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: 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% |
11273202 |
25.3 |
71.1 |
52709 |
56.8 |
| 25.0% |
14457434 |
37.4 |
83.8 |
80538 |
67.9 |
| 50.0% |
16709790 |
43.2 |
87.4 |
101065 |
73.1 |
| 75.0% |
19619572 |
49.0 |
90.7 |
128684 |
78.4 |
| 97.5% |
32199409 |
64.8 |
94.0 |
190971 |
85.3 |