This page presents detailed model outputs associated with the following article:
Estimating the impact of school closures on the COVID-19 dynamics in 74 countries: a modelling analysis.
Romain Ragonnet, Angus E Hughes, David S Shipman, Michael T Meehan, Alec S Henderson, Guillaume Briffoteaux, Nouredine Melab, Daniel Tuyttens, Emma S McBryde, James M Trauer.
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Selected country: Nepal
Selected analysis: Base case analysis
- The first panel presents a scenario comparison based on the maximum a-posteriori parameter set.
- The second and third panels present the uncertainty around the estimated epidemic trajectories, as median (black lines), interquartile range (dark shade) and 95% credible interval (light shade).
Relative outcomes
- Positive values indicate a positive effect of school closures on the relevant indicator.
- Negative values indicate that school closures exacerbated the relevant COVID-19 indicator..
|
N infections averted |
% infections averted |
% hospital peak reduction |
N deaths averted |
% deaths averted |
| percentile |
|
|
|
|
|
| 2.5% |
416251 |
2.2 |
24.3 |
419 |
2.2 |
| 25.0% |
5002604 |
23.2 |
64.2 |
7063 |
31.8 |
| 50.0% |
8161584 |
39.1 |
76.0 |
12454 |
48.1 |
| 75.0% |
11486560 |
51.1 |
84.9 |
19771 |
59.0 |
| 97.5% |
15843030 |
69.2 |
91.9 |
35236 |
76.0 |