PubMed:32520290
Annnotations
LitCovid-PD-MONDO
| Id | Subject | Object | Predicate | Lexical cue | mondo_id |
|---|---|---|---|---|---|
| T1 | 20-28 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T2 | 120-128 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T3 | 220-228 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T4 | 338-346 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T5 | 1317-1325 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T6 | 1444-1452 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T1 | 880-882 | http://purl.obolibrary.org/obo/CLO_0007860 | denotes | MR |
| T2 | 915-916 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | A |
| T3 | 950-953 | http://purl.obolibrary.org/obo/CLO_0051582 | denotes | has |
| T4 | 1081-1082 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T5 | 1129-1131 | http://purl.obolibrary.org/obo/CLO_0007860 | denotes | MR |
| T6 | 1162-1163 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
LitCovid-PD-CHEBI
| Id | Subject | Object | Predicate | Lexical cue | chebi_id |
|---|---|---|---|---|---|
| T1 | 880-882 | Chemical | denotes | MR | http://purl.obolibrary.org/obo/CHEBI_74698 |
| T2 | 1129-1131 | Chemical | denotes | MR | http://purl.obolibrary.org/obo/CHEBI_74698 |
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 1 | 20-28 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 6 | 220-228 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 7 | 338-346 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 8 | 1317-1325 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 9 | 1444-1452 | Disease | denotes | COVID-19 | MESH:C000657245 |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T1 | 0-89 | Sentence | denotes | Spatial Analysis of COVID-19 cases and intensive care beds in the State of Ceará, Brazil. |
| T2 | 90-186 | Sentence | denotes | Análise Espacial dos Casos de COVID-19 e leitos de terapia intensiva no estado do Ceará, Brasil. |
| T3 | 187-297 | Sentence | denotes | The geographical distribution of COVID-19 through Geographic Information Systems resources is hardly explored. |
| T4 | 298-421 | Sentence | denotes | We aimed to analyze the distribution of COVID-19 cases and the exclusive intensive care beds in the state of Ceará, Brazil. |
| T5 | 422-539 | Sentence | denotes | This is an ecological study with the geographic distribution of the case detection coefficient in 184 municipalities. |
| T6 | 540-684 | Sentence | denotes | Maps of crude and estimated values (global and local Bayesian method) were developed, calculating the Moran index and using BoxMap and MoranMap. |
| T7 | 685-750 | Sentence | denotes | Intensive care beds were distributed through geolocalized points. |
| T8 | 751-799 | Sentence | denotes | In total, 3,000 cases and 459 beds were studied. |
| T9 | 800-914 | Sentence | denotes | The highest rates were found in the capital Fortaleza, the Metropolitan Region (MR), and the south of this region. |
| T10 | 915-1008 | Sentence | denotes | A positive spatial autocorrelation has been identified in the local Bayesian rate (I = 0.66). |
| T11 | 1009-1152 | Sentence | denotes | The distribution of beds superimposed on the BoxMap shows clusters with a High-High pattern of number of beds (capital, MR, northwestern part). |
| T12 | 1153-1248 | Sentence | denotes | However, a similar pattern is found in the far east or transition areas with insufficient beds. |
| T13 | 1249-1316 | Sentence | denotes | The MoranMap shows clusters statistically significant in the state. |
| T14 | 1317-1487 | Sentence | denotes | COVID-19 interiorization in Ceará requires contingency measures geared to the distribution of specific intensive care beds for COVID-19 cases in order to meet the demand. |
ENG_RE_CONSENSUS
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T0 | 220-228 | DOID_0080600 | denotes | COVID-19 |
| T1 | 338-346 | DOID_0080600 | denotes | COVID-19 |
| T4 | 1317-1325 | DOID_0080600 | denotes | COVID-19 |
| T5 | 1444-1452 | DOID_0080600 | denotes | COVID-19 |
ENG_NER_NEL_CONSENSUS
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T12 | 700-704 | MESH:D001513 | denotes | beds |
| T13 | 781-785 | MESH:D001513 | denotes | beds |
| T18 | 1029-1033 | MESH:D001513 | denotes | beds |
| T2 | 220-228 | DOID:0080600 | denotes | COVID-19 |
| T21 | 1114-1118 | MESH:D001513 | denotes | beds |
| T23 | 1196-1204 | MESH:D005202 | denotes | far east |
| T26 | 1243-1247 | MESH:D001513 | denotes | beds |
| T28 | 1317-1325 | DOID:0080600 | denotes | COVID-19 |
| T31 | 1435-1439 | MESH:D001513 | denotes | beds |
| T32 | 1444-1452 | DOID:0080600 | denotes | COVID-19 |
| T33 | 685-699 | MESH:D003422 | denotes | Intensive care |
| T34 | 220-228 | MESH:D000086382 | denotes | COVID-19 |
| T35 | 338-346 | MESH:D000086382 | denotes | COVID-19 |
| T36 | 1317-1325 | MESH:D000086382 | denotes | COVID-19 |
| T37 | 1444-1452 | MESH:D000086382 | denotes | COVID-19 |
| T38 | 371-385 | MESH:D003422 | denotes | intensive care |
| T4 | 237-267 | MESH:D040362 | denotes | Geographic Information Systems |
| T7 | 338-346 | DOID:0080600 | denotes | COVID-19 |
| T9 | 386-390 | MESH:D001513 | denotes | beds |