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We present a new method for analyzing stochastic epidemic models underminimal assumptions. The method, dubbed DSA, is based on a simple yet powerfulobservation, namely that population-level mean-field trajectories described bya system of PDE may also approximate individual-level times of infection andrecovery. This idea gives rise to a certain non-Markovian agent-based model andprovides an agent-level likelihood function for a random sample of infectionand/or recovery times. Extensive numerical analyses on both synthetic and realepidemic data from the FMD in the United Kingdom and the COVID-19 in India showgood accuracy and confirm method's versatility in likelihood-based parameterestimation. The accompanying software package gives prospective users apractical tool for modeling, analyzing and interpreting epidemic data with thehelp of the DSA approach.

Dynamic Survival Analysis for non-Markovian Epidemic Models