Mathematical Models are a Powerful Method to Understand and Control the Spread of Huanglongbing

Authors

Rachel A Taylor, University of South Florida
Erin A Mordecai, Stanford University
Christopher A Gilligan, University of Cambridge
Jason R. Rohr, University of South FloridaFollow
Leah R Johnson, University of South Florida

Document Type

Article

Publication Date

11-3-2016

Keywords

Intervention strategies, Sensitivity analysis, Vector-borne disease, Mathematical modeling, Insecticide, Citrus greening, Temperature variation, Cost-benefit analysis, Flush

Digital Object Identifier (DOI)

https://doi.org/10.7717/peerj.2642

Abstract

Huanglongbing (HLB), or citrus greening, is a global citrus disease occurring in almost all citrus growing regions. It causes substantial economic burdens to individual growers, citrus industries and governments. Successful management strategies to reduce disease burden are desperately needed but with so many possible interventions and combinations thereof it is difficult to know which are worthwhile or cost-effective. We review how mathematical models have yielded useful insights into controlling disease spread for other vector-borne plant diseases, and the small number of mathematical models of HLB. We adapt a malaria model to HLB, by including temperature-dependent psyllid traits, "flushing" of trees, and economic costs, to show how models can be used to highlight the parameters that require more data collection or that should be targeted for intervention. We analyze the most common intervention strategy, insecticide spraying, to determine the most cost-effective spraying strategy. We find that fecundity and feeding rate of the vector require more experimental data collection, for wider temperatures ranges. Also, the best strategy for insecticide intervention is to spray for more days rather than pay extra for a more efficient spray. We conclude that mathematical models are able to provide useful recommendations for managing HLB spread.

Comments

A Preprint of this article also exists, first published May 20, 2016.

Rights Information

This work is licensed under a Creative Commons Attribution 4.0 License.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

PeerJ, v. 4, art. e2642

Scholar Commons Citation

Taylor, Rachel A; Mordecai, Erin A; Gilligan, Christopher A; Rohr, Jason R.; and Johnson, Leah R, "Mathematical Models are a Powerful Method to Understand and Control the Spread of Huanglongbing" (2016). Integrative Biology Faculty and Staff Publications. 355.
https://digitalcommons.usf.edu/bin_facpub/355