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: Saudi Arabia
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% |
-4516083 |
-25.6 |
-60.5 |
-1515 |
-13.2 |
| 25.0% |
237338 |
1.7 |
31.2 |
130 |
1.2 |
| 50.0% |
1099684 |
8.6 |
51.1 |
1238 |
10.1 |
| 75.0% |
2280292 |
21.7 |
59.7 |
2841 |
24.7 |
| 97.5% |
4502485 |
44.9 |
72.1 |
5889 |
45.8 |