  Mobile phone data have been widely used to model the spread of COVID-19,
however, quantifying and comparing their predictive value across different
settings is challenging. Their quality is affected by various factors and their
relationship with epidemiological indicators varies over time. Here we adopt a
model-free approach based on transfer entropy to quantify the relationship
between mobile phone-derived mobility metrics and COVID-19 cases and deaths in
more than 200 European subnational regions. We found that past knowledge of
mobility does not provide statistically significant information on COVID-19
cases or deaths in most of the regions. In the remaining ones, measures of
contact rates were often more informative than movements in predicting the
spread of the disease, while the most predictive metrics between mid-range and
short-range movements depended on the region considered. We finally identify
geographic and demographic factors, such as users' coverage and commuting
patterns, that can help determine the best metric for predicting disease
incidence in a particular location. Our approach provides epidemiologists and
public health officials with a general framework to evaluate the usefulness of
human mobility data in responding to epidemics.
