Large-deviations of disease spreading dynamics with vaccination

PLoS One. 2023 Jul 10;18(7):e0287932. doi: 10.1371/journal.pone.0287932. eCollection 2023.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Disease Susceptibility / epidemiology
  • Epidemics*
  • Humans
  • Likelihood Functions
  • Models, Biological
  • Vaccination

Grants and funding

The simulations were performed at the HPC Cluster CARL, located at the University of Oldenburg (Germany) and funded by the DFG through its Major Research Instrumentation Program (INST 184/157-1 FUGG) and the Ministry of Science and Culture (MWK) of the Lower Saxony State. This work also used the Scientific Compute Cluster at GWDG, the joint data center of Max Planck Society for the Advancement of Science (MPG) and University of Göttingen. Y. Feld has been financially supported by the German Academic Scholarship Foundation (Studienstiftung des Deutschen Volkes). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.