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

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 margin-alisation. We provide justification for this approach by introducing a new interpretation of the model approximation component as a stochastic constraint. This preserves the ratio-nale 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 2019-2020 and found that it achieved rela-tively fast, accurate predictions. Here we present the most recent version of our method and its application to this measles outbreak, along with additional validation.(c) 2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Prediction of infectious disease outbreak progression is crucial for planning and coordinating responses. A new modeling method that addresses parameterization difficulties and model misspecification can provide fast and accurate predictions while preserving the characteristics of stochastic models.