Likelihood-based estimation and prediction for a measles outbreak in Samoa  

作　　者：David Wu Helen Petousis-Harris Janine Paynter Vinod Suresh Oliver J.Maclaren 

机构地区：[1]Department of Engineering Science,University of Auckland,Grafton,Auckland,1010,New Zealand [2]Department of General Practice and Primary Health Care,University of Auckland,Grafton,Auckland,1023,New Zealand [3]Auckland Bioengineering Institute,University of Auckland,Grafton,Auckland,1010,New Zealand

出　　处：《Infectious Disease Modelling》2023年第1期212-227,共16页传染病建模（英文）

摘　　要：Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response.Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise.Furthermore,these models can suffer from misspecification,which biases predictions and parameter estimates.Stochastic models can help with misspecification but are even more expensive to simulate and perform inference with.Here,we develop an explicitly likelihood-based variation of the generalised profiling method as a tool for prediction and inference under model mis-specification.Our approach allows us to carry out identifiability analysis and uncertainty quantification using profile likelihood-based methods without the need for marginalisation.We provide justification for this approach by introducing a new interpretation of the model approximation component as a stochastic constraint.This preserves the rationale for using profiling rather than integration to remove nuisance parameters while also providing a link back to stochastic models.We applied an initial version of this method during an outbreak of measles in Samoa in 2019e2020 and found that it achieved relatively fast,accurate predictions.Here we present the most recent version of our method and its application to this measles outbreak,along with additional validation.

关 键 词：Generalised profiling Likelihood-based inference Profile likelihood Parameter estimation Bootstrap MEASLES 

分 类 号：R511[医药卫生—内科学]