Dynamic survival analysis for non-Markovian epidemic models

J R Soc Interface. 2022 Jun;19(191):20220124. doi: 10.1098/rsif.2022.0124. Epub 2022 Jun 1.


We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.

Keywords: MCMC methods; parameter inference; spatial epidemic models; survival analysis.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural

MeSH terms

  • Animals
  • COVID-19* / epidemiology
  • Epidemics*
  • Likelihood Functions
  • Prospective Studies
  • Survival Analysis