Institut für Physik, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
PLoS One. 2023 Jul 10;18(7):e0287932. doi: 10.1371/journal.pone.0287932. eCollection 2023.
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.
我们在从小世界集合中抽取的接触网络上对易感染-感染-恢复（SIR）模型的疾病传播进行了数值模拟。我们研究了两种疫苗接种策略，即随机接种和高度数启发式策略，对感染人数累积数量 C 的概率密度函数（pdf）的影响，C 的支持范围很大。为了获得即使在概率小至 10-80 的范围内的 pdf，我们应用了大偏差方法，特别是 1/t Wang-Landau 算法。为了在大偏差理论的框架内研究 pdf 的尺寸依赖性，我们分析了经验速率函数。为了找出轻度或重度感染的典型和极端情况，我们研究了在观察到的 C 值条件下的时间序列结构。
