@article{fdi:010088568,
  title = {{T}he limits of human mobility traces to predict the spread of {COVID}-19 : a transfer entropy approach},
  author = {{D}elussu, {F}. and {T}izzoni, {M}. and {G}auvin, {L}a{\¨e}titia},
  editor = {},
  language = {{ENG}},
  abstract = {{M}obile 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. {T}heir quality is affected by various factors and their relationship with epidemiological indicators varies over time. {H}ere, 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 {E}uropean subnational regions. {U}sing multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on {COVID}-19 spread. {O}ur approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. {A}dditionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. {O}ur work provides epidemiologists and public health officials with a general-not limited to {COVID}-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.},
  keywords = {human mobility ; {COVID}-19 ; mobile phone data ; transfer entropy ; {AUTRICHE} ; {FRANCE} ; {ITALIE} ; {ESPAGNE}},
  booktitle = {},
  journal = {{PNAS} {N}exus},
  volume = {2},
  numero = {10},
  pages = {pgad302 [12 p.]},
  year = {2023},
  DOI = {10.1093/pnasnexus/pgad302},
  URL = {https://www.documentation.ird.fr/hor/fdi:010088568},
}