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 …

We present a new method for analyzing stochastic epidemic models under minimal
assumptions. The method, dubbed Dynamic Survival Analysis (DSA), is based on a simple
yet powerful observation, namely that populationlevel mean-field trajectories described by a
system of Partial Differential Equations (PDEs) 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 …