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LitCovid-PMC-OGER-BB

Id Subject Object Predicate Lexical cue
T56 4895-4903 SP_7 denotes COVID-19
T55 4629-4637 SP_7 denotes COVID-19
T54 4713-4724 NCBITaxon:11118 denotes coronavirus
T53 3899-3910 NCBITaxon:11118 denotes coronavirus
T52 3985-3993 SP_7 denotes COVID-19
T51 14582-14590 SP_7 denotes COVID-19
T50 9215-9226 NCBITaxon:11118 denotes coronavirus
T49 9349-9357 SP_7 denotes COVID-19
T48 9541-9549 SP_7 denotes COVID-19
T47 9699-9707 SP_7 denotes COVID-19
T46 10251-10263 GO:0065007 denotes surveillance
T45 10434-10445 UBERON:0001004 denotes respiratory
T44 10629-10636 GO:0065007 denotes control
T43 10730-10741 UBERON:0001004 denotes respiratory
T42 10898-10906 SP_7 denotes COVID-19
T41 10995-11003 SP_7 denotes COVID-19
T40 11114-11122 SP_7 denotes COVID-19
T39 11203-11208 GO:0007612 denotes learn
T38 11219-11224 NCBITaxon:10239 denotes virus
T36 11624-11635 NCBITaxon:11118 denotes coronavirus
T35 12490-12498 SP_7 denotes COVID-19
T34 12638-12649 NCBITaxon:11118 denotes coronavirus
T33 12729-12740 NCBITaxon:11118 denotes coronavirus
T32 12796-12807 NCBITaxon:11118 denotes coronavirus
T31 12836-12844 SP_7 denotes COVID-19
T30 12995-13006 NCBITaxon:11118 denotes coronavirus
T29 13068-13076 SP_7 denotes COVID-19
T28 13138-13149 NCBITaxon:11118 denotes coronavirus
T3 60-68 SP_7 denotes COVID-19
T27 13170-13181 NCBITaxon:11118 denotes coronavirus
T26 13434-13445 NCBITaxon:11118 denotes coronavirus
T25 13739-13750 NCBITaxon:11118 denotes coronavirus
T24 13769-13774 NCBITaxon:10239 denotes viral
T23 4280-4288 SP_7 denotes COVID-19
T22 8278-8289 NCBITaxon:11118 denotes coronavirus
T21 8781-8792 NCBITaxon:11118 denotes coronavirus
T20 1542-1550 SP_7 denotes COVID-19
T19 1695-1703 SP_7 denotes COVID-19
T18 1760-1770 SP_7 denotes SARS-CoV-2
T17 1880-1888 SP_7 denotes COVID-19
T16 1977-1988 UBERON:0001004 denotes respiratory
T15 2057-2062 CL:0000738 denotes white
T14 2063-2068 CL:0000738;UBERON:0000178 denotes blood
T13 2069-2074 CL:0000738 denotes cells
T12 2725-2736 NCBITaxon:11118 denotes coronavirus
T11 3028-3039 NCBITaxon:11118 denotes coronavirus
T10 3154-3165 NCBITaxon:11118 denotes coronavirus
T9 3483-3494 NCBITaxon:11118 denotes coronavirus
T8 3650-3658 SP_7 denotes COVID-19
T7 3723-3731 SP_7 denotes COVID-19
T6 208-219 NCBITaxon:11118 denotes coronavirus
T5 234-242 SP_7 denotes COVID-19
T4 1354-1362 SP_7 denotes COVID-19
T2 208-219 NCBITaxon:11118 denotes coronavirus
T1 234-242 SP_7 denotes COVID-19
T37 11304-11312 SP_7 denotes COVID-19

LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 2057-2074 Body_part denotes white blood cells http://purl.org/sig/ont/fma/fma62852
T2 2069-2074 Body_part denotes cells http://purl.org/sig/ont/fma/fma68646

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 2063-2068 Body_part denotes blood http://purl.obolibrary.org/obo/UBERON_0000178

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 60-68 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 208-232 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T3 234-242 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 594-618 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T5 620-628 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T6 1354-1362 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T7 1434-1442 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 1542-1550 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T9 1695-1703 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 1760-1768 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T11 1880-1888 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T12 1998-2007 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T13 2698-2708 Disease denotes COVID-2019 http://purl.obolibrary.org/obo/MONDO_0100096
T14 2743-2752 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T15 3058-3067 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T16 3187-3196 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T17 3513-3522 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T18 3650-3658 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T19 3723-3731 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T20 3917-3926 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T21 3985-3993 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T22 4280-4288 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T23 4629-4637 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T24 4731-4740 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T25 4895-4903 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T26 5015-5024 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T27 5045-5054 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T28 5075-5084 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T29 8407-8416 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T30 8910-8919 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T31 9233-9242 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T32 9349-9357 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T33 9541-9549 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T34 9699-9707 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T35 9731-9749 Disease denotes infectious disease http://purl.obolibrary.org/obo/MONDO_0005550
T36 10353-10362 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T37 10368-10380 Disease denotes dengue fever http://purl.obolibrary.org/obo/MONDO_0005502
T38 10396-10400 Disease denotes Zika http://purl.obolibrary.org/obo/MONDO_0018661
T39 10406-10413 Disease denotes measles http://purl.obolibrary.org/obo/MONDO_0004619
T40 10504-10514 Disease denotes infectious http://purl.obolibrary.org/obo/MONDO_0005550
T41 10898-10906 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T42 10995-11003 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T43 11114-11122 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T44 11304-11312 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T45 12490-12498 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T46 12524-12533 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T47 12560-12569 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T48 12741-12750 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T49 12796-12820 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T50 12836-12844 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T51 12979-12988 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T52 13068-13076 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T53 13182-13191 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T54 13547-13556 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T55 13842-13851 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T56 14401-14419 Disease denotes infectious disease http://purl.obolibrary.org/obo/MONDO_0005550
T57 14582-14590 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 460-463 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T2 489-490 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T3 731-732 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 775-776 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T5 846-858 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T6 1664-1665 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T7 1716-1717 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T8 1778-1779 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T9 1810-1811 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T10 2063-2074 http://www.ebi.ac.uk/efo/EFO_0000296 denotes blood cells
T11 2153-2155 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T12 2631-2632 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T13 2922-2923 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T14 4031-4034 http://purl.obolibrary.org/obo/CLO_0050236 denotes Lag
T15 4154-4157 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T16 4381-4382 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T17 4550-4553 http://purl.obolibrary.org/obo/CLO_0050236 denotes Lag
T18 4779-4782 http://purl.obolibrary.org/obo/CLO_0050236 denotes Lag
T19 6180-6182 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T20 7698-7700 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T21 8582-8585 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T22 8673-8674 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T23 8683-8686 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T24 9576-9579 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T25 9599-9600 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T26 9711-9712 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T27 9810-9811 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T28 10309-10312 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T29 10823-10824 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T30 10837-10840 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T31 10927-10934 http://www.ebi.ac.uk/efo/EFO_0000881 denotes digital
T32 11007-11008 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T33 11070-11071 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T34 11219-11224 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T35 11362-11367 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T36 11441-11444 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T37 11557-11564 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T38 12136-12137 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T39 12185-12186 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T40 12410-12411 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T41 12630-12631 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T42 12850-12852 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T43 12932-12934 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T44 13816-13817 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T45 13829-13833 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T46 13856-13857 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T47 13969-13970 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T48 14090-14091 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T49 14140-14143 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T50 14158-14159 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T51 14244-14245 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T52 14395-14396 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T53 14983-14985 http://purl.obolibrary.org/obo/CLO_0052906 denotes CL
T54 15060-15062 http://purl.obolibrary.org/obo/CLO_0009645 denotes XC
T55 15060-15062 http://purl.obolibrary.org/obo/CLO_0050824 denotes XC

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 1634-1637 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T2 9274-9277 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T3 9567-9570 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T4 12090-12093 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T5 12758-12761 Chemical denotes NHC http://purl.obolibrary.org/obo/CHEBI_51369
T6 15069-15072 Chemical denotes CPP http://purl.obolibrary.org/obo/CHEBI_34603

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-135 Sentence denotes Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
T2 137-145 Sentence denotes Abstract
T3 146-324 Sentence denotes The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China.
T4 325-455 Sentence denotes Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations.
T5 456-588 Sentence denotes The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
T6 590-754 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 755-888 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 889-1014 Sentence denotes Predicting the development of the outbreak as early and as reliably as possible is critical for action to prevent its spread.
T9 1015-1213 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 1214-1381 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 1383-1466 Sentence denotes Trends in daily laboratory-confirmed and suspected COVID-19 cases and Internet data
T12 1467-1663 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 1664-2104 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 2105-2230 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 2231-2399 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 2400-2621 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 2622-2709 Sentence denotes We chose a period 2 weeks earlier than for the molecular diagnosis data for COVID-2019.
T18 2710-2782 Sentence denotes Two keywords, ‘coronavirus’ and ‘pneumonia’, were used in Google Trends.
T19 2783-2967 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 2968-3094 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 3095-3223 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 3224-3333 Sentence denotes Google Trends does not provide the raw number of search queries but the number normalised to the peak number.
T23 3334-3411 Sentence denotes The peaks for both keywords on Google Trends were reached on 25 January 2020.
T24 3412-3665 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 3666-3865 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 3866-4029 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 4031-4119 Sentence denotes Lag correlation between daily laboratory-confirmed/suspected cases and Internet searches
T28 4120-4371 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 4372-4539 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 4540-4771 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 4772-4939 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 4940-4998 Sentence denotes Days earlier Google Trends Baidu Index Sina Weibo Index
T33 4999-5087 Sentence denotes Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia p
T34 5088-5206 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 5207-5292 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 5293-5378 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 5379-5464 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 5465-5554 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 5555-5648 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 5649-5750 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 5751-5854 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 5855-5961 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 5962-6068 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 6069-6179 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 6180-6284 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 6285-6395 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 6396-6497 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 6498-6598 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 6599-6711 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 6712-6801 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 6802-6887 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 6888-6973 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 6974-7064 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 7065-7160 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 7161-7265 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 7266-7369 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 7370-7488 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 7489-7592 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 7593-7697 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 7698-7800 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 7801-7895 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 7896-7982 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 7983-8069 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 8070-8113 Sentence denotes Shaded text: high correlation with r > 0.7.
T65 8114-8151 Sentence denotes Text in italics: highest correlation.
T66 8152-8389 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 8390-8577 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 8578-8692 Sentence denotes The lag correlation of new suspected cases was similar to the laboratory-confirmed cases, with a shorter lag time.
T69 8693-8892 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 8893-9073 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 9075-9085 Sentence denotes Discussion
T72 9086-9385 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 9386-9571 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 9572-9698 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 9699-9851 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 9852-9996 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 9997-10229 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 10230-10459 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 10460-10652 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 10653-10794 Sentence denotes Internet search data have been shown to enable the monitoring of Middle East respiratory syndrome 3 days before laboratory confirmations [9].
T81 10795-10953 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 10954-10985 Sentence denotes There are several explanations.
T83 10986-11048 Sentence denotes Firstly, COVID-19 is a novel disease just recently recognised.
T84 11049-11156 Sentence denotes The first version of a guideline for diagnosis and management of COVID-19 was announced on 16 January 2020.
T85 11157-11276 Sentence denotes It took time for the medical professionals to learn about the virus and the disease in order to make correct diagnosis.
T86 11277-11412 Sentence denotes Secondly, the diagnosis of COVID-19 requires two independent confirmatory laboratory tests, which should be taken at least 1 day apart.
T87 11413-11522 Sentence denotes Our results showed that the lag correlation is shorter for the suspected than for laboratory-confirmed cases.
T88 11523-11716 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 11717-11934 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 11935-12135 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 12136-12265 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 12266-12357 Sentence denotes The peak in the Sina Weibo Index was reached earlier than in Google Trends and Baidu Index.
T93 12358-12489 Sentence denotes This suggests that Sina Weibo, which also serves as a social medium, disseminated the information faster than traditional websites.
T94 12490-12610 Sentence denotes COVID-19 was firstly reported as ‘pneumonia of unknown aetiology’ or ‘pneumonia of unknown cause’ in late December 2019.
T95 12611-12694 Sentence denotes On 8 January 2020, a novel coronavirus was identified as the cause of this disease.
T96 12695-12873 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 12874-12935 Sentence denotes Our search period was defined from January 16 to February 11.
T98 12936-13092 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 13093-13319 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 13320-13409 Sentence denotes It is also notable that the strength of correlation was different for different keywords.
T101 13410-13641 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 13642-13708 Sentence denotes We found the same pattern in the Baidu Index and Sina Weibo Index.
T103 13709-13968 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 13969-14023 Sentence denotes A limitation of our study is its retrospective nature.
T105 14024-14258 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 14260-14270 Sentence denotes Conclusion
T107 14271-14420 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 14421-14469 Sentence denotes Reliable data can be obtained early at low cost.
T109 14470-14591 Sentence denotes The Internet surveillance data provided an accurate and timely prediction about the outbreak and progression of COVID-19.
T110 14593-14609 Sentence denotes Acknowledgements
T111 14610-14628 Sentence denotes Funding statement:
T112 14629-14794 Sentence denotes This study was supported by the Grant for Key Disciplinary Project of Clinical Medicine under the Guangdong High-level University Development Program (002-18119101).
T113 14795-14920 Sentence denotes The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
T114 14922-14943 Sentence denotes Conflict of interest:
T115 14944-14958 Sentence denotes None declared.
T116 14959-15059 Sentence denotes Authors’ contributions: CL and LJC collected the data, analysed the data and drafted the manuscript.
T117 15060-15103 Sentence denotes XC, LJC, CPP and HC revised the manuscript.
T118 15104-15155 Sentence denotes MZ and HC convened the idea and designed the study.

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T1 1970-1975 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T2 1977-1996 Phenotype denotes respiratory illness http://purl.obolibrary.org/obo/HP_0002086
T3 1998-2007 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T4 2743-2752 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T5 3058-3067 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T6 3187-3196 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T7 3513-3522 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T8 3917-3926 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T9 4731-4740 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T10 5015-5024 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T11 5045-5054 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T12 5075-5084 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T13 8407-8416 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T14 8910-8919 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T15 9233-9242 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T16 10375-10380 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T17 12524-12533 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T18 12560-12569 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T19 12741-12750 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T20 12979-12988 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T21 13182-13191 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T22 13547-13556 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T23 13842-13851 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090

2_test

Id Subject Object Predicate Lexical cue
32183935-19020500-29321489 10364-10365 19020500 denotes 4
32183935-28719659-29321490 10382-10383 28719659 denotes 5
32183935-19941777-29321491 10392-10393 19941777 denotes 6
32183935-27251981-29321492 10402-10403 27251981 denotes 7
32183935-31268123-29321493 10415-10416 31268123 denotes 8
32183935-27595921-29321494 10456-10457 27595921 denotes 9
32183935-27595921-29321495 10791-10792 27595921 denotes 9

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 60-68 Disease denotes COVID-19 MESH:C000657245
4 208-232 Disease denotes coronavirus disease 2019 MESH:C000657245
5 234-242 Disease denotes COVID-19 MESH:C000657245
8 594-618 Disease denotes coronavirus disease 2019 MESH:C000657245
9 620-628 Disease denotes COVID-19 MESH:C000657245
11 1354-1362 Disease denotes COVID-19 MESH:C000657245
13 1434-1442 Disease denotes COVID-19 MESH:C000657245
23 1718-1725 Species denotes patient Tax:9606
24 1760-1770 Species denotes SARS-CoV-2 Tax:2697049
25 1812-1819 Species denotes patient Tax:9606
26 1542-1550 Disease denotes COVID-19 MESH:C000657245
27 1695-1703 Disease denotes COVID-19 MESH:C000657245
28 1880-1888 Disease denotes COVID-19 MESH:C000657245
29 1970-1975 Disease denotes fever MESH:D005334
30 1977-1996 Disease denotes respiratory illness MESH:D012140
31 1998-2007 Disease denotes pneumonia MESH:D011014
42 2501-2505 Species denotes Sina Tax:647292
43 2725-2736 Species denotes coronavirus Tax:11118
44 2904-2908 Species denotes Sina Tax:647292
45 3028-3039 Species denotes coronavirus Tax:11118
46 3123-3127 Species denotes Sina Tax:647292
47 3154-3165 Species denotes coronavirus Tax:11118
48 2698-2708 Disease denotes COVID-2019 MESH:C000657245
49 2743-2752 Disease denotes pneumonia MESH:D011014
50 3058-3067 Disease denotes pneumonia MESH:D011014
51 3187-3196 Disease denotes pneumonia MESH:D011014
58 3483-3494 Species denotes coronavirus Tax:11118
59 3569-3573 Species denotes Sina Tax:647292
60 3693-3697 Species denotes Sina Tax:647292
61 3513-3522 Disease denotes pneumonia MESH:D011014
62 3650-3658 Disease denotes COVID-19 MESH:C000657245
63 3723-3731 Disease denotes COVID-19 MESH:C000657245
67 3899-3910 Species denotes coronavirus Tax:11118
68 3917-3926 Disease denotes pneumonia MESH:D011014
69 3985-3993 Disease denotes COVID-19 MESH:C000657245
72 4354-4358 Species denotes Sina Tax:647292
73 4280-4288 Disease denotes COVID-19 MESH:C000657245
77 4713-4724 Species denotes coronavirus Tax:11118
78 4629-4637 Disease denotes COVID-19 MESH:C000657245
79 4731-4740 Disease denotes pneumonia MESH:D011014
82 4982-4986 Species denotes Sina Tax:647292
83 4999-5084 Disease denotes Coronavirus p Pneumonia p Coronavirus p Pneumonia p Coronavirus p Pneumonia MESH:D018352
85 4895-4903 Disease denotes COVID-19 MESH:C000657245
90 8278-8289 Species denotes coronavirus Tax:11118
91 8325-8329 Species denotes Sina Tax:647292
92 8513-8517 Species denotes Sina Tax:647292
93 8407-8416 Disease denotes pneumonia MESH:D011014
98 8781-8792 Species denotes coronavirus Tax:11118
99 8828-8832 Species denotes Sina Tax:647292
100 9009-9013 Species denotes Sina Tax:647292
101 8910-8919 Disease denotes pneumonia MESH:D011014
107 9168-9172 Species denotes Sina Tax:647292
108 9215-9226 Species denotes coronavirus Tax:11118
109 9233-9242 Disease denotes pneumonia MESH:D011014
110 9349-9357 Disease denotes COVID-19 MESH:C000657245
111 9541-9549 Disease denotes COVID-19 MESH:C000657245
120 10115-10119 Species denotes Sina Tax:647292
122 9699-9707 Disease denotes COVID-19 MESH:C000657245
123 9731-9749 Disease denotes infectious disease MESH:D003141
124 9812-9821 Disease denotes mortality MESH:D003643
125 10375-10380 Disease denotes fever MESH:D005334
126 10422-10454 Disease denotes Middle East respiratory syndrome MESH:D018352
127 10504-10523 Disease denotes infectious diseases MESH:D003141
136 11624-11635 Species denotes coronavirus Tax:11118
137 11685-11693 Species denotes patients Tax:9606
138 11804-11812 Species denotes patients Tax:9606
139 10718-10750 Disease denotes Middle East respiratory syndrome MESH:D018352
140 10898-10906 Disease denotes COVID-19 MESH:C000657245
141 10995-11003 Disease denotes COVID-19 MESH:C000657245
142 11114-11122 Disease denotes COVID-19 MESH:C000657245
143 11304-11312 Disease denotes COVID-19 MESH:C000657245
148 11996-12000 Species denotes Sina Tax:647292
149 12238-12242 Species denotes Sina Tax:647292
150 12282-12286 Species denotes Sina Tax:647292
151 12377-12381 Species denotes Sina Tax:647292
164 12632-12649 Species denotes novel coronavirus Tax:2697049
165 12995-13006 Species denotes coronavirus Tax:11118
166 13132-13149 Species denotes novel coronavirus Tax:2697049
167 12490-12498 Disease denotes COVID-19 MESH:C000657245
168 12524-12533 Disease denotes pneumonia MESH:D011014
169 12560-12569 Disease denotes pneumonia MESH:D011014
170 12723-12750 Disease denotes Novel coronavirus pneumonia MESH:C000657245
171 12796-12820 Disease denotes coronavirus disease 2019 MESH:C000657245
172 12836-12844 Disease denotes COVID-19 MESH:C000657245
173 12979-12988 Disease denotes pneumonia MESH:D011014
174 13068-13076 Disease denotes COVID-19 MESH:C000657245
175 13164-13191 Disease denotes novel coronavirus pneumonia MESH:C000657245
181 13434-13445 Species denotes coronavirus Tax:11118
182 13691-13695 Species denotes Sina Tax:647292
183 13739-13750 Species denotes coronavirus Tax:11118
184 13547-13556 Disease denotes pneumonia MESH:D011014
185 13842-13851 Disease denotes pneumonia MESH:D011014
189 14336-14340 Species denotes Sina Tax:647292
190 14401-14419 Disease denotes infectious disease MESH:D003141
191 14582-14590 Disease denotes COVID-19 MESH:C000657245
193 14983-14985 Disease denotes CL

MyTest

Id Subject Object Predicate Lexical cue
32183935-19020500-29321489 10364-10365 19020500 denotes 4
32183935-28719659-29321490 10382-10383 28719659 denotes 5
32183935-19941777-29321491 10392-10393 19941777 denotes 6
32183935-27251981-29321492 10402-10403 27251981 denotes 7
32183935-31268123-29321493 10415-10416 31268123 denotes 8
32183935-27595921-29321494 10456-10457 27595921 denotes 9
32183935-27595921-29321495 10791-10792 27595921 denotes 9