论文标题
一个计算框架,用于评估流动性在流行过程中传播的作用
A computational framework for evaluating the role of mobility on the propagation of epidemics on point processes
论文作者
论文摘要
本文的重点是以同质性泊松点过程模型的欧几里得平面模型的人群中的SIS(易感性感染)流行动力学(也称为接触过程),其中易感个体的感染率与周围椎间盘感染的个体的数量成正比。本文的主要重点是一个模型,其中点也会受到一些随机运动。掌握矩措施的保护方程式,以分析感染和易感人群的点过程的固定状态。然后提出了第三刻测量的启发式分解,以获得简单的多项式方程,允许一个人在稳态处于感染个体的比例中得出封闭形式的近似值。这些多项式方程还导致了一个相图,该相位图暂时描述了参数空间的区域(种群密度,感染半径,感染和恢复速率以及运动速率),其中流行病得以幸存和灭绝的地方。该相图的钥匙是,流行病的灭绝并不总是受到运动速率的降低的帮助。这些结果通过大型二维摩托车上的模拟来证实。这些模拟表明,当多项式方程能够准确预测流行病生存时感染个体的比例。模拟还表明,所提出的相图准确地预测了参数区域,其中流行病的平均存活时间随运动速率而增加(分别降低)。
This paper is focused on SIS (Susceptible-Infected-Susceptible) epidemic dynamics (also known as the contact process) on populations modelled by homogeneous Poisson point processes of the Euclidean plane, where the infection rate of a susceptible individual is proportional to the number of infected individuals in a disc around it. The main focus of the paper is a model where points are also subject to some random motion. Conservation equations for moment measures are leveraged to analyze the stationary regime of the point processes of infected and susceptible individuals. A heuristic factorization of the third moment measure is then proposed to obtain simple polynomial equations allowing one to derive closed form approximations for the fraction of infected individuals in the steady state. These polynomial equations also lead to a phase diagram which tentatively delineates the regions of the space of parameters (population density, infection radius, infection and recovery rate, and motion rate) where the epidemic survives and those where there is extinction. A key take-away from this phase diagram is that the extinction of the epidemic is not always aided by a decrease in the motion rate. These results are substantiated by simulations on large two dimensional tori. These simulations show that the polynomial equations accurately predict the fraction of infected individuals when the epidemic survives. The simulations also show that the proposed phase diagram accurately predicts the parameter regions where the mean survival time of the epidemic increases (resp. decreases) with motion rate.