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arXiv · 2202.09948

Dynamic Survival Analysis for non-Markovian Epidemic Models

Francesco Di Lauro·Wasiur R. KhudaBukhsh·Istvan Z. Kiss·Eben Kenah·Max Jensen·Grzegorz A. Rempala
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Abstract

We present a new method for analyzing stochastic epidemic models under minimal assumptions. The method, dubbed DSA, is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of PDE 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 the FMD in the United Kingdom and the COVID-19 in India show good accuracy and confirm method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modeling, analyzing and interpreting epidemic data with the help of the DSA approach.

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Francesco Di Lauro, Wasiur R. KhudaBukhsh, Istvan Z. Kiss, Eben Kenah, Max Jensen, Grzegorz A. Rempala. 2022-02-21. Dynamic Survival Analysis for non-Markovian Epidemic Models. https://arxiv.org/abs/2202.09948

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