Dynamic survival analysis for non-Markovian epidemic models

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.

Automated summary

This article presents a new method called dynamic survival analysis (DSA) for analyzing stochastic epidemic models. The method utilizes a simple yet powerful observation that approximates population-level trajectories with individual-level times of infection and recovery. Extensive numerical analyses confirm the accuracy and versatility of the method in analyzing epidemic data and estimating parameters. The accompanying software package provides a practical tool for users.