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: 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% |
9032860 |
21.4 |
58.8 |
43427 |
52.8 |
| 25.0% |
12298889 |
34.4 |
77.7 |
65105 |
63.4 |
| 50.0% |
14128981 |
40.7 |
82.8 |
81939 |
69.3 |
| 75.0% |
16022587 |
46.7 |
86.7 |
102486 |
74.1 |
| 97.5% |
23970408 |
59.6 |
91.4 |
151596 |
81.0 |