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PMC:7102659 / 15240-17652 JSONTXT

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LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T42 167-176 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T43 181-190 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T44 238-247 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T45 285-293 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T46 344-353 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T47 366-370 Disease denotes SARS http://purl.obolibrary.org/obo/MONDO_0005091
T48 384-393 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T49 501-510 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T50 515-524 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T51 579-588 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T52 596-605 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T53 684-692 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T54 1742-1751 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T55 1799-1807 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T56 1812-1821 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T57 2125-2133 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T71 124-125 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T72 427-428 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T73 885-886 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T74 925-926 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T75 1121-1122 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T76 1230-1231 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T77 1331-1332 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T78 1390-1391 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T79 1544-1551 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T80 1607-1608 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T81 2094-2101 http://purl.obolibrary.org/obo/CLO_0009985 denotes focused
T82 2392-2397 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T7 181-190 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T8 515-524 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T4 801-812 http://purl.obolibrary.org/obo/GO_0007610 denotes behavioural

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T125 0-26 Sentence denotes Discussion and conclusions
T126 27-83 Sentence denotes We used some parameter estimates from (He et al., 2013).
T127 84-257 Sentence denotes The estimates were obtained via fitting a mechanistic model to the observed weekly influenza and pneumonia mortality in England and Wales during the 1918 influenza pandemic.
T128 258-314 Sentence denotes Recent studies showed that COVID-19 transmitted rapidly.
T129 315-371 Sentence denotes In this regard, it resembles influenza rather than SARS.
T130 372-563 Sentence denotes In our 1918 influenza work (He et al., 2013), we built a similar model as we introduced here, and we fitted that model to weekly influenza and pneumonia mortality in 334 administrative units.
T131 564-709 Sentence denotes Note that 1918 influenza had an infection-fatality-rate of 2%, which was at the same level of the case-fatality-rate of COVID-19 in Wuhan, China.
T132 710-945 Sentence denotes The merit of our model is that we considered some essential elements, including individual behavioural response, governmental actions, zoonotic transmission and emigration of a large proportion of the population in a short time period.
T133 946-1062 Sentence denotes Meanwhile, our model is relatively simple and our estimates are in line with previous studies (Imai et al., 2020, P.
T134 1063-1080 Sentence denotes Wu et al., 2020).
T135 1081-1162 Sentence denotes Thus, our model should be considered as a baseline model for further improvement.
T136 1163-1213 Sentence denotes We avoid to fit model to data in conventional way.
T137 1214-1296 Sentence denotes Instead, we use a simple model framework to discuss what elements might be needed.
T138 1297-1571 Sentence denotes For instance, in order to achieve a good fitting performance, one obviously needs to include a time-varying report rate (as we reconstructed in Figure 4b), which was caused by the availability of medical supplies, hospital capacities and changing testing/reporting policies.
T139 1572-1692 Sentence denotes Thus it would be challenging given a relatively short time series, and several other unknown parameters to be estimated.
T140 1693-1880 Sentence denotes We employ some parameter estimates from the 1918 influenza pandemic, given the similar characteristics of COVID-19 and influenza (most cases are mild) and the similar level of mitigation.
T141 1881-2076 Sentence denotes Transmission from asymptotically infected cases is reported but the contribution of asymptomatic transmission is unclear (presumably small), which shall be further investigated in future studies.
T142 2077-2150 Sentence denotes In this work, we focused on the transmission of COVID-19 in Wuhan, China.
T143 2151-2308 Sentence denotes Our conceptual framework can be applied to other cities/countries, or be built into one multiple-patch model for modelling multiple cities/countries context.
T144 2309-2412 Sentence denotes Our model can be fitted to daily data when more information (e.g., daily number of tests) is available.

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
126 181-190 Disease denotes pneumonia MESH:D011014
127 285-293 Disease denotes COVID-19 MESH:C000657245
128 366-370 Disease denotes SARS MESH:D045169
129 515-524 Disease denotes pneumonia MESH:D011014
130 596-605 Disease denotes infection MESH:D007239
131 684-692 Disease denotes COVID-19 MESH:C000657245
133 845-853 Disease denotes zoonotic MESH:D015047
136 1799-1807 Disease denotes COVID-19 MESH:C000657245
137 1914-1922 Disease denotes infected MESH:D007239
139 2125-2133 Disease denotes COVID-19 MESH:C000657245