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.
机构:
Stanford Univ, Prevent Res Ctr, Stanford, CA 94305 USA
Stanford Univ, Ctr Hlth Policy Primary Care & Outcomes Res, Stanford, CA 94305 USA
Stanford Univ, Ctr Poverty & Inequal, Stanford, CA 94305 USA
London Sch Hyg & Trop Med, Dept Publ Hlth & Policy, London WC1, EnglandStanford Univ, Prevent Res Ctr, Stanford, CA 94305 USA
Basu, Sanjay
Andrews, Jason
论文数: 0引用数: 0
h-index: 0
机构:
Massachusetts Gen Hosp, Div Infect Dis, Boston, MA 02114 USA
Harvard Univ, Sch Med, Boston, MA USAStanford Univ, Prevent Res Ctr, Stanford, CA 94305 USA
机构:
Stanford Univ, Prevent Res Ctr, Stanford, CA 94305 USA
Stanford Univ, Ctr Hlth Policy Primary Care & Outcomes Res, Stanford, CA 94305 USA
Stanford Univ, Ctr Poverty & Inequal, Stanford, CA 94305 USA
London Sch Hyg & Trop Med, Dept Publ Hlth & Policy, London WC1, EnglandStanford Univ, Prevent Res Ctr, Stanford, CA 94305 USA
Basu, Sanjay
Andrews, Jason
论文数: 0引用数: 0
h-index: 0
机构:
Massachusetts Gen Hosp, Div Infect Dis, Boston, MA 02114 USA
Harvard Univ, Sch Med, Boston, MA USAStanford Univ, Prevent Res Ctr, Stanford, CA 94305 USA