Dynamic Survival Analysis for non-Markovian Epidemic Models
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.
Cite
@article{arxiv.2202.09948,
title = {Dynamic Survival Analysis for non-Markovian Epidemic Models},
author = {Francesco Di Lauro and Wasiur R. KhudaBukhsh and Istvan Z. Kiss and Eben Kenah and Max Jensen and Grzegorz A. Rempala},
journal= {arXiv preprint arXiv:2202.09948},
year = {2022}
}