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.
Back to analysis selection index page
Selected country: Serbia
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% |
-1222923 |
-16.6 |
-29.0 |
-844 |
-4.1 |
| 25.0% |
-69498 |
-0.9 |
15.7 |
2650 |
12.5 |
| 50.0% |
352628 |
4.0 |
37.2 |
4941 |
20.7 |
| 75.0% |
800378 |
8.4 |
56.2 |
7349 |
28.2 |
| 97.5% |
1918198 |
18.4 |
80.2 |
14853 |
43.6 |