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用确定性微分方程和随机初始条件对简单流行病进行建模。

Modeling the simple epidemic with deterministic differential equations and random initial conditions.

作者信息

Kegan Bonnie, West R Webster

机构信息

US Census Bureau, Washington, DC, USA.

出版信息

Math Biosci. 2005 Apr;194(2):217-31. doi: 10.1016/j.mbs.2005.02.002.

Abstract

In a simple epidemic the only transition in the population is from susceptible to infected and the total population size is fixed for all time. This paper investigates the effect of random initial conditions on the deterministic model for the simple epidemic. By assuming a Beta distribution on the initial proportion of susceptibles, we define a distribution that describes the proportion of susceptibles in a population at any time during an epidemic. The mean and variance for this distribution are derived as hypergeometric functions, and the behavior of these functions is investigated. Lastly, we define a distribution to describe the time until a given proportion of the population remains susceptible. A method for finding the quantiles of this distribution is developed and used to make confidence statements regarding the time until a given proportion of the population is susceptible.

摘要

在简单传染病模型中,人群中的唯一转变是从易感者变为感染者,且总人口规模在任何时候都是固定的。本文研究了随机初始条件对简单传染病确定性模型的影响。通过假设易感者初始比例服从贝塔分布,我们定义了一种分布,该分布描述了传染病流行期间任何时刻人群中易感者的比例。此分布的均值和方差被推导为超几何函数,并对这些函数的行为进行了研究。最后,我们定义了一种分布来描述直到给定比例的人群仍保持易感状态所需的时间。开发了一种用于找到该分布分位数的方法,并用于对直到给定比例的人群易感所需的时间做出置信陈述。

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