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: Argentina
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% |
1166065 |
1.5 |
-6.5 |
-22927 |
-17.9 |
| 25.0% |
3605485 |
4.4 |
9.7 |
-14466 |
-11.6 |
| 50.0% |
9241683 |
10.6 |
36.0 |
16063 |
9.8 |
| 75.0% |
13395307 |
14.6 |
60.8 |
34487 |
18.7 |
| 97.5% |
20117551 |
24.5 |
84.9 |
65377 |
30.7 |