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: Myanmar
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% |
-8262006 |
-46.6 |
-147.0 |
-6023 |
-31.2 |
| 25.0% |
-1569522 |
-7.6 |
-30.3 |
-451 |
-2.2 |
| 50.0% |
1980496 |
8.8 |
12.2 |
3248 |
12.9 |
| 75.0% |
6482995 |
27.0 |
45.0 |
9342 |
34.4 |
| 97.5% |
23365374 |
63.9 |
83.2 |
35195 |
67.7 |