Choices and trade-offs in inference with infectious disease models

Sebastian Funk; Aaron A King

doi:10.1016/j.epidem.2019.100383

Choices and trade-offs in inference with infectious disease models

Epidemics. 2019 Dec 20:30:100383. doi: 10.1016/j.epidem.2019.100383. Online ahead of print.

Authors

Sebastian Funk¹, Aaron A King²

Affiliations

¹ Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.
² Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA; Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI, USA; Department of Mathematics, University of Michigan, Ann Arbor, MI, USA. Electronic address: sebastian.funk@lshtm.ac.uk.

PMID: 32007792
DOI: 10.1016/j.epidem.2019.100383

Abstract

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.

Keywords: Bayesian; Frequentist; Infectious disease model; Inference; Model fitting.

Grants and funding

210758/Z/18/Z/WT_/Wellcome Trust/United Kingdom