How Robust Are the Estimated Effects of Nonpharmaceutical Interventions Against COVID-19?
Abstract
To what extent are effectiveness estimates of nonpharmaceutical interventions (NPIs) against COVID-19 influenced by the assumptions our models make? To answer this question, we investigate 2 state-of-the-art NPI effectiveness models and propose 6 variants that make different structural assumptions. In particular, we investigate how well NPI effectiveness estimates generalise to unseen countries, and their sensitivity to unobserved factors. Models which account for noise in disease transmission compare favourably. We further evaluate how robust estimates are to different choices of epidemiological parameters and data. Focusing on models that assume transmission noise, we find that previously published results are robust across these choices and across different models. Finally, we mathematically ground the interpretation of NPI effectiveness estimates when certain common assumptions do not hold.
Cite
Text
Sharma et al. "How Robust Are the Estimated Effects of Nonpharmaceutical Interventions Against COVID-19?." Neural Information Processing Systems, 2020.Markdown
[Sharma et al. "How Robust Are the Estimated Effects of Nonpharmaceutical Interventions Against COVID-19?." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/sharma2020neurips-robust/)BibTeX
@inproceedings{sharma2020neurips-robust,
title = {{How Robust Are the Estimated Effects of Nonpharmaceutical Interventions Against COVID-19?}},
author = {Sharma, Mrinank and Mindermann, Sören and Brauner, Jan and Leech, Gavin and Stephenson, Anna and Gavenčiak, Tomáš and Kulveit, Jan and Teh, Yee Whye and Chindelevitch, Leonid and Gal, Yarin},
booktitle = {Neural Information Processing Systems},
year = {2020},
url = {https://mlanthology.org/neurips/2020/sharma2020neurips-robust/}
}