PMC:7033348 / 18520-19866 JSONTXT

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T1","span":{"begin":703,"end":708},"obj":"Body_part"}],"attributes":[{"id":"A1","pred":"uberon_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/UBERON_0002542"}],"text":"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. This can, in part, be attributed to the smaller case counts and smaller initial prediction interval range seen in other provinces. 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). 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. Therefore, it is not necessarily surprising that estimates from earlier dates, specifically prior to saturation, yield predictions with higher uncertainty.\nFig. 4 15-day ahead GLM forecasts of cumulative reported 2019-nCoV cases in China – Hubei and other provinces – generated on February 9, 2020.\nFig. 5 15-day ahead Richards forecasts of cumulative reported 2019-nCoV cases in China – Hubei and other provinces – generated on February 9, 2020.\nFig. 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."}

{"project":"LitCovid-sentences","denotations":[{"id":"T122","span":{"begin":0,"end":204},"obj":"Sentence"},{"id":"T123","span":{"begin":205,"end":335},"obj":"Sentence"},{"id":"T124","span":{"begin":336,"end":530},"obj":"Sentence"},{"id":"T125","span":{"begin":531,"end":741},"obj":"Sentence"},{"id":"T126","span":{"begin":742,"end":897},"obj":"Sentence"},{"id":"T127","span":{"begin":898,"end":1040},"obj":"Sentence"},{"id":"T128","span":{"begin":1041,"end":1188},"obj":"Sentence"},{"id":"T129","span":{"begin":1189,"end":1346},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"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. This can, in part, be attributed to the smaller case counts and smaller initial prediction interval range seen in other provinces. 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). 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. Therefore, it is not necessarily surprising that estimates from earlier dates, specifically prior to saturation, yield predictions with higher uncertainty.\nFig. 4 15-day ahead GLM forecasts of cumulative reported 2019-nCoV cases in China – Hubei and other provinces – generated on February 9, 2020.\nFig. 5 15-day ahead Richards forecasts of cumulative reported 2019-nCoV cases in China – Hubei and other provinces – generated on February 9, 2020.\nFig. 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."}