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

@article{Cauchemez2008LikelihoodbasedEO,
  title={Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London.},
  author={Simon Cauchemez and Neil M. Ferguson},
  journal={Journal of the Royal Society, Interface},
  year={2008},
  volume={5 25},
  pages={885-97}
}
We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimics the susceptible-infectious-removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models… CONTINUE READING