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

被引：0
|
作者：
Sharma, Mrinank [1 ,2 ]
Mindermann, Soren
Brauner, Jan M. [3 ]
Leech, Gavin [4 ]
Stephenson, Anna B. [5 ]
Gavenciak, Tomas
Kulveit, Jan [6 ]
Teh, Yee Whye [1 ]
Chindelevitch, Leonid [7 ,8 ]
Gal, Yarin [3 ]
机构：
[1] Univ Oxford, Dept Stat, Oxford, England
[2] Univ Oxford, Dept Engn Sci, Oxford, England
[3] Univ Oxford, Dept Comp Sci, OATML Grp, Oxford, England
[4] Univ Bristol, Dept Comp Sci, Bristol, England
[5] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA USA
[6] Univ Oxford, Future Human Inst, Oxford, England
[7] MRC Ctr Global Infect Dis Anal, London, England
[8] Imperial Coll London, Sch Publ Hlth, Abdul Latif Jameel Inst Dis & Emergency Analyt J, London, England
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020 | 2020年 / 33卷
基金：
英国医学研究理事会; 英国工程与自然科学研究理事会;
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 that 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 remarkably robust across these variables. Finally, we mathematically ground the interpretation of NPI effectiveness estimates when certain common assumptions do not hold.
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页数：12
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