Journal
EPIDEMICS
Volume 30, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.epidem.2019.100383
Keywords
Inference; Infectious disease model; Bayesian; Frequentist; Model fitting
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Funding
- Wellcome Trust Senior Research Fellowship in Biomedical Sciences [210758/Z/18/Z]
- UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Modelling Methodology at Imperial College London
- Public Health England (PHE) [HPRU-2012-10080]
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Inference using mathematical models of infectious disease dynamics can be an invaluable tool for the interpretation and analysis of epidemiological data. However, researchers wishing to use this tool are faced with a choice of models and model types, simulation methods, inference methods and software packages. Given the multitude of options, it can be challenging to decide on the best approach. Here, we delineate the choices and trade-offs involved in deciding on an approach for inference, and discuss aspects that might inform this decision. We provide examples of inference with a dataset of influenza cases using the R packages pomp and rbi.
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