PMC:7210464 / 19158-19702
Annnotations
LitCovid-PD-MONDO
| Id | Subject | Object | Predicate | Lexical cue | mondo_id |
|---|---|---|---|---|---|
| T61 | 77-85 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T62 | 184-192 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T63 | 413-421 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T86 | 267-268 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T87 | 494-495 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T125 | 0-152 | Sentence | denotes | Regarding the explanatory variables, we calculate the number of new cases of COVID-19 in the preceding first and second weeks for each city on each day. |
| T126 | 153-544 | Sentence | denotes | To estimate the impacts of new COVID-19 cases in other cities, we first calculate the geographic distance between a city and all other cities using the latitudes and longitudes of the centroids of each city and then calculate the weighted sum of the number of COVID-19 new cases in all other cities using the inverse of log distance between a city and each of the other cities as the weight. |
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 163 | 77-85 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 164 | 184-192 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 165 | 413-421 | Disease | denotes | COVID-19 | MESH:C000657245 |