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: Thailand
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% |
7171103 |
18.1 |
60.3 |
30754 |
42.9 |
| 25.0% |
9389565 |
29.3 |
76.4 |
49218 |
56.4 |
| 50.0% |
11308120 |
34.3 |
81.0 |
62551 |
62.3 |
| 75.0% |
13700835 |
41.2 |
85.3 |
82051 |
69.5 |
| 97.5% |
23005265 |
54.7 |
90.9 |
131607 |
78.9 |