Using statistics and mathematical modelling to understand infectious disease outbreaks:COVID-19 as an example  

作　　者：Christopher E.Overton Helena B.Stage Shazaad Ahmad Jacob Curran-Sebastian Paul Dark Rajenki Das Elizabeth Fearon Timothy Felton Martyn Fyles Nick Gent Ian Hall Thomas House Hugo Lewkowicz Xiaoxi Pang Lorenzo Pellis Robert Sawko Andrew Ustianowski Bindu Vekaria Luke Webb 

机构地区：[1]Department of Mathematics,University of Manchester,UK [2]IBM Research,Hartree Centre,SciTech Daresbury,UK [3]Department of Global Health and Development,London School of Hygiene and Tropical Medicine,UK [4]Emergency Response Department,Public Health England,UK [5]Division of Infection,Immunity and Respiratory Medicine,NIHR Biomedical Research Centre,University of Manchester,UK [6]Department of Virology,Manchester Medical Microbiology Partnership,Manchester Foundation Trust,UK [7]Regional Infectious Diseases Unit,North Manchester General Hospital,UK [8]School of Medical Sciences,University of Manchester,UK [9]Intensive Care Unit,Wythenshawe Hospital,Manchester University NHS Foundation Trust,UK [10]Department of Health Sciences,University of Manchester,UK [11]Department of Mathematical Sciences,University of Liverpool,UK [12]The Alan Turing Institute,UK [13]Manchester Academic Health Sciences Centre,UK [14]Critical Care Unit,Salford Royal Hospital,Northern Care Alliance NHS Group,UK

出　　处：《Infectious Disease Modelling》2020年第1期409-441,共33页传染病建模（英文）

摘　　要：During an infectious disease outbreak,biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy.Motivated by the ongoing response to COVID-19,we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions.In particular,we focus on parameter estimation in the presence of known biases in the data,and the effect of non-pharmaceutical interventions in enclosed subpopulations,such as households and care homes.We illustrate these methods by applying them to the COVID-19 pandemic.

关 键 词：COVID-19 Epidemic modelling Parameter estimation OUTBREAK BIAS INTERVENTION 

分 类 号：R563.1[医药卫生—呼吸系统] R18[医药卫生—内科学]