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: Indonesia
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% |
6259212 |
2.8 |
-162.1 |
-54482 |
-36.9 |
| 25.0% |
34259583 |
15.9 |
-56.1 |
-24244 |
-15.4 |
| 50.0% |
42429360 |
20.8 |
-18.8 |
-9323 |
-5.2 |
| 75.0% |
50668436 |
26.4 |
11.7 |
13258 |
7.1 |
| 97.5% |
67797306 |
37.9 |
55.5 |
82895 |
33.8 |