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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
2 16-24 Disease denotes COVID-19 MESH:C000657245
3 25-34 Disease denotes mortality MESH:D003643
6 123-131 Disease denotes COVID-19 MESH:C000657245
7 132-141 Disease denotes mortality MESH:D003643
10 191-199 Disease denotes COVID-19 MESH:C000657245
11 200-209 Disease denotes mortality MESH:D003643
13 264-272 Disease denotes COVID-19 MESH:C000657245
15 395-403 Species denotes patients Tax:9606
17 446-454 Species denotes patients Tax:9606
23 577-587 Species denotes SARS-CoV-2 Tax:2697049
24 520-546 Disease denotes novel coronavirus diseases MESH:C000657245
25 548-556 Disease denotes COVID-19 MESH:C000657245
26 712-718 Disease denotes deaths MESH:D003643
27 830-838 Disease denotes COVID-19 MESH:C000657245
31 904-912 Species denotes patients Tax:9606
32 1171-1180 Disease denotes mortality MESH:D003643
33 1203-1211 Disease denotes COVID-19 MESH:C000657245
41 3212-3220 Species denotes patients Tax:9606
42 3490-3498 Species denotes patients Tax:9606
43 2655-2663 Disease denotes COVID-19 MESH:C000657245
44 2664-2673 Disease denotes mortality MESH:D003643
45 2798-2807 Disease denotes Mortality MESH:D003643
46 2855-2863 Disease denotes COVID-19 MESH:C000657245
47 3368-3376 Disease denotes COVID-19 MESH:C000657245
56 2285-2293 Species denotes patients Tax:9606
57 1754-1763 Disease denotes mortality MESH:D003643
58 1773-1781 Disease denotes COVID-19 MESH:C000657245
59 1845-1854 Disease denotes mortality MESH:D003643
60 1931-1940 Disease denotes mortality MESH:D003643
61 2276-2284 Disease denotes COVID-19 MESH:C000657245
62 2483-2491 Disease denotes COVID-19 MESH:C000657245
63 2492-2501 Disease denotes mortality MESH:D003643
72 3684-3692 Species denotes patients Tax:9606
73 4154-4162 Species denotes patients Tax:9606
74 4587-4595 Species denotes patients Tax:9606
75 4832-4840 Species denotes patients Tax:9606
76 4908-4916 Species denotes patients Tax:9606
77 5073-5081 Species denotes patients Tax:9606
78 3675-3683 Disease denotes COVID-19 MESH:C000657245
79 4899-4907 Disease denotes COVID-19 MESH:C000657245
87 5444-5452 Species denotes patients Tax:9606
88 5829-5837 Species denotes patients Tax:9606
89 6061-6069 Species denotes patients Tax:9606
90 5308-5317 Disease denotes infection MESH:D007239
91 5774-5783 Disease denotes mortality MESH:D003643
92 5820-5828 Disease denotes COVID-19 MESH:C000657245
93 6154-6162 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 6671-6679 Body_part denotes Appendix http://purl.org/sig/ont/fma/fma14542

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 16-24 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 123-131 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T3 191-199 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 264-272 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T5 548-556 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T6 577-585 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T7 830-838 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 1203-1211 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T9 1773-1781 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 2015-2017 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T11 2123-2125 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T12 2189-2191 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T13 2276-2284 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T14 2370-2372 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T15 2483-2491 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T16 2655-2663 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T17 2855-2863 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T18 3094-3096 Disease denotes R2 http://purl.obolibrary.org/obo/MONDO_0019903
T19 3368-3376 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T20 3675-3683 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T21 4899-4907 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T22 5308-5317 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T23 5820-5828 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T24 6154-6162 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 613-616 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T2 1080-1081 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T3 1141-1142 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 1910-1911 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T5 2060-2061 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T6 2073-2075 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T7 2433-2434 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T8 2446-2448 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T9 2792-2793 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T10 2795-2796 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T11 2820-2821 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T12 2848-2849 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T13 3085-3087 http://purl.obolibrary.org/obo/CLO_0050050 denotes S1
T14 3768-3775 http://purl.obolibrary.org/obo/CLO_0009985 denotes focused
T15 3887-3888 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T16 3905-3906 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T17 4024-4026 http://purl.obolibrary.org/obo/CLO_0008922 denotes S2
T18 4024-4026 http://purl.obolibrary.org/obo/CLO_0050052 denotes S2
T19 4233-4236 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T20 4237-4240 http://purl.obolibrary.org/obo/CLO_0050884 denotes ten
T21 4272-4274 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T22 4360-4362 http://purl.obolibrary.org/obo/CLO_0053799 denotes 45
T23 4419-4422 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T24 4992-4995 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T25 4996-4999 http://purl.obolibrary.org/obo/CLO_0050884 denotes ten
T26 5031-5033 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T27 5064-5065 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T28 5706-5707 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T29 6199-6200 http://purl.obolibrary.org/obo/CLO_0001021 denotes B

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 3315-3317 Chemical denotes S3 http://purl.obolibrary.org/obo/CHEBI_29388
T2 4024-4026 Chemical denotes S2 http://purl.obolibrary.org/obo/CHEBI_29387
T3 4085-4087 Chemical denotes S3 http://purl.obolibrary.org/obo/CHEBI_29388

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 1250-1256 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T2 2357-2363 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T3 2720-2726 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T4 2928-2934 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T5 6140-6146 http://purl.obolibrary.org/obo/GO_0040007 denotes growth

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-107 Sentence denotes Wuhan and Hubei COVID-19 mortality analysis reveals the critical role of timely supply of medical resources
T2 109-119 Sentence denotes Highlights
T3 120-187 Sentence denotes • COVID-19 mortality rates in Wuhan and Hubei decay exponentially.
T4 188-260 Sentence denotes • COVID-19 mortality rates outside Hubei and Wuhan are nearly constant.
T5 261-326 Sentence denotes • COVID-19 recovery rates in Wuhan and Hubei grow exponentially.
T6 327-404 Sentence denotes • Over 40,000 aided health workers help Hubei to effectively treat patients.
T7 405-491 Sentence denotes • Newly supplied beds allow over 38,000 patients in Wuhan to be treated in hospitals.
T8 493-509 Sentence denotes Graphic abstract
T9 511-825 Sentence denotes The 2019 novel coronavirus diseases (COVID-19) outbreak caused by SARS-CoV-2 is on-going in China and has hit many countries.1, 2, 3 As of 3 March 2020, there have been 80,270 confirmed cases and 2981 deaths in China, most of which are from the epicenter of the outbreak, Wuhan City, the capital of Hubei Province.
T10 826-876 Sentence denotes New COVID-19 cases have been steadily declining in
T11 877-1277 Sentence denotes China and more than 60,000 patients have been recovered,4 largely due to the effective implementation of comprehensive control measures in China.5 , 6 Here we report that some of these measures, such as a dramatic and timely increase of medical supplies, may play a critical role such that the mortality and recovery rates of COVID-19 in Wuhan follow exponential decay and growth modes, respectively.
T12 1278-1554 Sentence denotes We collected data for analysis on the officially released cumulative numbers of confirmed, dead and recovered cases (from 23 Jan to 3 Mar 2020) in five geographic regions, i.e., mainland China, Hubei Province, outside Hubei (in China), Wuhan City and outside Wuhan (in Hubei).
T13 1555-1914 Sentence denotes As of 3 Mar 2020, crude fatality ratios (CFRs) in the above regions are 0.027±0.006, 0.035±0.007, 0.005±0.002, 0.045±0.012 and 0.021±0.008, respectively, in line with earlier reports.5 , 6 While the mortality rates of COVID-19 outside Hubei and outside Wuhan appear constant over time, the mortality rates in Hubei and Wuhan decline continuously (Fig. 1 (A)).
T14 1915-2227 Sentence denotes Strikingly, the mortality rates in Hubei and Wuhan are well-fitted with the exponential decay mode (R2 being 0.93 and 0.82, respectively; Fig. 1(A) and Table S1), and it is the same forth with that in China (R2 being 0.86) but not with that outside Hubei and outside Wuhan (R2 being 0.39 and 0.32, respectively).
T15 2228-2450 Sentence denotes Remarkably, we found that the recovery rates of COVID-19 patients in the above regions were all well-fitted with the exponential growth mode (R2 being 0.96, 0.95, 0.95, 0.88 and 0.95, respectively; Fig. 1(B) and Table S1).
T16 2451-2635 Sentence denotes Such intriguing pattern for the COVID-19 mortality and recovery rates in Wuhan (or Hubei) somehow contradicts traditional epidemiological models wherein both are assumed as constants.7
T17 2636-3025 Sentence denotes Fig. 1 Fitting the COVID-19 mortality and recovery rates with exponential decay and growth functions, respectively and timely supply of medical resources. (A, B) Mortality rate (panel A) and recovery rate (panel B) for COVID-19 in China over time and by location, with exponential decay- and growth-based regression analyses being performed, respectively (as shown by colored solid lines).
T18 3026-3174 Sentence denotes Parameters from the regression analyses are shown in Table S1, with R2 being shown here. (C) Numbers of the aided health workers in Hubei over time.
T19 3175-3472 Sentence denotes Ratio of the aided health workers to patients was also plotted (note: most of the aided health workers are working in Wuhan; refer to Table S3). (D) Numbers of the remaining confirmed cases of COVID-19, and acute care beds, makeshift beds from Fangcang hospitals and total beds in Wuhan over time.
T20 3473-3516 Sentence denotes Ratio of beds to patients was also plotted.
T21 3517-3623 Sentence denotes Here the data of newly supplied beds in Hubei are not available and thus the data for Wuhan were analyzed.
T22 3624-3759 Sentence denotes The above unique pattern may reflect the fact that COVID-19 patients in Wuhan (or Hubei) have been treated more effectively day by day.
T23 3760-3883 Sentence denotes Here we focused on two components essential for effective treatments, i.e., the supply of health workers and hospital beds.
T24 3884-4089 Sentence denotes As a matter of fact, a great number (up to 42,000, as of 1 March 2020) of health workers have been aided by other provinces in China (Table S2) and they are working in different cities of Hubei (Table S3).
T25 4090-4291 Sentence denotes This extraordinary aid keeps the ratio of the health workers to patients in Hubei at above 0.6 despite the number of remaining confirmed cases has ten-fold increased up to 50,000 on 18 Feb (Fig. 1(C)).
T26 4292-4537 Sentence denotes Results also show that the number of acute care beds from more than 45 designated hospitals plus two newly built ones in Wuhan has been consecutively increasing up to 23,532 (as of Feb 24) under the government-directed re-allocation (Fig. 1(D)).
T27 4538-4853 Sentence denotes This supply thus enabled the severe and critical patients to be treated timely and effectively.6 More importantly, there have been over 10 temporary hospitals (named Fangcang hospitals) reconstructed from gymnasium and exhibition centers, which provide more than 26 000 makeshift beds for mild patients (Fig. 1(D)).
T28 4854-5050 Sentence denotes These combinations guaranteed nearly 100% of COVID-19 patients to be treated in hospitals even if the number of remaining confirmed cases has ten-fold increased up to 38 000 on 18 Feb (Fig. 1(D)).
T29 5051-5227 Sentence denotes In contrast, a lot of patients had to stay at home in the early stage of the outbreak in Wuhan due to the shortage of beds such that many transmissions in households occurred.6
T30 5228-5453 Sentence denotes Accordingly, the effective implementation of comprehensive control measures and infection-treatment practices is critical for combatting any new pathogens, not only interrupting the transmissions but also saving the patients.
T31 5454-5883 Sentence denotes Timely supplied medical resources, including re-allocation of acute care beds, rapid construction of new hospitals and generous aid of health workers by other less-severe areas, apparently help the epicenter of the outbreak Hubei (Wuhan) to accomplish a unique and also encouraging outcome for life-saving such that the mortality and recovery rates of nearly 50,000 COVID-19 patients exponentially decays and grows, respectively.
T32 5884-6463 Sentence denotes Other crucial factors contributing to this success may include the improved and optimized diagnosis and treatment strategies,8 which are critical for saving severe and critical patients.5 , 6 , 9 This speculation appears to be supported by the exponential growth of the COVID-19 recovery rate outside Hubei (Fig. 1(B)) where medical resources are relatively sufficient over time.10 Collectively, the achievement made in Hubei (or Wuhan) may provide useful guidance for many countries to be better prepared for the potential pandemic2 that may overwhelm local health care systems.
T33 6465-6498 Sentence denotes Declaration of Competing Interest
T34 6499-6669 Sentence denotes The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
T35 6671-6704 Sentence denotes Appendix Supplementary materials
T36 6706-6721 Sentence denotes Acknowledgments
T37 6722-6821 Sentence denotes This work is support by the 10.13039/501100001809National Natural Science Foundation of China (Nos.
T38 6822-6851 Sentence denotes 31972918 and 31770830 to XF).
T39 6852-6921 Sentence denotes All authors report no conflicts of interest relevant to this article.
T40 6922-7045 Sentence denotes Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2020.03.018.