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: Kenya
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% |
-4940515 |
-18.0 |
-43.7 |
-5190 |
-54.2 |
| 25.0% |
638163 |
2.7 |
38.1 |
-1502 |
-15.1 |
| 50.0% |
4701585 |
19.1 |
56.6 |
785 |
7.0 |
| 75.0% |
10020082 |
36.7 |
70.8 |
3812 |
28.9 |
| 97.5% |
20265006 |
61.3 |
86.5 |
13083 |
56.7 |