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LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 452-456 Body_part denotes cell http://purl.org/sig/ont/fma/fma68646
T2 491-494 Body_part denotes map http://purl.org/sig/ont/fma/fma67847

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 27-35 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 81-89 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T3 372-380 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T4 1009-1019 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T5 1164-1173 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T6 1580-1588 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 120-125 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T2 234-239 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T3 262-263 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 452-456 http://purl.obolibrary.org/obo/GO_0005623 denotes cell
T5 774-775 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T6 936-937 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T7 989-990 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 892-900 http://purl.obolibrary.org/obo/GO_0007610 denotes behavior

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 27-35 Disease denotes COVID-19 MESH:C000657245
10 81-89 Disease denotes COVID-19 MESH:C000657245
11 120-125 Species denotes human Tax:9606
12 372-382 Species denotes SARS-CoV-2 Tax:2697049
13 530-536 Species denotes people Tax:9606
14 1009-1019 Disease denotes infections MESH:D007239
15 1164-1173 Disease denotes infection MESH:D007239
16 1449-1457 Disease denotes infected MESH:D007239
17 1580-1588 Disease denotes COVID-19 MESH:C000657245

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-76 Sentence denotes Mobility network models of COVID-19 explain inequities and inform reopening.
T2 77-248 Sentence denotes The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread1.
T3 249-438 Sentence denotes We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas.
T4 439-729 Sentence denotes Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges.
T5 730-911 Sentence denotes We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time.
T6 912-1122 Sentence denotes Our model predicts that a small minority of "superspreader" POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility.
T7 1123-1428 Sentence denotes Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2-8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk.
T8 1429-1589 Sentence denotes By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.