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: Myanmar
Selected analysis: SA1: Increased household transmission during closures
- 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% |
-9781793 |
-47.0 |
-151.6 |
-7346 |
-39.5 |
| 25.0% |
-1368815 |
-5.7 |
-33.4 |
-994 |
-4.4 |
| 50.0% |
3063079 |
12.2 |
8.7 |
4790 |
17.5 |
| 75.0% |
8503502 |
31.8 |
41.7 |
13049 |
39.7 |
| 97.5% |
26522317 |
69.3 |
82.9 |
41420 |
71.1 |