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: Lithuania
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% |
-664256 |
-20.0 |
-47.3 |
-1772 |
-16.2 |
| 25.0% |
-197356 |
-5.3 |
4.2 |
-195 |
-1.5 |
| 50.0% |
-31209 |
-0.9 |
27.9 |
654 |
5.1 |
| 75.0% |
87252 |
2.2 |
46.3 |
1902 |
14.1 |
| 97.5% |
529677 |
14.1 |
71.8 |
5613 |
32.4 |