Research map / Probabilistic and scientific modeling / Causal discovery
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add transmission noise (ablation) | NPI effectiveness models · 6-country holdout test set | predictive log-likelihood | Helped |
| add transmission noise (ablation) | NPI effectiveness models · NPI leave-outs and additional unobserved NPIs | sensitivity to unobserved factors | Helped |
| use a discrete renewal infection process instead of constant exponential growth (ablation) | Default model · NPI leave-outs and additional unobserved NPIs | sensitivity to unobserved factors | Hurt |
| constrain total NPI effectiveness using additive effects (ablation) | Default model · NPI leave-outs and additional unobserved NPIs | sensitivity to unobserved factors | Helped |
| vary model structure, input data, and epidemiological parameters (ablation) | Models with transmission noise · COVID-19 NPI effectiveness estimates | robustness of estimated effects | No clear effect |
| close schools and universities in conjunction | NPI effectiveness models · COVID-19 transmission | estimated NPI effectiveness | Helped |
| implement stay-at-home orders | NPI effectiveness models · COVID-19 transmission | estimated NPI effectiveness | No clear effect |
| close most nonessential businesses when some businesses are already closed | Multiplicative-effect models · COVID-19 transmission | marginal NPI effectiveness | No clear effect |
| set a smaller maximum gathering size | NPI effectiveness models · COVID-19 transmission | estimated gathering-ban effectiveness | Helped |
| interpret NPI effects as average marginal effects over contexts in which each NPI was active | NPI effectiveness estimates · COVID-19 NPI analysis | interpretability | Helped |
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