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Title: 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.  more » « less
Award ID(s):
2027001
PAR ID:
10421175
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Journal of The Royal Society Interface
Volume:
19
Issue:
191
ISSN:
1742-5662
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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