PMC:7224658 / 16549-17707
Annnotations
LitCovid-PubTator
Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
---|---|---|---|---|---|
139 | 51-59 | Disease | denotes | COVID-19 | MESH:C000657245 |
141 | 832-840 | Disease | denotes | COVID-19 | MESH:C000657245 |
143 | 764-772 | Disease | denotes | COVID-19 | MESH:C000657245 |
148 | 211-219 | Disease | denotes | COVID-19 | MESH:C000657245 |
149 | 354-362 | Disease | denotes | COVID-19 | MESH:C000657245 |
150 | 572-580 | Disease | denotes | COVID-19 | MESH:C000657245 |
151 | 693-701 | Disease | denotes | COVID-19 | MESH:C000657245 |
LitCovid-PD-MONDO
Id | Subject | Object | Predicate | Lexical cue | mondo_id |
---|---|---|---|---|---|
T51 | 51-59 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T52 | 211-219 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T53 | 354-362 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T54 | 572-580 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T55 | 693-701 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T56 | 764-772 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T57 | 832-840 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
LitCovid-PD-CLO
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
T66 | 278-279 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
T67 | 453-460 | http://purl.obolibrary.org/obo/CLO_0009985 | denotes | focused |
T68 | 528-529 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
T69 | 945-952 | http://purl.obolibrary.org/obo/CLO_0009985 | denotes | focused |
LitCovid-sentences
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
T150 | 0-59 | Sentence | denotes | Types of relationship between regional traffic and COVID-19 |
T151 | 60-250 | Sentence | denotes | In Table 3 the analyses in Table 2, Figure 4, and Figure 5 have been classified into types for each region, based on whether the trends in traffic and COVID-19 were increasing or decreasing. |
T152 | 251-382 | Sentence | denotes | Incheon was categorized as a region requiring strong control (Type 1), with increasing trends for both COVID-19 spread and traffic. |
T153 | 383-587 | Sentence | denotes | Gyeonggi and Seoul were categorized as regions in the early stages of focused control or requiring control (Type 2), with increasing traffic but a relatively stable trend for new confirmed COVID-19 cases. |
T154 | 588-709 | Sentence | denotes | The other regions were categorized as stable (Type 3), with increasing traffic but decreasing trends for COVID-19 spread. |
T155 | 710-789 | Sentence | denotes | Table 3 The level of relationship between traffic and COVID-19 in cities, 2020. |
T156 | 790-817 | Sentence | denotes | Trend in 2020 Specific City |
T157 | 818-840 | Sentence | denotes | Level Traffic COVID-19 |
T158 | 841-887 | Sentence | denotes | 1 + + (Danger) Strong control required Incheon |
T159 | 888-976 | Sentence | denotes | 2 0 (Caution) Control required, or in the early stage of focused control Gyeonggi, Seoul |
T160 | 977-1116 | Sentence | denotes | 3 − (Stable) Under stable control Daegu, Busan, Gwangju, Daejeon, Ulsan, Sejong, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam |
T161 | 1117-1158 | Sentence | denotes | + = increasing; 0 = same; − = decreasing. |