Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal
- Resource Type
- Conference
- Authors
- Rosato, Conor; Harris, John; Panovska-Griffiths, Jasmina; Maskell, Simon
- Source
- 2022 25th International Conference on Information Fusion (FUSION) Information Fusion (FUSION), 2022 25th International Conference on. :1-8 Jul, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Parameter estimation
Infectious diseases
Sociology
Stochastic processes
Information filters
Particle filters
State-space methods
Differentiable particle filter
Particle-MCMC
gradients
NUTS
epidemics
- Language
State-space models have been widely used to model the dynamics of communicable diseases in populations of interest by fitting to time-series data. Particle filters have enabled these models to incorporate stochasticity and so can better reflect the true nature of population behaviours. Relevant parameters such as the spread of the disease, R t , and recovery rates can be inferred using Particle MCMC. The standard method uses a Metropolis-Hastings random-walk proposal which can struggle to reach the stationary distribution in a reasonable time when there are multiple parameters. In this paper we obtain full Bayesian parameter estimations using gradient information and the No U-Turn Sampler (NUTS) when proposing new parameters of stochastic non-linear Susceptible-Exposed-Infected-Recovered (SEIR) and SIR models. Although NUTS makes more than one target evaluation per iteration, we show that it can provide more accurate estimates in a shorter run time than Metropolis-Hastings.