Garira Winston, Mathebula Dephney, Netshikweta Rendani
Modelling Health and Environmental Linkages Research Group (MHELRG), C/o Department of Mathematics and Applied Mathematics, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa.
Modelling Health and Environmental Linkages Research Group (MHELRG), C/o Department of Mathematics and Applied Mathematics, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa.
Math Biosci. 2014 Oct;256:58-78. doi: 10.1016/j.mbs.2014.08.004. Epub 2014 Aug 19.
In this study we develop a mathematical modelling framework for linking the within-host and between-host dynamics of infections with free-living pathogens in the environment. The resulting linked models are sometimes called immuno-epidemiological models. However, there is still no generalised framework for linking the within-host and between-host dynamics of infectious diseases. Furthermore, for infections with free-living pathogens in the environment, there is an additional stumbling block in that there is a gap in knowledge on how environmental factors (through water, air, soil, food, fomites, etc.) alter many aspects of such infections including susceptibility to infective dose, persistence of infection, pathogen shedding and severity of the disease. In this work, we link the two subsystems (within-host and between-host models) by identifying the within-host and between-host variables and parameters associated with the environmental dynamics of the pathogen and then design a feedback of the variables and parameters across the within-host and between-host models using human schistosomiasis as a case study. We study the mathematical properties of the linked model and show that the model is epidemiologically well-posed. Using results from the analysis of the endemic equilibrium expression, the disease reproductive number R0, and numerical simulations of the full model, we adequately account for the reciprocal influence of the linked within-host and between-host models. In particular, we illustrate that for human schistosomiasis, the outcome of infection at the individual level determines if, when and how much the individual host will further transmit the infectious agent into the environment, eventually affecting the spread of the infection in the host population. We expect the conceptual modelling framework developed here to be applicable to many infectious disease with free-living pathogens in the environment beyond the specific disease system of human schistosomiasis considered here.
在本研究中，我们开发了一个数学建模框架，用于将宿主体内感染动态与环境中自由生活病原体的宿主间传播动态联系起来。由此产生的关联模型有时被称为免疫流行病学模型。然而，目前仍没有一个通用框架来连接传染病的宿主体内和宿主间动态。此外，对于环境中自由生活病原体引起的感染，还存在一个额外的障碍，即关于环境因素（通过水、空气、土壤、食物、污染物等）如何改变此类感染的许多方面，包括对感染剂量的易感性、感染的持续时间、病原体排出以及疾病严重程度，目前还存在知识空白。在这项工作中，我们通过识别与病原体环境动态相关的宿主体内和宿主间变量及参数，将两个子系统（宿主体内模型和宿主间模型）联系起来，然后以人类血吸虫病为例，设计跨宿主体内和宿主间模型的变量和参数反馈。我们研究了关联模型的数学性质，并表明该模型在流行病学上是适定的。利用地方病平衡表达式分析结果、疾病繁殖数(R_0)以及完整模型的数值模拟结果，我们充分考虑了宿主体内和宿主间关联模型的相互影响。特别是，我们举例说明，对于人类血吸虫病，个体层面的感染结果决定了个体宿主是否、何时以及将多少感染源进一步传播到环境中，最终影响感染在宿主群体中的传播。我们期望这里开发的概念性建模框架能够适用于环境中存在自由生活病原体的许多传染病，而不仅仅局限于这里所考虑的人类血吸虫病特定疾病系统。
