Modeling Approach Influences Dynamics of a Vector-Borne Pathogen System
- 249 Downloads
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.
KeywordsBarley yellow dwarf virus Individual-based model Mean field Ordinary differential equation Vector-borne plant pathogen
We thank members of the Shaw lab and two anonymous reviewers for helpful advice and insight. We acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper (http://www.msi.umn.edu).
- Ajayi BO, Dewar AM (1983) The effect of barley yellow dwarf virus on field populations of the cereal aphids, Sitobion avenae and Metopolophium dirhodum. Ann Appl Biol 103(1):1–11Google Scholar
- Bazghandi A (2012) Techniques, advantages and problems of agent based modeling for traffic simulation. Int J Comput Sci 9(1):115–119Google Scholar
- Dixon AFG, Glen DM (1971) Morph determination in the bird cherry-oat aphid, Rhopalosiphum padi L. Ann Appl Biol 68(1):11–21Google Scholar
- Durrett R, Levin SA (1994) The importance of being discrete (and spatial). Theor Popul Biol 46:363–363Google Scholar
- Jiménez-Martnez ES, Bosque-Pérez NA (2004) Variation in Barley yellow dwarf virus transmission efficiency by Rhopalosiphum padi (Homoptera: Aphididae) after acquisition from transgenic and nontransformed wheat genotypes. J Econ Entomol 97(6):1790–1796Google Scholar
- Jiménez-Martnez ES, Bosque-Pérez NA, Berger PH, Zemetra RS (2004) Life history of the bird cherry-oat aphid, Rhopalosiphum padi (Homoptera: Aphididae), on transgenic and untransformed wheat challenged with Barley yellow dwarf virus. J Econ Entomol 97(2):203–212Google Scholar
- Levins R (1966) The strategy of model building in population biology. Am Sci 54(4):421–431Google Scholar
- Scholl HJ (2001) Agent-based and system dynamics modeling: a call for cross study and joint research. In: Proceedings of the 34th annual Hawaii international conference on system sciences, 2001. IEEE, pp 1–8Google Scholar
- Thresh JM (1988) Eradication as a virus disease control measure. In: Clifford BC, Lester E (eds) Control of plant diseases: costs and benefits. Blackwell Scientific Publications, Oxford, pp 155–194Google Scholar