How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
| Autor: | Mrinank Sharma, Sören Mindermann, Jan Brauner, Gavin Leech, Anna Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 1702 Cognitive Sciences Populations and Evolution (q-bio.PE) Machine Learning (stat.ML) Statistics - Applications Quantitative Biology - Quantitative Methods Machine Learning (cs.LG) Statistics - Machine Learning 1701 Psychology FOS: Biological sciences Applications (stat.AP) Quantitative Biology - Populations and Evolution Quantitative Methods (q-bio.QM) |
| Zdroj: | Neural Information Processing Systems (NeurIPS 2020) University of Bristol-PURE |
| DOI: | 10.48550/arxiv.2007.13454 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|