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: 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% |
-8225646 |
-32.7 |
-109.0 |
-4901 |
-20.9 |
| 25.0% |
203472 |
0.7 |
-11.7 |
2221 |
9.2 |
| 50.0% |
4525047 |
17.1 |
25.5 |
8396 |
29.6 |
| 75.0% |
9127485 |
36.4 |
50.5 |
17639 |
47.8 |
| 97.5% |
24799084 |
68.4 |
80.6 |
43758 |
74.5 |