Human contact network analytics and COVID-19 hospital incidence in France

<p>This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:</p> <p>Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029</p> <p>See methods in the article for detailed descriptions and the data curation process.</p> <p>&nbsp;</p> <p><strong>1) cov_mob_tst_national.csv contains national-level data</strong><br> &nbsp;</p> <p>The columns comprise:</p> <p>incid_hosp: hospital admission incidence &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence&nbsp;</p> <p>incid_rad: incidence of those returned home</p> <p>within_departement_colocation_X%: X%-quantile of colocation probabilities with d&eacute;partements</p> <p>between_departement_colocation_X%: X%-quantile of colocation probabilities between d&eacute;partements</p> <p>fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in d&eacute;partement</p> <p>null_links_X%: X%-quantile of null links across d&eacute;partements</p> <p>clustering_X%: X%-quantile of clustering coefficients across d&eacute;partements</p> <p>ricci_X%: X%-quantile of curvature across d&eacute;partements</p> <p>ricci_min_X%: X%-quantile of minimum curvature across d&eacute;partements</p> <p>ricci_mean_X%: X%-quantile of average curvature across d&eacute;partements</p> <p>ricci_max_X%: X%-quantile of maximum curvature across d&eacute;partements</p> <p>strength_X%: X%-quantile of network strengths across d&eacute;partements</p> <p>betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across d&eacute;partements</p> <p>positive_test_ratio_weekly: ratio of weekly cumulated positive tested over&nbsp;weekly cumulated tests</p> <p>retail_and_recreation_percent_change_from_baseline: Google Mobility Reports</p> <p>grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports</p> <p>parks_percent_change_from_baseline:&nbsp; Google Mobility Reports &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>transit_stations_percent_change_from_baseline: Google Mobility Reports</p> <p>workplaces_percent_change_from_baseline: Google Mobility Reports</p> <p>residential_percent_change_from_baseline: Google Mobility Reports</p> <p>mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across d&eacute;partements</p> <p>min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across d&eacute;partements</p> <p>max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across d&eacute;partements</p> <p>&nbsp;</p> <p>&nbsp;</p> <p><strong>2) cov_mob_dep.csv contains d&eacute;partement-level data</strong></p> <p>The columns comprise:</p> <p>dep: d&eacute;partement code</p> <p>incid_hosp: hospital admission incidence &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence&nbsp;</p> <p>incid_rad: incidence of those returned home</p> <p>week: week (matched to colocation data recording usually on Tuesdays)</p> <p>dep_name: name of the d&eacute;partement</p> <p>null_links: number of null links</p> <p>betweenness_centrality: betweenness centrality</p> <p>clustering: clustering coefficient</p> <p>strength: network strength</p> <p>ricci_mean: minimum curvature among all edges incident to a d&eacute;partement</p> <p>ricci_min: mean curvature across all edges incident to a d&eacute;partement &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>ricci_X%: X%-quantile curvature among all edges incident to a d&eacute;partement</p> <p>fb_population: number of facebook users &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> <p>facebook_colocation_within_dep: colocation probability within d&eacute;partement</p> <p>fb_population_coverage: ratio of fb_population over census population in d&eacute;partement</p> <p>facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the d&eacute;partement</p> <p>min_temperature: minimum daily temperature averaged over the week</p> <p>max_temperature: maximum daily temperature averaged over the week</p> <p>mean_temperature: mean daily temperature averaged over the week</p> <p>incid_hosp_Y: incidence of hospital admission from Ynd most colocated d&eacute;partement</p> <p>incid_rea_Y: incidence of ICU admission from Ynd most colocated d&eacute;partement</p> <p>incid_dc_Y: incidence of hospital deaths from Ynd most colocated d&eacute;partement</p> <p>incid_rad_Y: incidence of returned home from Ynd most colocated d&eacute;partement</p> <p>&nbsp;</p>