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Bulletin of Mathematical Biology

, Volume 81, Issue 6, pp 2011–2028 | Cite as

Modeling Approach Influences Dynamics of a Vector-Borne Pathogen System

  • Allison K. ShawEmail author
  • Morganne Igoe
  • Alison G. Power
  • Nilsa A. Bosque-Pérez
  • Angela Peace
Article

Abstract

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.

Keywords

Barley yellow dwarf virus Individual-based model Mean field Ordinary differential equation Vector-borne plant pathogen 

Notes

Acknowledgements

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).

Supplementary material

11538_2019_595_MOESM1_ESM.pdf (109 kb)
Supplementary material 1 (pdf 110 KB)

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Copyright information

© Society for Mathematical Biology 2019

Authors and Affiliations

  1. 1.Department of Ecology, Evolution, and BehaviorUniversity of MinnesotaSt. PaulUSA
  2. 2.Department of MathematicsUniversity of MinnesotaMinneapolisUSA
  3. 3.Department of MathematicsUniversity of TennesseeKnoxvilleUSA
  4. 4.Department of Ecology and Evolutionary BiologyCornell UniversityIthacaUSA
  5. 5.Department of Entomology, Plant Pathology and NematologyUniversity of IdahoMoscowUSA
  6. 6.Department of Mathematics and StatisticsTexas Tech UniversityLubbockUSA

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