on conditions related to COVID-19. For example, studies examining GTD and MERS-Cov outbreak (Fung et al., 2013; Shin et al., 2016). Studies examining the correlation between GTD and COVID-19 are rapidly appearing (Effenberger et al., 2020; Husnayain et al., 2020; Walker and Sulyok, 2020) . However, none of these studies has used such data in disease modelling as was performed here.
In conclusion, GTD showed a strong contemporaneous correlation with incident case numbers across Europe. It also enhanced the quality of  disease models using solely case numbers for a range of European countries. This improvement suggests such techniques could be used across country boundaries. This is potentially important as COVID-19 reaches new states, especially ones where testing and surveillance are not as reliable as in Europe.
Conflict of Interest Statement : We have no conflict of interest to declare.
Ethic Statement : The authors confirm that the ethical policies of the journal, as noted on the journal’s author guidelines page, have been adhered to. No ethical approval was required since completely anonymized data were obtained from publicly available sources.
Data Availability Statement: All data and the statistical analyses code are available under the link: https://github.com/msulyok/COVID19GoogleTrendsEurope