Large-deviations of disease spreading dynamics with vaccination

We numerically simulated the spread of disease for a Susceptible-Infected-Recovered (SIR) model on contact networks drawn from a small-world ensemble. We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density f...

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Bibliographic Details
Published in:PloS one 2023-07, Vol.18 (7), p.e0287932-e0287932
Main Authors: Feld, Yannick, Hartmann, Alexander K
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biology and Life Sciences
Computer and Information Sciences
Deviation
Disease
Disease susceptibility
Disease Susceptibility - epidemiology
Disease transmission
Distribution (Probability theory)
Empirical analysis
Epidemics
Health aspects
Humans
Likelihood Functions
Medical research
Medicine and Health Sciences
Medicine, Experimental
Models, Biological
Physical Sciences
Prevention
Probability
Probability density function
Probability density functions
Research and Analysis Methods
Statistical physics
Vaccination
Vaccines
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Summary:We numerically simulated the spread of disease for a Susceptible-Infected-Recovered (SIR) model on contact networks drawn from a small-world ensemble. We investigated the impact of two types of vaccination strategies, namely random vaccination and high-degree heuristics, on the probability density function (pdf) of the cumulative number C of infected people over a large range of its support. To obtain the pdf even in the range of probabilities as small as 10-80, we applied a large-deviation approach, in particular the 1/t Wang-Landau algorithm. To study the size-dependence of the pdfs within the framework of large-deviation theory, we analyzed the empirical rate function. To find out how typical as well as extreme mild or extreme severe infection courses arise, we investigated the structures of the time series conditioned to the observed values of C.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0287932