Document Type
Article
Publication Date
2016
Keywords
intervention strategies, sensitivity analysis, vector-borne disease, mathematical modeling, insecticide, citrus greening, temperature variation, cost-benefit analysis
Digital Object Identifier (DOI)
https://doi.org/10.7287/peerj.preprints.2059v1
Abstract
Huanglongbing, or citrus greening, is a global citrus disease occurring in almost all citrus growing regions and causing 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 Huanglongbing. We adapt a malaria model to Huanglongbing, by including temperature-dependent psyllid traits and economic costs, to show how models can be used to highlight which parameters require more data collection or which should be targeted for intervention. We analyze the most common intervention strategy, insecticide spraying, to determine the most cost-effective spraying strategy. We found that fecundity and feeding rate of the vector require more experimental data collection, for wider temperatures ranges. The best strategy for insecticide intervention was 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 Huanglongbing spread.
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. e2059
Scholar Commons Citation
Taylor, Rachel A.; Mordecai, Erin; 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. 542.
https://digitalcommons.usf.edu/bin_facpub/542
