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: Russian Federation
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% |
-1971061 |
-1.2 |
-10.6 |
9833 |
2.5 |
| 25.0% |
8076446 |
5.7 |
18.7 |
30305 |
6.8 |
| 50.0% |
12047570 |
9.5 |
27.0 |
40632 |
8.9 |
| 75.0% |
15075992 |
12.5 |
34.6 |
49821 |
10.9 |
| 97.5% |
20801302 |
18.0 |
58.4 |
70336 |
14.5 |