Truscott J E, Gilligan C A
Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, United Kingdom.
Proc Natl Acad Sci U S A. 2003 Jul 22;100(15):9067-72. doi: 10.1073/pnas.1436273100. Epub 2003 Jul 11.
Fluctuations in the natural environment introduce variability into the biological systems that exist within them. In this paper, we develop a model for the influence of random fluctuations in the environment on a simple epidemiological system. The model describes the infection of a dynamic host population by an environmentally sensitive pathogen and is based on the infection of sugar beet plants by the endoparasitic slime-mold vector Polymyxa betae. The infection process is switched on only when the temperature is above a critical value. We discuss some of the problems inherent in modeling such a system and analyze the resulting model by using asymptotic techniques to generate closed-form solutions for the mean and variance of the net amount of new inoculum produced within a season. In this way, the variance of temperature profile can be linked with that of the inoculum produced in a season and hence the risk of disease. We also examine the connection between the model developed in this paper and discrete Markov-chain models for weather.
自然环境的波动会给其中存在的生物系统带来变异性。在本文中，我们构建了一个模型，用于研究环境中的随机波动对一个简单流行病学系统的影响。该模型描述了一种对环境敏感的病原体对动态宿主种群的感染，其基于内寄生黏菌载体甜菜多黏菌对甜菜植株的感染。只有当温度高于临界值时，感染过程才会开启。我们讨论了对这样一个系统进行建模所固有的一些问题，并通过使用渐近技术对一个季节内产生的新接种体净量的均值和方差生成封闭形式的解来分析所得模型。通过这种方式，温度分布的方差可以与一个季节内产生的接种体的方差联系起来，进而与疾病风险联系起来。我们还研究了本文所开发的模型与天气的离散马尔可夫链模型之间的联系。
