@article{fdi:010083292,
  title = {{P}redicting {COVID}-19 incidence in {F}rench hospitals using human contact network analytics},
  author = {{S}elinger, {C}hristian and {C}hoisy, {M}arc and {A}lizon, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {COVID}-19 was first detected in {W}uhan, {C}hina, in 2019 and spread worldwide within a few weeks. {T}he {COVID}-19 epidemic started to gain traction in {F}rance in {M}arch 2020. {S}ubnational hospital admissions and deaths were then recorded daily and served as the main policy indicators. {C}oncurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. {C}ontrarily to individual tracking data, these can be a proxy for human contact networks between subnational administrative units. {M}ethods {M}otivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between {J}uly 2020 and {A}pril 2021. {W}e added human contact network analytics, such as clustering coefficients, contact network strength, null links or curvature, as regressors. {F}indings {W}e found that predictions can be improved substantially (by more than 50% ) at both the national level and the subnational level for up to 2 weeks. {O}ur subnational analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. {T}his original application of network analytics from colocalization data to epidemic spread opens new perspectives for epidemic forecasting and public health.},
  keywords = {time series ; human mobility ; networks ; infectious disease ; {FRANCE}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {I}nfectious {D}iseases},
  volume = {111},
  numero = {},
  pages = {100--107},
  ISSN = {1201-9712},
  year = {2021},
  DOI = {10.1016/j.ijid.2021.08.029},
  URL = {https://www.documentation.ird.fr/hor/fdi:010083292},
}