> top > docs > PMC:7110798 > spans > 998-1419 > annotations

PMC:7110798 / 998-1419 JSONTXT

Annnotations TAB JSON ListView MergeView

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
18 105-114 Species denotes 2019-nCoV Tax:2697049
19 342-362 Disease denotes coronavirus diseases MESH:D018352
20 364-368 Disease denotes MERS MESH:D018352
21 373-377 Disease denotes SARS MESH:D045169

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T3 373-377 Disease denotes SARS http://purl.obolibrary.org/obo/MONDO_0005091

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 314-316 Chemical denotes SI http://purl.obolibrary.org/obo/CHEBI_90326
T2 418-420 Chemical denotes SI http://purl.obolibrary.org/obo/CHEBI_90326

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T5 213-219 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T6 250-256 http://purl.obolibrary.org/obo/GO_0040007 denotes growth

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T13 0-220 Sentence denotes Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth.
T14 221-421 Sentence denotes With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.