Selected article for: "prediction interval and relatively stable remain"

Author: Roosa, K.; Lee, Y.; Luo, R.; Kirpich, A.; Rothenberg, R.; Hyman, J.M.; Yan, P.; Chowell, G.
Title: Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020
  • Document date: 2020_2_14
  • ID: 0zw3ukpx_33
    Snippet: 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 Fig. 3 . Forecasting results for 15-days ahead estimates, generated daily from Feb.....
    Document: 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 Fig. 3 . Forecasting results for 15-days ahead estimates, generated daily from February 5e9, 2020, of cumulative reported cases in Hubei (a) and other provinces (b). The mean case estimate is represented by the dots, while the lines represent the 95% prediction intervals for each model. uncertainty remain relatively stable in other provinces though, while the mean estimates of 10 and 15 days ahead decrease significantly in Hubei (Figs. 2 and 3 ). This suggests that the epidemic lasts longer in Hubei compared to other provinces (Figs. 4e6), 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.

    Search related documents: