Dynamic survival analysis for non-Markovian epidemic models

Di Lauro, Francesco; KhudaBukhsh, Wasiur R.; Kiss, István Z.; Kenah, Eben; Jensen, Max; Rempała, Grzegorz A.

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.

Citation

Di Lauro, F., KhudaBukhsh, W. R., Kiss, I. Z., Kenah, E., Jensen, M., & Rempała, G. A. (2022). Dynamic survival analysis for non-Markovian epidemic models. Journal of the Royal Society. Interface, 19(191), Article 20220124. https://doi.org/10.1098/rsif.2022.0124

• Journal Article Type: Article
• Acceptance Date: May 3, 2022
• Online Publication Date: Jun 1, 2022
• Publication Date: Jun 1, 2022
• Deposit Date: Jun 2, 2022
• Publicly Available Date: Jun 6, 2022
• Journal: Journal of The Royal Society Interface
• Print ISSN: 1742-5689
• Electronic ISSN: 1742-5662
• Publisher: The Royal Society
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 191
• Article Number: 20220124
• DOI: https://doi.org/10.1098/rsif.2022.0124
• Keywords: Biomedical Engineering; Biochemistry; Biomaterials; Bioengineering; Biophysics; Biotechnology
• Public URL: https://nottingham-repository.worktribe.com/output/8309562
• Publisher URL: https://royalsocietypublishing.org/doi/10.1098/rsif.2022.0124