Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, 55108, USA.
Department of Mathematics, University of Minnesota, Minneapolis, MN, 55455, USA.
Bull Math Biol. 2019 Jun;81(6):2011-2028. doi: 10.1007/s11538-019-00595-z. Epub 2019 Mar 21.
The choice of a modeling approach is a critical decision in the modeling process, as it determines the complexity of the model and the phenomena that the model captures. In this paper, we developed an individual-based model (IBM) and compared it to a previously published ordinary differential equation (ODE) model, both developed to describe the same biological system although with slightly different emphases given the underlying assumptions and processes of each modeling approach. We used both models to examine the effect of insect vector life history and behavior traits on the spread of a vector-borne plant virus, and determine how choice of approach affects the results and their biological interpretation. A non-random distribution of insect vectors across plant hosts emerged in the IBM version of the model and was not captured by the ODE. This distribution led simultaneously to a slower-growing vector population and a faster spread of the pathogen among hosts. The IBM model also enabled us to test the effect of potential control measures to slow down virus transmission. We found that removing virus-infected hosts was a more effective strategy for controlling infection than removing vector-infested hosts. Our findings highlight the need to carefully consider possible modeling approaches before constructing a model.
模型方法的选择是建模过程中的一个关键决策，因为它决定了模型的复杂性和模型所捕捉的现象。在本文中，我们开发了一个基于个体的模型（IBM），并将其与之前发表的常微分方程（ODE）模型进行了比较，这两个模型都是为了描述相同的生物系统而开发的，尽管由于每种建模方法的基本假设和过程略有不同，因此侧重点也略有不同。我们使用这两个模型来研究昆虫媒介的生活史和行为特征对媒介传播的植物病毒传播的影响，并确定方法的选择如何影响结果及其生物学解释。在 IBM 版本的模型中，昆虫媒介在植物宿主中的分布是非随机的，而 ODE 模型则无法捕捉到这一点。这种分布导致媒介种群的增长率降低，而病原体在宿主之间的传播速度加快。IBM 模型还使我们能够测试潜在的控制措施来减缓病毒传播的效果。我们发现，去除感染病毒的宿主是控制感染的更有效策略，而不是去除受媒介侵染的宿主。我们的研究结果强调了在构建模型之前仔细考虑可能的建模方法的必要性。
