Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

被引：15

作者：

Swallow, Ben ^{[1
,2
]}

Birrell, Paul ^{[3
,4
]}

Blake, Joshua ^{[4
]}

Burgman, Mark ^{[5
]}

Challenor, Peter ^{[6
,7
]}

Coffeng, Luc E. ^{[8
]}

Dawid, Philip ^{[9
]}

De Angelis, Daniela ^{[4
,16
]}

Goldstein, Michael ^{[10
]}

Hemming, Victoria ^{[11
]}

Marion, Glenn ^{[2
,12
]}

McKinley, Trevelyan J. ^{[13
,16
]}

Overton, Christopher E. ^{[14
,15
,16
]}

Panovska-Griffiths, Jasmina ^{[17
,18
]}

Pellis, Lorenzo ^{[6
,14
,16
]}

Probert, Will ^{[17
]}

Shea, Katriona ^{[19
]}

Villela, Daniel ^{[20
]}

Vernon, Ian ^{[10
]}

机构：

[1] Univ Glasgow, Sch Math & Stat, Glasgow G12 8QQ, Lanark, Scotland

[2] Scottish COVID 19 Response Consortium, Edinburgh, Midlothian, Scotland

[3] UKHSA, Analyt & Data Sci, Salisbury, Wilts, England

[4] Univ Cambridge, MRC Biostat Unit, Cambridge, England

[5] Imperial Coll London, Ctr Environm Policy, London, England

[6] Alan Turing Inst, London, England

[7] Univ Exeter, Coll Engn Math & Phys Sci, Exeter, Devon, England

[8] Univ Med Ctr Rotterdam, Dept Publ Hlth, Erasmus MC, Rotterdam, Netherlands

[9] Univ Cambridge, Stat Lab, Cambridge, England

[10] Univ Durham, Dept Math Sci, Stockton Rd, Durham, England

[11] Univ British Columbia, Dept Forest & Conservat Sci, Vancouver, BC, Canada

[12] Biomath & Stat Scotland, Edinburgh, Midlothian, Scotland

[13] Univ Exeter, Coll Med & Hlth, Exeter, Devon, England

[14] Univ Manchester, Dept Math, Manchester, Lancs, England

[15] Manchester Univ NHS Fdn Trust, Clin Data Sci Unit, Manchester, Lancs, England

[16] Joint UNIv Pandem & Epidemiol Res, Cambridge, England

[17] Univ Oxford, Big Data Inst, Oxford, England

[18] Univ Oxford, Queen S Coll, Oxford, England

[19] Penn State Univ, Dept Biol & Ctr Infect Dis Dynam, State Coll, PA 16802 USA

[20] Funda Oswaldo Cruz, Program Sci Comp, Rio De Janeiro, Brazil

来源：

EPIDEMICS | 2022年 / 38卷

关键词：

Statistical estimation; Uncertainty quantification; Expert elicitation; Pandemic modelling; APPROXIMATE BAYESIAN COMPUTATION; INFERENCE; KNOWLEDGE;

D O I：

10.1016/j.epidem.2022.100547

中图分类号：

R51 [传染病];

学科分类号：

100401 ;

摘要：

The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of information using data and models to help inform policy. In this paper, we identify and discuss four broad challenges in the estimation paradigm relating to infectious disease modelling, namely the Uncertainty Quantification framework, data challenges in estimation, model-based inference and prediction, and expert judgement. We also postulate priorities in estimation methodology to facilitate preparation for future pandemics.

引用

页数：12

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