How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

Mrinank Sharma*, Sören Mindermann, Jan Brauner, Gavin Leech, Anna Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems
Publication statusE-pub ahead of print - 2020

Structured keywords

  • Covid19
  • Interactive Artificial Intelligence CDT


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