PMC:7058650 / 13614-14619
Annnotations
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 103 | 327-336 | Species | denotes | 2019-nCoV | Tax:2697049 |
| 104 | 338-348 | Species | denotes | SARS-COV-2 | Tax:2697049 |
| 105 | 350-359 | Disease | denotes | infection | MESH:D007239 |
| 106 | 543-562 | Disease | denotes | 2019-nCoV infection | MESH:C000657245 |
| 107 | 968-986 | Disease | denotes | 2019-nCoV-infected | MESH:C000657245 |
| 109 | 110-119 | Species | denotes | 2019-nCoV | Tax:2697049 |
LitCovid-PD-FMA-UBERON
| Id | Subject | Object | Predicate | Lexical cue | fma_id |
|---|---|---|---|---|---|
| T1 | 191-194 | Body_part | denotes | map | http://purl.org/sig/ont/fma/fma67847 |
| T2 | 270-273 | Body_part | denotes | map | http://purl.org/sig/ont/fma/fma67847 |
LitCovid-PD-MONDO
| Id | Subject | Object | Predicate | Lexical cue | mondo_id |
|---|---|---|---|---|---|
| T11 | 338-342 | Disease | denotes | SARS | http://purl.obolibrary.org/obo/MONDO_0005091 |
| T12 | 350-359 | Disease | denotes | infection | http://purl.obolibrary.org/obo/MONDO_0005550 |
| T13 | 543-562 | Disease | denotes | 2019-nCoV infection | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T14 | 553-562 | Disease | denotes | infection | http://purl.obolibrary.org/obo/MONDO_0005550 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T61 | 166-167 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | A |
| T62 | 510-511 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T63 | 604-605 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T64 | 689-690 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T65 | 757-760 | http://purl.obolibrary.org/obo/CLO_0051582 | denotes | has |
| T66 | 940-943 | http://purl.obolibrary.org/obo/CLO_0051582 | denotes | has |
| T67 | 966-967 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T136 | 0-91 | Sentence | denotes | We modelled 388 287 passengers travelling to 1297 airports in 168 countries or territories. |
| T137 | 92-165 | Sentence | denotes | The risk index of 2019-nCoV for these countries is presented in Figure 1. |
| T138 | 166-257 | Sentence | denotes | A regularly updated risk map is hosted on PANDORA's website ( https://ncovdata.io/import/). |
| T139 | 258-265 | Sentence | denotes | Fig. 1. |
| T140 | 266-366 | Sentence | denotes | The map with the risk index of countries or territories with 2019-nCoV (SARS-COV-2) infection (0-1). |
| T141 | 367-459 | Sentence | denotes | The darker colour indicates higher risk and light blue colour indicates the absence of data. |
| T142 | 460-563 | Sentence | denotes | In general, China and neighbouring countries have a higher risk of transmission of 2019-nCoV infection. |
| T143 | 564-631 | Sentence | denotes | Africa and South America generally have a low risk of transmission. |
| T144 | 632-775 | Sentence | denotes | Ethiopia, South Africa, Egypt, Mauritius and Brazil have a similar risk of transmission to countries where at least one case has been detected. |
| T145 | 776-1005 | Sentence | denotes | For example, the risk index of 0.1 for Thailand indicates that based on travel patterns observed during 1–31 January 2020 from four major cities of China, Thailand has 10% risk of importing a 2019-nCoV-infected person from China. |