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LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T22 131-141 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T23 395-405 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T24 714-722 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T25 827-835 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T26 937-945 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T27 1086-1094 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T28 1141-1149 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T20 3-4 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T21 226-227 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T22 269-270 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T23 737-749 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T24 837-842 http://purl.obolibrary.org/obo/CLO_0007373 denotes Lowen
T25 892-904 http://purl.obolibrary.org/obo/OBI_0000968 denotes instrumental

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T28 0-185 Sentence denotes In a linear equation of the current number of new cases on the number of new cases in the past, the unobserved determinants of new infections may be serially correlated for two reasons.
T29 186-292 Sentence denotes First, the number of people infected by a disease usually first increases, reaches a peak, and then drops.
T30 293-455 Sentence denotes Second, there are persistent, unobservable variables, such as clusters that generate large numbers of infections, people’s living habits, and government policies.
T31 456-627 Sentence denotes Serial correlations in errors give rise to correlations between the lagged numbers of cases and the error term, rendering the ordinary least square (OLS) estimator biased.
T32 628-870 Sentence denotes Combining insights in Adda (2016), the existing knowledge of the incubation period of COVID-19 (World Health Organization 2020b), and data on weather conditions that affect the transmission rates of COVID-19 (Lowen and Steel 2014; Wang et al.
T33 871-981 Sentence denotes 2020b), we construct instrumental variables for the number of new COVID-19 cases during the preceding 2 weeks.
T34 982-1219 Sentence denotes Weather characteristics in the previous third and fourth weeks do not directly affect the number of new COVID-19 cases after controlling for the number of new COVID-19 cases and weather conditions in the preceding first and second weeks.
T35 1220-1323 Sentence denotes Therefore, our estimated impacts have causal interpretations and reflect population transmission rates.

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
60 650-654 Gene denotes Adda Gene:118
61 207-213 Species denotes people Tax:9606
62 407-413 Species denotes people Tax:9606
63 131-141 Disease denotes infections MESH:D007239
64 214-222 Disease denotes infected MESH:D007239
65 395-405 Disease denotes infections MESH:D007239
66 479-495 Disease denotes errors give rise MESH:D012030
67 714-722 Disease denotes COVID-19 MESH:C000657245
68 827-835 Disease denotes COVID-19 MESH:C000657245
69 937-945 Disease denotes COVID-19 MESH:C000657245
70 1086-1094 Disease denotes COVID-19 MESH:C000657245
71 1141-1149 Disease denotes COVID-19 MESH:C000657245

2_test

Id Subject Object Predicate Lexical cue
32395017-24789791-64435792 853-857 24789791 denotes 2014