This paper is concerned with the application of recent statistical advances to inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Hamiltonian Monte Carlo and variational inference as implemented in the freely available Stan software. We apply the two methods to real data from outbreaks as well as routinely collected observations. Our results suggest that both inference methods are computationally feasible in this context, and show a trade-off between statistical efficiency versus computational speed. The latter appears particularly relevant for real-time applications.
Bibliographical noteFunding Information:
The authors thank the UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) for funding (grant HPRU-2012-10080). AC acknowledges that part of this research is co-financed by Greece and the European Union (European Social Fund ? ESF) through the Operational Programme ?Human Resources Development, Education and Lifelong Learning? in the context of the project ?Strengthening Human Resources Research Potential via Doctorate Research? (MIS-5000432), implemented by the State Scholarships Foundation (IKY)?; ND acknowledges support from the Athens University of Economics and Business? Research Centre Action: ?Original Scientific Publications?; OR from the NIH (grant number 1R01AI127232-01) and the Bill & Melinda GatesFoundation (OPP1175094); MB thanks the MRC Centre for Global Infectious Disease Analysis (grant MR/R015600/1).
© 2019 The Authors
- Automatic differentiation variational inference
- Epidemic models
- Hamiltonian Monte Carlo
- No-U-turn sampler