ArticleJournal of the Royal Society, Interface2022
Dynamic survival analysis for non-Markovian epidemic models.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Non-Markovian dynamics and effective reproduction number in COVID-19: Evidence from Cyprus contact tracing data.PLoS computational biology · 2026Article
- Model-based clustering of time-dependent observations with common structural changes.Statistics and computing · 2026Article
- Compartmental Models Driven by Renewal Processes: Survival Analysis and Applications to SVIS Epidemic Models.Scientific reports · 2025Article
- Article
- Dynamic contagion potential framework for optimizing infection control in healthcare.Frontiers in public health · 2025Article
- Towards Inferring Network Properties from Epidemic Data.Bulletin of mathematical biology · 2023Article
- Projecting COVID-19 cases and hospital burden in Ohio.Journal of theoretical biology · 2023Article
- COVID-19 dynamics in an Ohio prison.Frontiers in public health · 2023Article
- Quantifying the relationship between sub-population wastewater samples and community-wide SARS-CoV-2 seroprevalence.The Science of the total environment · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
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.
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Registered trials
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