Survival dynamical systems: individual-level survival analysis from population-level epidemic models

KhudaBukhsh, Wasiur R.; Choi, Boseung; Kenah, Eben; Rempa?a, Grzegorz A.

Abstract

In this paper, we show that solutions to ordinary differential equations describing the large-population limits of Markovian stochastic epidemic models can be interpreted as survival or cumulative hazard functions when analysing data on individuals sampled from the population. We refer to the individual-level survival and hazard functions derived from population-level equations as a survival dynamical system (SDS). To illustrate how population-level dynamics imply probability laws for individual-level infection and recovery times that can be used for statistical inference, we show numerical examples based on synthetic data. In these examples, we show that an SDS analysis compares favourably with a complete-data maximum-likelihood analysis. Finally, we use the SDS approach to analyse data from a 2009 influenza A(H1N1) outbreak at Washington State University.

Citation

KhudaBukhsh, W. R., Choi, B., Kenah, E., & Rempała, G. A. (2020). Survival dynamical systems: individual-level survival analysis from population-level epidemic models. Interface Focus, 10(1), Article 20190048. https://doi.org/10.1098/rsfs.2019.0048

• Journal Article Type: Article
• Acceptance Date: Oct 7, 2019
• Online Publication Date: Dec 13, 2019
• Publication Date: Feb 6, 2020
• Deposit Date: Apr 9, 2022
• Publicly Available Date: Apr 13, 2022
• Journal: Interface Focus
• Electronic ISSN: 2042-8901
• Publisher: The Royal Society
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• Article Number: 20190048
• DOI: https://doi.org/10.1098/rsfs.2019.0048
• Keywords: Biomedical Engineering; Biomaterials; Biochemistry; Bioengineering; Biophysics; Biotechnology
• Public URL: https://nottingham-repository.worktribe.com/output/7715599
• Publisher URL: https://royalsocietypublishing.org/doi/10.1098/rsfs.2019.0048