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LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 2511-2517 Body_part denotes radius http://purl.org/sig/ont/fma/fma23463

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 1334-1340 Body_part denotes scales http://purl.obolibrary.org/obo/UBERON_0002542
T2 2658-2664 Body_part denotes scales http://purl.obolibrary.org/obo/UBERON_0002542

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 110-118 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 282-290 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 154-155 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T2 242-243 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T3 1041-1043 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T4 1051-1053 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T5 1347-1348 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T6 1566-1567 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T7 2161-2167 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T8 2502-2503 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 110-118 Disease denotes COVID-19 MESH:C000657245
4 282-290 Disease denotes COVID-19 MESH:C000657245
5 322-330 Species denotes patients Tax:9606

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-179 Sentence denotes Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study.
T2 180-191 Sentence denotes Background:
T3 192-355 Sentence denotes On March 17, 2020, French authorities implemented a nationwide lockdown to respond to the COVID-19 epidemic and curb the surge of patients requiring critical care.
T4 356-567 Sentence denotes Assessing the effect of lockdown on individual displacements is essential to quantify achievable mobility reductions and identify the factors driving the changes in social dynamics that affected viral diffusion.
T5 568-812 Sentence denotes We aimed to use mobile phone data to study how mobility in France changed before and during lockdown, breaking down our findings by trip distance, user age and residency, and time of day, and analysing regional data and spatial heterogeneities.
T6 813-821 Sentence denotes Methods:
T7 822-992 Sentence denotes For this population-based study, we used temporally resolved travel flows among 1436 administrative areas of mainland France reconstructed from mobile phone trajectories.
T8 993-1087 Sentence denotes Data were stratified by age class (younger than 18 years, 18-64 years, and 65 years or older).
T9 1088-1251 Sentence denotes We distinguished between residents and non-residents and used population data and regional socioeconomic indicators from the French National Statistical Institute.
T10 1252-1374 Sentence denotes We measured mobility changes before and during lockdown at both local and country scales using a case-crossover framework.
T11 1375-1539 Sentence denotes We analysed all trips combined and trips longer than 100 km (termed long trips), and separated trips by daytime or night-time, weekdays or weekends, and rush hours.
T12 1540-1549 Sentence denotes Findings:
T13 1550-1805 Sentence denotes Lockdown caused a 65% reduction in the countrywide number of displacements (from about 57 million to about 20 million trips per day) and was particularly effective in reducing work-related short-range mobility, especially during rush hour, and long trips.
T14 1806-1957 Sentence denotes Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localised in space.
T15 1958-2125 Sentence denotes During lockdown, mobility drops were unevenly distributed across regions (eg, Île-de-France, the region of Paris, went from 585 000 to 117 000 outgoing trips per day).
T16 2126-2351 Sentence denotes They were strongly associated with active populations, workers employed in sectors highly affected by lockdown, and number of hospitalisations per region, and moderately associated with the socioeconomic level of the regions.
T17 2352-2579 Sentence denotes Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting (95% of traffic leaving Paris was contained in a 201 km radius before lockdown, which was reduced to 29 km during lockdown).
T18 2580-2595 Sentence denotes Interpretation:
T19 2596-2665 Sentence denotes Lockdown was effective in reducing population mobility across scales.
T20 2666-2840 Sentence denotes Caution should be taken in the timing of policy announcements and implementation, because anomalous mobility followed policy announcements, which might act as seeding events.
T21 2841-2945 Sentence denotes Conversely, risk aversion might be beneficial in further decreasing mobility in highly affected regions.
T22 2946-3039 Sentence denotes We also identified socioeconomic and demographic constraints to the efficacy of restrictions.
T23 3040-3256 Sentence denotes The unveiled links between geography, demography, and timing of the response to mobility restrictions might help to design interventions that minimise invasiveness while contributing to the current epidemic response.
T24 3257-3265 Sentence denotes Funding:
T25 3266-3313 Sentence denotes Agence Nationale de la Recherche, EU, REACTing.