Quantifying Uncertainty in Mechanistic Models of Infectious Disease

被引:8
|
作者
McGowan, Lucy D'Agostino [1 ]
Grantz, Kyra H. [2 ]
Murray, Eleanor [3 ]
机构
[1] Wake Forest Univ, Dept Math & Stat, 127 Manchester Hall,Box 7388, Winston Salem, NC 27109 USA
[2] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD USA
[3] Boston Univ, Sch Publ Hlth, Dept Epidemiol, Boston, MA 02215 USA
关键词
infectious disease modeling; mechanistic models; Monte Carlo simulation; SARS-CoV-2; sensitivity analyses; statistics; uncertainty; TRANSMISSION; ROTAVIRUS; PARAMETER;
D O I
10.1093/aje/kwab013
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, R-0, for SARS-CoV-2.
引用
下载
收藏
页码:1377 / 1385
页数:9
相关论文
共 50 条
  • [21] Quantifying Sources of Uncertainty for Creep Models under Varying Stresses
    Keitel, Holger
    JOURNAL OF STRUCTURAL ENGINEERING, 2013, 139 (06) : 949 - 956
  • [22] Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models
    van Oijen, Marcel
    CURRENT FORESTRY REPORTS, 2017, 3 (04): : 269 - 280
  • [23] Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models
    Marcel van Oijen
    Current Forestry Reports, 2017, 3 : 269 - 280
  • [24] Quantifying demographic uncertainty: Bayesian methods for integral projection models
    Elderd, Bret D.
    Miller, Tom E. X.
    ECOLOGICAL MONOGRAPHS, 2016, 86 (01) : 125 - 144
  • [25] Quantifying uncertainty for temperature maps derived from computer models
    Paci, Lucia
    Gelfand, Alan E.
    Cocchi, Daniela
    SPATIAL STATISTICS, 2015, 12 : 96 - 108
  • [26] Bayesian synthesis for quantifying uncertainty in predictions from process models
    Green, EJ
    MacFarlane, DW
    Valentine, HT
    TREE PHYSIOLOGY, 2000, 20 (5-6) : 415 - 419
  • [27] QUANTIFYING UNCERTAINTY FOR PLANAR PSEUDO-RIGID BODY MODELS
    Lusk, Craig P.
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE - 2011, VOL 6, PTS A AND B, 2012, : 67 - 73
  • [28] Quantifying uncertainty in nanofiltration transport models for enhanced metals recovery
    Rehman, Danyal
    Sheriff, Fareed
    Lienhard, John H.
    WATER RESEARCH, 2023, 243
  • [29] Quantifying Uncertainty of Deep Learning Models for the Segmentation of Geographic Atrophy
    Spaide, Theodore
    Owen, Julia
    De Sisternes, Luis
    Lewis, Warren
    Manivannan, Niranchana
    Pramil, Varsha
    Sheikh, Harris Asad
    Waheed, Nadia K.
    Lee, Cecilia S.
    Lee, Aaron Y.
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2023, 64 (08)
  • [30] MARKOV RANDOM FIELD MODELS FOR QUANTIFYING UNCERTAINTY IN SUBSURFACE REMEDIATION
    Kaluza, M. Clara De Paolis
    Miller, Eric L.
    Abriola, Linda M.
    2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, : 4296 - 4299