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

Infectious Disease Modelling 2023年第1期8卷 212-227页

作者： David Wu helen petous.s.harris.janine paynter Vinod suresh Oliver J.Maclaren Department of Engineering science University of AucklandGraftonAuckland1010New Zealand Department of General Practice and Primary Health Care University of AucklandGraftonAuckland1023New Zealand Auckland Bioengineering Institute University of AucklandGraftonAuckland1010New Zealand

Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a *** equations are often used to model an epidemic outbreak's behaviour but are challenging to ***,these models can suffer from misspecification,which biases predictions and parameter *** models can help with misspecification but are even more expensive to simulate and perform inference ***,we develop an explicitly likelihood-based variation of the generalised profiling method as a tool for prediction and inference under model *** approach allows us to carry out identifiability analysis and uncertainty quantification using profile likelihood-based methods without the need for *** provide justification for this approach by introducing a new interpretation of the model approximation component as a stochastic *** preserves the rationale for using profiling rather than integration to remove nuisance parameters while also providing a link back to stochastic *** applied an initial version of this method during an outbreak of measles in samoa in 2019e2020 and found that it achieved relatively fast,accurate *** 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

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