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: SA2: Without Google mobility data
- 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% |
-496728 |
-18.5 |
-43.1 |
-1843 |
-18.3 |
| 25.0% |
-126225 |
-3.8 |
-0.2 |
-365 |
-3.2 |
| 50.0% |
-25214 |
-0.7 |
8.6 |
202 |
1.6 |
| 75.0% |
48908 |
1.4 |
26.7 |
987 |
8.0 |
| 97.5% |
461078 |
10.6 |
54.8 |
3764 |
22.2 |