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
收藏
页码:1377 / 1385
页数:9
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