ArticleInterface focus2020
Survival dynamical systems: individual-level survival analysis from population-level epidemic models.
Article in Interface focus, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Estimating transmission parameters from stochastic epidemic models using survival analysis techniques.Statistical methods in medical research · 2026Article
- Hazard curvature makes within-host variability costly for survival.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Inferring structure and parameters of stochastic reaction networks with logistic regression.PloS one · 2026Article
- Model-based clustering of time-dependent observations with common structural changes.Statistics and computing · 2026Article
- Connecting mass-action models and network models for infectious diseases.PLoS computational biology · 2025Article
- Deterministic epidemic models overestimate the basic reproduction number of observed outbreaks.Infectious Disease Modelling · 2024Article
- 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
- Efficient empirical likelihood inference for recovery rate of COVID19 under double-censoring.Journal of statistical planning and inference · 2022Article
- Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio.medRxiv : the preprint server for health sciences · 2022Article
- Dynamic survival analysis for non-Markovian epidemic models.Journal of the Royal Society, Interface · 2022Article
- Quantifying the Population-Level Effect of the COVID-19 Mass Vaccination Campaign in Israel: A Modeling Study.Open forum infectious diseases · 2022Article
- Analysis of individual-level data from 2018-2020 Ebola outbreak in Democratic Republic of the Congo.Scientific reports · 2022Article
- Combining epidemiological and ecological methods to quantify social effects onRoyal Society open science · 2021Article
- Incorporating age and delay into models for biophysical systems.Physical biology · 2021Article
- Modeling and analysis of different scenarios for the spread of COVID-19 by using the modified multi-agent systems - Evidence from the selected countries.Results in physics · 2021Article
- A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions.Chaos, solitons, and fractals · 2020Article
- Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC.Biomath (Sofia, Bulgaria) · 2019Article
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4 authors.
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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.
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