ArticlePhysical review letters2016
Epidemic Extinction and Control in Heterogeneous Networks.
Article in Physical review letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Stochastic SIRS models on networks: mean and variance of infection.Infectious Disease Modelling · 2025Article
- Amplification of the power of network hubs and degree skewness over infectious disease spread during lulls.PloS one · 2025Article
- Article
- Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations.Scientific reports · 2019Article
- Degree Dispersion Increases the Rate of Rare Events in Population Networks.Physical review letters · 2019Article
- Persistence time of SIS infections in heterogeneous populations and networks.Journal of mathematical biology · 2018Article
- A paradox of epidemics between the state and parameter spaces.Scientific reports · 2018Article
- Large order fluctuations, switching, and control in complex networks.Scientific reports · 2017Article
- Hamiltonian Analysis of Subcritical Stochastic Epidemic Dynamics.Computational and mathematical methods in medicine · 2017Article
- Hysteresis loop of nonperiodic outbreaks of recurrent epidemics.Physical review. E · 2016Article
- Network inoculation: Heteroclinics and phase transitions in an epidemic model.Chaos (Woodbury, N.Y.) · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We consider epidemic extinction in finite networks with a broad variation in local connectivity. Generalizing the theory of large fluctuations to random networks with a given degree distribution, we are able to predict the most probable, or optimal, paths to extinction in various configurations, including truncated power laws. We find that paths for heterogeneous networks follow a limiting form in which infection first decreases in low-degree nodes, which triggers a rapid extinction in high-degree nodes, and finishes with a residual low-degree extinction. The usefulness of our approach is further demonstrated through optimal control strategies that leverage the dependence of finite-size fluctuations on network topology. Interestingly, we find that the optimal control is a mix of treating both high- and low-degree nodes based on theoretical predictions, in contrast to methods that ignore dynamical fluctuations.
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Registered trials
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