Dynamic survival analysis for non-Markovian epidemic models

Jensen, Max and Kiss, istvan (2022) Dynamic survival analysis for non-Markovian epidemic models. Journal of the Royal Society Interface. ISSN 1742-5662 (Accepted)

[img] PDF - Accepted Version
Available under License Creative Commons Attribution.

Download (935kB)

Abstract

We present a new method for analyzing stochastic epidemic models under minimal assumptions. The method, dubbed Dynamic Survival Analysis (DSA), is based on a simple yet powerful observation, namely that populationlevel mean-field trajectories described by a system of Partial Differential Equations (PDEs) 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 Foot-and-Mouth Disease (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.

Item Type: Article
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
Research Centres and Groups: Numerical Analysis and Scientific Computing Research Group
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 23 Jun 2022 17:33
Last Modified: 24 Jun 2022 07:01
URI: http://sro.sussex.ac.uk/id/eprint/106583

View download statistics for this item

📧 Request an update