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LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 2085-2090 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T70 444-445 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T71 602-603 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T72 3163-3170 http://purl.obolibrary.org/obo/UBERON_0000473 denotes testing
T73 3451-3452 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T74 3588-3590 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T75 3704-3706 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T76 3802-3803 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T77 4350-4351 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T78 4566-4572 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T79 4577-4578 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T80 4649-4650 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T81 5001-5004 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T82 5013-5014 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T15 869-875 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T16 3889-3895 http://purl.obolibrary.org/obo/GO_0040007 denotes growth

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T113 0-10 Sentence denotes Discussion
T114 11-205 Sentence denotes In this report, we provide timely short-term forecasts of the cumulative number of reported cases of the 2019-nCoV epidemic in Hubei province and other provinces in China as of February 9, 2020.
T115 206-359 Sentence denotes As the epidemic continues, we are also publishing online daily 10day ahead forecasts including each of the models presented here (Roosa & Chowell, 2020).
T116 360-584 Sentence denotes Based on the three models calibrated to data up until February 9, 2020, we forecast a cumulative number of reported cases between 37,415 and 38,028 in Hubei Province and 11,588–13,499 in other provinces by February 24, 2020.
T117 585-790 Sentence denotes Our models yield a good visual fit to the epidemic curves, based on residuals, with the sub-epidemic model outperforming the other models in terms of mean squared error (MSE) (Supplemental Tables 1 and 2).
T118 791-944 Sentence denotes Parameter estimation results from the GLM consistently show that the epidemic growth is near exponential in Hubei and sub-exponential in other provinces.
T119 945-1156 Sentence denotes Overall, models predict similar ranges of short-term forecasts, except for those generated on February 5th, where the sub-epidemic model predicts significantly higher case counts than the other two models (Figs.
T120 1157-1162 Sentence denotes 1–3).
T121 1163-1381 Sentence denotes The sub-epidemic model predicts similar ranges to the other models for subsequent dates, so the higher ranges on February 5th may indicate that more data are required to inform the parameters of the sub-epidemic model.
T122 1382-1586 Sentence denotes We observe that the width of the prediction intervals decreases on average as more data are included for forecasts in Hubei; however, this pattern is not obvious for our analysis based on other provinces.
T123 1587-1717 Sentence denotes This can, in part, be attributed to the smaller case counts and smaller initial prediction interval range seen in other provinces.
T124 1718-1912 Sentence denotes Mean predictions and associated uncertainty remain relatively stable in other provinces though, while the mean estimates of 10 and 15 days ahead decrease significantly in Hubei (Fig. 2, Fig. 3).
T125 1913-2123 Sentence denotes This suggests that the epidemic lasts longer in Hubei compared to other provinces (Fig. 4, Fig. 5, Fig. 6 ), which may be attributed to intensive control efforts and large-scale social distancing interventions.
T126 2124-2279 Sentence denotes Therefore, it is not necessarily surprising that estimates from earlier dates, specifically prior to saturation, yield predictions with higher uncertainty.
T127 2280-2422 Sentence denotes Fig. 4 15-day ahead GLM forecasts of cumulative reported 2019-nCoV cases in China – Hubei and other provinces – generated on February 9, 2020.
T128 2423-2570 Sentence denotes Fig. 5 15-day ahead Richards forecasts of cumulative reported 2019-nCoV cases in China – Hubei and other provinces – generated on February 9, 2020.
T129 2571-2728 Sentence denotes Fig. 6 15-day ahead sub-epidemic model forecasts of cumulative reported 2019-nCoV cases in China – Hubei and other provinces – generated on February 9, 2020.
T130 2729-3023 Sentence denotes We retrieve the data from the Chinese media conglomerate Tencent (Chinese National Health Commission); however, the data show small differences in case counts compared to data of the epidemic reported by other sources (Johns Hopkins University Center for Systems Science and Engineering, 2020).
T131 3024-3206 Sentence denotes Importantly, the curves of confirmed cases that we employ in our study are reported according to reporting date and could be influenced by testing capacity and other related factors.
T132 3207-3380 Sentence denotes Further, there may be significant delays in identifying, isolating, and reporting cases in Hubei due to the magnitude of the epidemic, which could influence our predictions.
T133 3381-3517 Sentence denotes Incidence curves according to the date of symptom onset could provide a clearer picture of the transmission dynamics during an epidemic.
T134 3518-3640 Sentence denotes We also note that we analyzed the epidemic curves starting on January 22, 2020, but the epidemic started in December 2019.
T135 3641-3760 Sentence denotes Hence, the first data point accumulates cases up until January 22, 2020, as data were not available prior to this date.
T136 3761-4000 Sentence denotes The 2019-nCoV outbreak in China presents a significant challenge for modelers, as there are limited data available on the early growth trajectory, and epidemiological characteristics of the novel coronavirus have not been fully elucidated.
T137 4001-4300 Sentence denotes Our timely short-term forecasts based on phenomenological models can be useful for real-time preparedness, such as anticipating the required number of hospital beds and other medical resources, as they provide an estimate of the number of cases hospitals will need to prepare for in the coming days.
T138 4301-4453 Sentence denotes In future work, we plan to report the results of a retrospective analysis of forecasting performance across models based on various performance metrics.
T139 4454-4573 Sentence denotes Of note, the case definition changed on February 12, 2020 to count clinical cases that have not been laboratory tested.
T140 4574-4701 Sentence denotes As a result in this change in reporting, the province of Hubei experienced a jump in the nuber of cases on February 13th, 2020.
T141 4702-4825 Sentence denotes This change in reporting will need to be taken into account in order to assess the accuracy of the forecasts reported here.
T142 4826-4961 Sentence denotes In conclusion, our most recent forecasts, based on data for the last three days (February 7th – 9th, 2020), remained relatively stable.
T143 4962-5067 Sentence denotes These models predict that the epidemic has reached a saturation point for both Hubei and other provinces.
T144 5068-5233 Sentence denotes This likely reflects the impact of the wide spectrum of social distancing measures implemented by the Chinese government, which likely helped stabilize the epidemic.
T145 5234-5332 Sentence denotes The forecasts presented are based on the assumption that current mitigation efforts will continue.