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流行病学中的多智能体系统:媒介传播疾病传播研究中计算生物学的第一步。

Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission.

作者信息

Roche Benjamin, Guégan Jean-François, Bousquet François

机构信息

UMR 2724 Génétique et Evolution des Maladies Infectieuses, IRD-CNRS-Université de Montpellier I, Montpellier, France.

出版信息

BMC Bioinformatics. 2008 Oct 15;9:435. doi: 10.1186/1471-2105-9-435.

Abstract

BACKGROUND

Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems.

RESULTS

Computational biology may prove to be flexible enough to take into account the natural complexity observed in both natural and man-made ecosystems. In this paper, we propose a new computational model to study the transmission of infectious diseases in a spatially explicit context. We developed a multi-agent system model for vector-borne disease transmission in a realistic spatial environment.

CONCLUSION

Here we describe in detail the general behavior of this model that we hope will become a standard reference for the study of vector-borne disease transmission in wildlife. To conclude, we show how this simple model could be easily adapted and modified to be used as a common framework for further research developments in this field.

摘要

背景

计算生物学通常仅与遗传或基因组研究相关联。然而,由于计算资源的增加,计算模型在许多其他科学领域被视为有用的工具。此类建模系统对于复杂系统的研究尤为相关,例如新发传染病的流行病学。到目前为止,数学模型仍然是传染病流行病学和生态学分析的主要工具,SIR模型可被视为流行病学中的隐含标准。不幸的是,这些模型基于微分方程,因此,由于需要考虑的参数过多,可能会很快变得难以处理。例如,在野生动物中的人畜共患病和媒介传播疾病的情况下,许多不同的潜在宿主物种可能参与疾病传播的生命周期,而SIR模型可能不是真正捕捉该环境中整体疾病传播的最合适工具。这一局限性凸显了开发一种能够应对现实生态系统中疾病传播的标准空间模型的必要性。

结果

计算生物学可能足够灵活,能够考虑到自然和人造生态系统中观察到的自然复杂性。在本文中,我们提出了一种新的计算模型,用于研究空间明确背景下的传染病传播。我们开发了一种多智能体系统模型,用于在现实空间环境中进行媒介传播疾病的传播。

结论

在这里,我们详细描述了该模型的一般行为,我们希望它将成为野生动物中媒介传播疾病传播研究的标准参考。最后,我们展示了这个简单的模型如何能够轻松地进行调整和修改,以用作该领域进一步研究发展的通用框架。

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