Di Lauro, Francesco; KhudaBukhsh, Wasiur R; Kiss, István Z; Kenah, Eben; Jensen, Max; Rempała, Grzegorz A; (2022) Dynamic survival analysis for non-Markovian epidemic models. Journal of The Royal Society Interface , 19 (191) , Article 20220124. 10.1098/rsif.2022.0124.

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Abstract

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.

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