Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes

Robustness analysis of a hybrid of recursive neural dynamics for online matrix inversion

(2016) Ke Chen et al.   APPLIED MATHEMATICS AND COMPUTATION

Capturing the time-varying drivers of an epidemic using stochastic dynamical systems

(2013) J. Dureau et al.   BIOSTATISTICS

Extracting the time-dependent transmission rate from infection data via solution of an inverse ODE problem

(2012) Mark Pollicott et al.   Journal of Biological Dynamics

Inference in Epidemic Models without Likelihoods

(2009) Trevelyan McKinley et al.  

Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

(2008) S. Cauchemez et al.   Journal of the Royal Society Interface

Estimating the Basic Reproductive Number in the General Epidemic Model with an Unknown Initial Number of Susceptible Individuals

(2008) ERIC H. Y. LAU et al.   SCANDINAVIAN JOURNAL OF STATISTICS

Nonparametric Estimation of Multiplicative Counting Process Intensity Functions with an Application to the Beijing SARS Epidemic

(2007) Feng Chen et al.   COMMUNICATIONS IN STATISTICS-THEORY AND METHODS