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