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LitCovid-PMC-OGER-BB

Id Subject Object Predicate Lexical cue
T56 4305-4313 SP_7 denotes COVID-19
T55 4039-4047 SP_7 denotes COVID-19
T54 4123-4134 NCBITaxon:11118 denotes coronavirus
T53 3309-3320 NCBITaxon:11118 denotes coronavirus
T52 3395-3403 SP_7 denotes COVID-19
T51 13992-14000 SP_7 denotes COVID-19
T50 8625-8636 NCBITaxon:11118 denotes coronavirus
T49 8759-8767 SP_7 denotes COVID-19
T48 8951-8959 SP_7 denotes COVID-19
T47 9109-9117 SP_7 denotes COVID-19
T46 9661-9673 GO:0065007 denotes surveillance
T45 9844-9855 UBERON:0001004 denotes respiratory
T44 10039-10046 GO:0065007 denotes control
T43 10140-10151 UBERON:0001004 denotes respiratory
T42 10308-10316 SP_7 denotes COVID-19
T41 10405-10413 SP_7 denotes COVID-19
T40 10524-10532 SP_7 denotes COVID-19
T39 10613-10618 GO:0007612 denotes learn
T38 10629-10634 NCBITaxon:10239 denotes virus
T36 11034-11045 NCBITaxon:11118 denotes coronavirus
T35 11900-11908 SP_7 denotes COVID-19
T34 12048-12059 NCBITaxon:11118 denotes coronavirus
T33 12139-12150 NCBITaxon:11118 denotes coronavirus
T32 12206-12217 NCBITaxon:11118 denotes coronavirus
T31 12246-12254 SP_7 denotes COVID-19
T30 12405-12416 NCBITaxon:11118 denotes coronavirus
T29 12478-12486 SP_7 denotes COVID-19
T28 12548-12559 NCBITaxon:11118 denotes coronavirus
T27 12580-12591 NCBITaxon:11118 denotes coronavirus
T26 12844-12855 NCBITaxon:11118 denotes coronavirus
T25 13149-13160 NCBITaxon:11118 denotes coronavirus
T24 13179-13184 NCBITaxon:10239 denotes viral
T23 3690-3698 SP_7 denotes COVID-19
T22 7688-7699 NCBITaxon:11118 denotes coronavirus
T21 8191-8202 NCBITaxon:11118 denotes coronavirus
T20 952-960 SP_7 denotes COVID-19
T19 1105-1113 SP_7 denotes COVID-19
T18 1170-1180 SP_7 denotes SARS-CoV-2
T17 1290-1298 SP_7 denotes COVID-19
T16 1387-1398 UBERON:0001004 denotes respiratory
T15 1467-1472 CL:0000738 denotes white
T14 1473-1478 CL:0000738;UBERON:0000178 denotes blood
T13 1479-1484 CL:0000738 denotes cells
T12 2135-2146 NCBITaxon:11118 denotes coronavirus
T11 2438-2449 NCBITaxon:11118 denotes coronavirus
T10 2564-2575 NCBITaxon:11118 denotes coronavirus
T9 2893-2904 NCBITaxon:11118 denotes coronavirus
T8 3060-3068 SP_7 denotes COVID-19
T7 3133-3141 SP_7 denotes COVID-19
T4 764-772 SP_7 denotes COVID-19
T37 10714-10722 SP_7 denotes COVID-19

LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 1467-1484 Body_part denotes white blood cells http://purl.org/sig/ont/fma/fma62852
T2 1479-1484 Body_part denotes cells http://purl.org/sig/ont/fma/fma68646

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 1473-1478 Body_part denotes blood http://purl.obolibrary.org/obo/UBERON_0000178

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T4 4-28 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T5 30-38 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T6 764-772 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T7 844-852 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 952-960 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T9 1105-1113 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 1170-1178 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T11 1290-1298 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T12 1408-1417 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T13 2108-2118 Disease denotes COVID-2019 http://purl.obolibrary.org/obo/MONDO_0100096
T14 2153-2162 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T15 2468-2477 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T16 2597-2606 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T17 2923-2932 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T18 3060-3068 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T19 3133-3141 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T20 3327-3336 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T21 3395-3403 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T22 3690-3698 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T23 4039-4047 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T24 4141-4150 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T25 4305-4313 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T26 4425-4434 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T27 4455-4464 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T28 4485-4494 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T29 7817-7826 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T30 8320-8329 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T31 8643-8652 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T32 8759-8767 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T33 8951-8959 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T34 9109-9117 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T35 9141-9159 Disease denotes infectious disease http://purl.obolibrary.org/obo/MONDO_0005550
T36 9763-9772 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T37 9778-9790 Disease denotes dengue fever http://purl.obolibrary.org/obo/MONDO_0005502
T38 9806-9810 Disease denotes Zika http://purl.obolibrary.org/obo/MONDO_0018661
T39 9816-9823 Disease denotes measles http://purl.obolibrary.org/obo/MONDO_0004619
T40 9914-9924 Disease denotes infectious http://purl.obolibrary.org/obo/MONDO_0005550
T41 10308-10316 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T42 10405-10413 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T43 10524-10532 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T44 10714-10722 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T45 11900-11908 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T46 11934-11943 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T47 11970-11979 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T48 12151-12160 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T49 12206-12230 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T50 12246-12254 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T51 12389-12398 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T52 12478-12486 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T53 12592-12601 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T54 12957-12966 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T55 13252-13261 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T56 13811-13829 Disease denotes infectious disease http://purl.obolibrary.org/obo/MONDO_0005550
T57 13992-14000 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T3 141-142 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 185-186 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T5 256-268 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T6 1074-1075 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T7 1126-1127 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T8 1188-1189 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T9 1220-1221 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T10 1473-1484 http://www.ebi.ac.uk/efo/EFO_0000296 denotes blood cells
T11 1563-1565 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T12 2041-2042 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T13 2332-2333 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T14 3441-3444 http://purl.obolibrary.org/obo/CLO_0050236 denotes Lag
T15 3564-3567 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T16 3791-3792 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T17 3960-3963 http://purl.obolibrary.org/obo/CLO_0050236 denotes Lag
T18 4189-4192 http://purl.obolibrary.org/obo/CLO_0050236 denotes Lag
T19 5590-5592 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T20 7108-7110 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T21 7992-7995 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T22 8083-8084 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T23 8093-8096 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T24 8986-8989 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T25 9009-9010 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T26 9121-9122 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T27 9220-9221 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T28 9719-9722 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T29 10233-10234 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T30 10247-10250 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T31 10337-10344 http://www.ebi.ac.uk/efo/EFO_0000881 denotes digital
T32 10417-10418 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T33 10480-10481 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T34 10629-10634 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T35 10772-10777 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T36 10851-10854 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T37 10967-10974 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T38 11546-11547 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T39 11595-11596 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T40 11820-11821 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T41 12040-12041 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T42 12260-12262 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T43 12342-12344 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T44 13226-13227 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T45 13239-13243 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T46 13266-13267 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T47 13379-13380 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T48 13500-13501 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T49 13550-13553 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T50 13568-13569 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T51 13654-13655 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T52 13805-13806 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 1044-1047 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T2 8684-8687 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T3 8977-8980 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T4 11500-11503 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T5 12168-12171 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T6 0-164 Sentence denotes The coronavirus disease 2019 (COVID-19) outbreak began in Wuhan, China, in late December 2019 and quickly spread to other cities in China in a matter of days [1,2].
T7 165-298 Sentence denotes It was announced as a public health emergency of international concern by the World Health Organization (WHO) on 30 January 2020 [3].
T8 299-424 Sentence denotes Predicting the development of the outbreak as early and as reliably as possible is critical for action to prevent its spread.
T9 425-623 Sentence denotes Internet searches and social media data have been reported to correlate with traditional surveillance data and can even predict the outbreak of disease epidemics several days or weeks earlier [4-9].
T10 624-791 Sentence denotes In this study, we aimed to evaluate the prediction value of the Internet search data from web-based search engines and social media for the COVID-19 outbreak in China.
T11 793-876 Sentence denotes Trends in daily laboratory-confirmed and suspected COVID-19 cases and Internet data
T12 877-1073 Sentence denotes The daily numbers of new laboratory-confirmed cases and suspected cases of COVID-19 were collected from the data published by the National Health Commission of China (NHC, http://www.nhc.gov.cn/).
T13 1074-1514 Sentence denotes A laboratory-confirmed case of COVID-19 was defined a patient with positive real-time RT-PCR to SARS-CoV-2, while a suspected case was defined as a patient with history of travelling to Wuhan City or in contact with COVID-19 cases in the 14 days before onset of symptoms and with clinical manifestation of fever, respiratory illness, pneumonia on computed tomography (CT) scan, and/or reduced white blood cells count, but no RT-PCR results.
T14 1515-1640 Sentence denotes The study period was set between 16 January and 11 February 2020, because the diagnosis criteria were set on 16 January 2020.
T15 1641-1809 Sentence denotes The results showed that the peak of daily new laboratory-confirmed cases was 3,887 on 4 February and the peak of daily new suspected cases was 5,328 on 5 February 2020.
T16 1810-2031 Sentence denotes Daily trend data related to specific search terms were acquired from Google Trends, Baidu Index, and Sina Weibo Index by setting the time parameter to ‘2 January to 12 February 2020’ and the location parameter to ‘China’.
T17 2032-2119 Sentence denotes We chose a period 2 weeks earlier than for the molecular diagnosis data for COVID-2019.
T18 2120-2192 Sentence denotes Two keywords, ‘coronavirus’ and ‘pneumonia’, were used in Google Trends.
T19 2193-2377 Sentence denotes The respective Chinese terms, ‘冠状病毒‘ and ‘肺炎’ were used in Baidu Index, the most popular web search engine in China, and Sina Weibo Index, a social media platform widely used in China.
T20 2378-2504 Sentence denotes The peak number of search queries in Baidu was 682,888 for ‘coronavirus’ and 760,460 for ‘pneumonia’, both on 25 January 2020.
T21 2505-2633 Sentence denotes The peak number of posts on Sina Weibo was 26,297,746 for ‘coronavirus’ and 30,704,753 for ‘pneumonia’, both on 21 January 2020.
T22 2634-2743 Sentence denotes Google Trends does not provide the raw number of search queries but the number normalised to the peak number.
T23 2744-2821 Sentence denotes The peaks for both keywords on Google Trends were reached on 25 January 2020.
T24 2822-3075 Sentence denotes Figure 1 shows the overall trends of data from the keyword search for ‘coronavirus’ (or ‘冠状病毒’) and ‘pneumonia’ (or ‘肺炎’) via Google Trends, Baidu Index and Sina Weibo Index, and the number of daily new laboratory-confirmed and suspected COVID-19 cases.
T25 3076-3275 Sentence denotes The data from Baidu Index, Sina Weibo Index and national COVID-19 daily incidence data were also normalised to the peak number, so that the values fall into the same range (0–100) during that period.
T26 3276-3439 Sentence denotes Figure 1 Searches for keywords ‘coronavirus’ and ‘pneumonia’, obtained via different indices, and number of daily new COVID-19 cases, China, January–February 2020
T27 3441-3529 Sentence denotes Lag correlation between daily laboratory-confirmed/suspected cases and Internet searches
T28 3530-3781 Sentence denotes Figure 2 and the Table showed the lag Spearman correlations between the daily new laboratory-confirmed cases (upper panel) and suspected cases (lower panel) of COVID-19 and the Internet search data from Google Trends, Baidu Index and Sina Weibo Index.
T29 3782-3949 Sentence denotes We found a high correlation with the Internet search data (r > 0.7) 8–10 days earlier for new laboratory-confirmed cases, and 5-7 days earlier for new suspected cases.
T30 3950-4181 Sentence denotes Figure 2 Lag correlations between new laboratory-confirmed cases and suspected cases of COVID-19 and data from Google Trends, Baidu Index and Weibo Index for the keywords ‘coronavirus’ and ‘pneumonia’, China, January–February 2020
T31 4182-4349 Sentence denotes Table Lag correlation coefficients and p values between Internet search data and daily new laboratory-confirmed/suspected COVID-19 cases, China, January–February 2020
T32 4350-4408 Sentence denotes Days earlier Google Trends Baidu Index Sina Weibo Index
T33 4409-4497 Sentence denotes Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p
T34 4498-4616 Sentence denotes New laboratory-confirmed cases 0 0.176 0.370 −0.035 0.861 0.021 0.917 0.129 0.513 0.106 0.593 0.109 0.582
T35 4617-4702 Sentence denotes 1 0.324 0.093 0.122 0.537 0.160 0.416 0.265 0.172 0.202 0.303 0.190 0.332
T36 4703-4788 Sentence denotes 2 0.455 0.015 0.271 0.164 0.299 0.122 0.411 0.030 0.346 0.072 0.298 0.123
T37 4789-4874 Sentence denotes 3 0.561 0.002 0.388 0.041 0.406 0.032 0.516 0.005 0.431 0.022 0.408 0.031
T38 4875-4964 Sentence denotes 4 0.672 < 0.001 0.505 0.006 0.529 0.004 0.641 < 0.001 0.498 0.007 0.470 0.012
T39 4965-5058 Sentence denotes 5 0.779  < 0.001 0.606 0.001 0.624 < 0.001 0.722  < 0.001 0.562 0.002 0.553 0.002
T40 5059-5160 Sentence denotes 6 0.850  < 0.001 0.712  < 0.001 0.706  < 0.001 0.808  < 0.001 0.679 < 0.001 0.668 < 0.001
T41 5161-5264 Sentence denotes 7 0.902  < 0.001 0.777  < 0.001 0.750  < 0.001 0.861  < 0.001 0.751  < 0.001 0.754  < 0.001
T42 5265-5371 Sentence denotes 8 0.944  < 0.001 0.835  < 0.001 0.823  < 0.001 0.902   < 0.001 0.829  < 0.001 0.817  < 0.001
T43 5372-5478 Sentence denotes 9 0.958   < 0.001 0.878  < 0.001 0.887  < 0.001 0.892  < 0.001 0.876  < 0.001 0.872  < 0.001
T44 5479-5589 Sentence denotes 10 0.953  < 0.001 0.893   < 0.001 0.928  < 0.001 0.873  < 0.001 0.921  < 0.001 0.899   < 0.001
T45 5590-5694 Sentence denotes 11 0.924  < 0.001 0.845  < 0.001 0.925  < 0.001 0.786  < 0.001 0.917  < 0.001 0.875  < 0.001
T46 5695-5805 Sentence denotes 12 0.857  < 0.001 0.818  < 0.001 0.933   < 0.001 0.715  < 0.001 0.944   < 0.001 0.875  < 0.001
T47 5806-5907 Sentence denotes 13 0.815  < 0.001 0.762  < 0.001 0.908  < 0.001 0.609 0.001 0.916  < 0.001 0.812  < 0.001
T48 5908-6008 Sentence denotes 14 0.783  < 0.001 0.697 < 0.001 0.858  < 0.001 0.496 0.007 0.885  < 0.001 0.733  < 0.001
T49 6009-6121 Sentence denotes New suspected cases 0 −0.003 0.989 −0.372 0.073 −0.309 0.142 −0.091 0.671 −0.279 0.187 −0.309 0.142
T50 6122-6211 Sentence denotes 1 0.246 0.246 −0.116 0.590 −0.068 0.753 0.141 0.511 −0.078 0.716 −0.103 0.630
T51 6212-6297 Sentence denotes 2 0.413 0.045 0.104 0.630 0.125 0.560 0.346 0.098 0.089 0.680 0.050 0.818
T52 6298-6383 Sentence denotes 3 0.614 0.001 0.312 0.138 0.352 0.091 0.551 0.005 0.253 0.233 0.248 0.243
T53 6384-6474 Sentence denotes 4 0.768  < 0.001 0.514 0.010 0.538 0.007 0.697 < 0.001 0.431 0.035 0.383 0.065
T54 6475-6570 Sentence denotes 5 0.832  < 0.001 0.687 < 0.001 0.670 < 0.001 0.816  < 0.001 0.520 0.009 0.501 0.013
T55 6571-6675 Sentence denotes 6 0.912   < 0.001 0.771  < 0.001 0.725  < 0.001 0.895  < 0.001 0.672 < 0.001 0.670 < 0.001
T56 6676-6779 Sentence denotes 7 0.933  < 0.001 0.850  < 0.001 0.830  < 0.001 0.914  < 0.001 0.813  < 0.001 0.872  < 0.001
T57 6780-6898 Sentence denotes 8 0.875  < 0.001 0.960   < 0.001 0.906   < 0.001 0.926   < 0.001 0.924   < 0.001 0.907   < 0.001
T58 6899-7002 Sentence denotes 9 0.787  < 0.001 0.865  < 0.001 0.882  < 0.001 0.850  < 0.001 0.883  < 0.001 0.899  < 0.001
T59 7003-7107 Sentence denotes 10 0.744  < 0.001 0.827  < 0.001 0.841  < 0.001 0.766  < 0.001 0.818  < 0.001 0.832  < 0.001
T60 7108-7210 Sentence denotes 11 0.671 < 0.001 0.770  < 0.001 0.790  < 0.001 0.698 < 0.001 0.781  < 0.001 0.802  < 0.001
T61 7211-7305 Sentence denotes 12 0.544 0.006 0.693 < 0.001 0.686 < 0.001 0.559 0.005 0.697 < 0.001 0.683 < 0.001
T62 7306-7392 Sentence denotes 13 0.482 0.017 0.578 0.003 0.583 0.003 0.454 0.026 0.622 0.001 0.600 0.002
T63 7393-7479 Sentence denotes 14 0.497 0.013 0.448 0.028 0.547 0.006 0.288 0.173 0.609 0.002 0.550 0.005
T64 7480-7523 Sentence denotes Shaded text: high correlation with r > 0.7.
T65 7524-7561 Sentence denotes Text in italics: highest correlation.
T66 7562-7799 Sentence denotes For new laboratory-confirmed cases, the highest correlation was found 9, 12 and 12 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index with, respectively, r = 0.958, 0.933 and 0.944.
T67 7800-7987 Sentence denotes For the keyword ‘pneumonia’, the highest correlation was found 10, 8 and 10 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.893, 0.944 and 0.899, respectively.
T68 7988-8102 Sentence denotes The lag correlation of new suspected cases was similar to the laboratory-confirmed cases, with a shorter lag time.
T69 8103-8302 Sentence denotes The highest correlation was found 6, 8 and 8 days earlier for searches for the keyword ‘coronavirus’ in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.912, 0.906 and 0.924, respectively.
T70 8303-8483 Sentence denotes For the keyword ‘pneumonia’, the highest correlation was found all 8 days earlier in Google Trends, Baidu Index and Sina Weibo Index, with r = 0.960, 0.926 and 0.907, respectively.
T71 8485-8495 Sentence denotes Discussion
T72 8496-8795 Sentence denotes Our study demonstrated that the data obtained from Google Trends, Baidu Index and Sina Weibo Index on searches for the keywords ‘coronavirus’ and ‘pneumonia’ correlated with the published NHC data on daily incidence of laboratory-confirmed and suspected cases of COVID-19, with the maximum r > 0.89.
T73 8796-8981 Sentence denotes We also found that the peak interest for these keywords in Internet search engines and social media data was 10–14 days earlier than the incidence peak of COVID-19 published by the NHC.
T74 8982-9108 Sentence denotes The lag correlation showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
T75 9109-9261 Sentence denotes COVID-19 is a rapidly spreading infectious disease with, at the time of submission, more than 80,000 cases and a mortality so far known to be 3.4% [10].
T76 9262-9406 Sentence denotes It is important to predict the development of this outbreak as early and as reliably as possible, in order to take action to prevent its spread.
T77 9407-9639 Sentence denotes Our data showed that the two popularly used Internet search engines, Google and Baidu, and the social media platform, Sina Weibo, were able to predict the disease outbreak 1–2 weeks earlier than the traditional surveillance systems.
T78 9640-9869 Sentence denotes The role of Internet surveillance tools in early prediction of other epidemics has been reported previously, including for influenza [4], dengue fever [5], H1N1 [6], Zika [7], measles [8] and Middle East respiratory syndrome [9].
T79 9870-10062 Sentence denotes The availability of early information about infectious diseases through Internet search engines and social media will be helpful for making decisions related to disease control and prevention.
T80 10063-10204 Sentence denotes Internet search data have been shown to enable the monitoring of Middle East respiratory syndrome 3 days before laboratory confirmations [9].
T81 10205-10363 Sentence denotes However, our results showed a much longer lag time for reported new laboratory-confirmed and suspected COVID-19 cases compared with digital surveillance data.
T82 10364-10395 Sentence denotes There are several explanations.
T83 10396-10458 Sentence denotes Firstly, COVID-19 is a novel disease just recently recognised.
T84 10459-10566 Sentence denotes The first version of a guideline for diagnosis and management of COVID-19 was announced on 16 January 2020.
T85 10567-10686 Sentence denotes It took time for the medical professionals to learn about the virus and the disease in order to make correct diagnosis.
T86 10687-10822 Sentence denotes Secondly, the diagnosis of COVID-19 requires two independent confirmatory laboratory tests, which should be taken at least 1 day apart.
T87 10823-10932 Sentence denotes Our results showed that the lag correlation is shorter for the suspected than for laboratory-confirmed cases.
T88 10933-11126 Sentence denotes Thirdly, the supply of laboratory testing kits may have been insufficient in the early stages of the coronavirus outbreak, which would have limited the number of patients that can be confirmed.
T89 11127-11344 Sentence denotes Finally, the Internet searches and social media mentions are not only initiated by the patients and their family members, but also globally by the general public who are concerned about this rapidly spreading disease.
T90 11345-11545 Sentence denotes In addition, we found that the data from the Baidu Index and Sina Weibo Index could monitor the number of daily new confirmed and suspected cases from the NHC earlier than the data from Google Trends.
T91 11546-11675 Sentence denotes A possible explanation is that the Google is not a major search engine used in China, where Baidu and Sina Weibo are widely used.
T92 11676-11767 Sentence denotes The peak in the Sina Weibo Index was reached earlier than in Google Trends and Baidu Index.
T93 11768-11899 Sentence denotes This suggests that Sina Weibo, which also serves as a social medium, disseminated the information faster than traditional websites.
T94 11900-12020 Sentence denotes COVID-19 was firstly reported as ‘pneumonia of unknown aetiology’ or ‘pneumonia of unknown cause’ in late December 2019.
T95 12021-12104 Sentence denotes On 8 January 2020, a novel coronavirus was identified as the cause of this disease.
T96 12105-12283 Sentence denotes The disease was first named Novel coronavirus pneumonia by the NHC of China on 8 February and later ‘coronavirus disease 2019’ (abbreviated ‘COVID-19’) on 11 February by the WHO.
T97 12284-12345 Sentence denotes Our search period was defined from January 16 to February 11.
T98 12346-12502 Sentence denotes Therefore, we think that the two keywords ‘pneumonia’ and ‘coronavirus’ were sufficient to include most Internet content related to COVID-19 in this period.
T99 12503-12729 Sentence denotes We also used other terms such as ‘新冠‘ (novel coronavirus), ‘新型冠状病毒肺炎’ (novel coronavirus pneumonia) as keywords but they returned much smaller numbers of queries and posts and we did therefore not include them in the analysis.
T100 12730-12819 Sentence denotes It is also notable that the strength of correlation was different for different keywords.
T101 12820-13051 Sentence denotes On Google, the keyword ‘coronavirus’ had the highest correlation coefficient (r = 0.958) with daily new laboratory-confirmed cases, and ‘pneumonia’ had the highest correlation coefficient with daily new suspected cases (r = 0.960).
T102 13052-13118 Sentence denotes We found the same pattern in the Baidu Index and Sina Weibo Index.
T103 13119-13378 Sentence denotes An explanation could be that ‘coronavirus’ is linked to the viral pathogen which should be investigated by a laboratory test, while ‘pneumonia’ is a clinical term and should link stronger to the suspected cases that are based on clinical and imaging evidence.
T104 13379-13433 Sentence denotes A limitation of our study is its retrospective nature.
T105 13434-13668 Sentence denotes If the Internet search engines and social media data were used in a real-time surveillance system, finding the best lag time would be a challenge because we would not have any training data to calibrate the analysis for a new disease.
T106 13670-13680 Sentence denotes Conclusion
T107 13681-13830 Sentence denotes This study reveals the advantages of Internet surveillance using Sina Weibo Index, Google Trends and Baidu Index to monitor a new infectious disease.
T108 13831-13879 Sentence denotes Reliable data can be obtained early at low cost.
T109 13880-14001 Sentence denotes The Internet surveillance data provided an accurate and timely prediction about the outbreak and progression of COVID-19.

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T1 1380-1385 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T2 1387-1406 Phenotype denotes respiratory illness http://purl.obolibrary.org/obo/HP_0002086
T3 1408-1417 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T4 2153-2162 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T5 2468-2477 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T6 2597-2606 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T7 2923-2932 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T8 3327-3336 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T9 4141-4150 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T10 4425-4434 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T11 4455-4464 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T12 4485-4494 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T13 7817-7826 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T14 8320-8329 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T15 8643-8652 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T16 9785-9790 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T17 11934-11943 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T18 11970-11979 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T19 12151-12160 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T20 12389-12398 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T21 12592-12601 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T22 12957-12966 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T23 13252-13261 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090

2_test

Id Subject Object Predicate Lexical cue
32183935-19020500-29321489 9774-9775 19020500 denotes 4
32183935-28719659-29321490 9792-9793 28719659 denotes 5
32183935-19941777-29321491 9802-9803 19941777 denotes 6
32183935-27251981-29321492 9812-9813 27251981 denotes 7
32183935-31268123-29321493 9825-9826 31268123 denotes 8
32183935-27595921-29321494 9866-9867 27595921 denotes 9
32183935-27595921-29321495 10201-10202 27595921 denotes 9

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
8 4-28 Disease denotes coronavirus disease 2019 MESH:C000657245
9 30-38 Disease denotes COVID-19 MESH:C000657245
11 764-772 Disease denotes COVID-19 MESH:C000657245
13 844-852 Disease denotes COVID-19 MESH:C000657245
23 1128-1135 Species denotes patient Tax:9606
24 1170-1180 Species denotes SARS-CoV-2 Tax:2697049
25 1222-1229 Species denotes patient Tax:9606
26 952-960 Disease denotes COVID-19 MESH:C000657245
27 1105-1113 Disease denotes COVID-19 MESH:C000657245
28 1290-1298 Disease denotes COVID-19 MESH:C000657245
29 1380-1385 Disease denotes fever MESH:D005334
30 1387-1406 Disease denotes respiratory illness MESH:D012140
31 1408-1417 Disease denotes pneumonia MESH:D011014
42 1911-1915 Species denotes Sina Tax:647292
43 2135-2146 Species denotes coronavirus Tax:11118
44 2314-2318 Species denotes Sina Tax:647292
45 2438-2449 Species denotes coronavirus Tax:11118
46 2533-2537 Species denotes Sina Tax:647292
47 2564-2575 Species denotes coronavirus Tax:11118
48 2108-2118 Disease denotes COVID-2019 MESH:C000657245
49 2153-2162 Disease denotes pneumonia MESH:D011014
50 2468-2477 Disease denotes pneumonia MESH:D011014
51 2597-2606 Disease denotes pneumonia MESH:D011014
58 2893-2904 Species denotes coronavirus Tax:11118
59 2979-2983 Species denotes Sina Tax:647292
60 3103-3107 Species denotes Sina Tax:647292
61 2923-2932 Disease denotes pneumonia MESH:D011014
62 3060-3068 Disease denotes COVID-19 MESH:C000657245
63 3133-3141 Disease denotes COVID-19 MESH:C000657245
67 3309-3320 Species denotes coronavirus Tax:11118
68 3327-3336 Disease denotes pneumonia MESH:D011014
69 3395-3403 Disease denotes COVID-19 MESH:C000657245
72 3764-3768 Species denotes Sina Tax:647292
73 3690-3698 Disease denotes COVID-19 MESH:C000657245
77 4123-4134 Species denotes coronavirus Tax:11118
78 4039-4047 Disease denotes COVID-19 MESH:C000657245
79 4141-4150 Disease denotes pneumonia MESH:D011014
82 4392-4396 Species denotes Sina Tax:647292
83 4409-4494 Disease denotes Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia MESH:D018352
85 4305-4313 Disease denotes COVID-19 MESH:C000657245
90 7688-7699 Species denotes coronavirus Tax:11118
91 7735-7739 Species denotes Sina Tax:647292
92 7923-7927 Species denotes Sina Tax:647292
93 7817-7826 Disease denotes pneumonia MESH:D011014
98 8191-8202 Species denotes coronavirus Tax:11118
99 8238-8242 Species denotes Sina Tax:647292
100 8419-8423 Species denotes Sina Tax:647292
101 8320-8329 Disease denotes pneumonia MESH:D011014
107 8578-8582 Species denotes Sina Tax:647292
108 8625-8636 Species denotes coronavirus Tax:11118
109 8643-8652 Disease denotes pneumonia MESH:D011014
110 8759-8767 Disease denotes COVID-19 MESH:C000657245
111 8951-8959 Disease denotes COVID-19 MESH:C000657245
120 9525-9529 Species denotes Sina Tax:647292
122 9109-9117 Disease denotes COVID-19 MESH:C000657245
123 9141-9159 Disease denotes infectious disease MESH:D003141
124 9222-9231 Disease denotes mortality MESH:D003643
125 9785-9790 Disease denotes fever MESH:D005334
126 9832-9864 Disease denotes Middle East respiratory syndrome MESH:D018352
127 9914-9933 Disease denotes infectious diseases MESH:D003141
136 11034-11045 Species denotes coronavirus Tax:11118
137 11095-11103 Species denotes patients Tax:9606
138 11214-11222 Species denotes patients Tax:9606
139 10128-10160 Disease denotes Middle East respiratory syndrome MESH:D018352
140 10308-10316 Disease denotes COVID-19 MESH:C000657245
141 10405-10413 Disease denotes COVID-19 MESH:C000657245
142 10524-10532 Disease denotes COVID-19 MESH:C000657245
143 10714-10722 Disease denotes COVID-19 MESH:C000657245
148 11406-11410 Species denotes Sina Tax:647292
149 11648-11652 Species denotes Sina Tax:647292
150 11692-11696 Species denotes Sina Tax:647292
151 11787-11791 Species denotes Sina Tax:647292
164 12042-12059 Species denotes novel coronavirus Tax:2697049
165 12405-12416 Species denotes coronavirus Tax:11118
166 12542-12559 Species denotes novel coronavirus Tax:2697049
167 11900-11908 Disease denotes COVID-19 MESH:C000657245
168 11934-11943 Disease denotes pneumonia MESH:D011014
169 11970-11979 Disease denotes pneumonia MESH:D011014
170 12133-12160 Disease denotes Novel coronavirus pneumonia MESH:C000657245
171 12206-12230 Disease denotes coronavirus disease 2019 MESH:C000657245
172 12246-12254 Disease denotes COVID-19 MESH:C000657245
173 12389-12398 Disease denotes pneumonia MESH:D011014
174 12478-12486 Disease denotes COVID-19 MESH:C000657245
175 12574-12601 Disease denotes novel coronavirus pneumonia MESH:C000657245
181 12844-12855 Species denotes coronavirus Tax:11118
182 13101-13105 Species denotes Sina Tax:647292
183 13149-13160 Species denotes coronavirus Tax:11118
184 12957-12966 Disease denotes pneumonia MESH:D011014
185 13252-13261 Disease denotes pneumonia MESH:D011014
189 13746-13750 Species denotes Sina Tax:647292
190 13811-13829 Disease denotes infectious disease MESH:D003141
191 13992-14000 Disease denotes COVID-19 MESH:C000657245

MyTest

Id Subject Object Predicate Lexical cue
32183935-19020500-29321489 9774-9775 19020500 denotes 4
32183935-28719659-29321490 9792-9793 28719659 denotes 5
32183935-19941777-29321491 9802-9803 19941777 denotes 6
32183935-27251981-29321492 9812-9813 27251981 denotes 7
32183935-31268123-29321493 9825-9826 31268123 denotes 8
32183935-27595921-29321494 9866-9867 27595921 denotes 9
32183935-27595921-29321495 10201-10202 27595921 denotes 9