Document Type
Article
Publication Date
2020
Keywords
Eastern equine encephalitis virus, index model, maximum covering location model, spatial optimization
Digital Object Identifier (DOI)
https://doi.org/10.1080/19475683.2019.1688391
Abstract
Diseases carried by mosquitoes and other arthropods endanger human health globally. Though costly, surveillance efforts are vital for disease control and prevention This paper describes an approach for strategically configuring targeted disease surveillance sites across a study area. The methodology combines risk index mapping and spatial optimization modelling. The risk index is used to identify demand for surveillance, and the maximum covering location problem is used to select a specified number of candidate surveillance sites that covers the maximum amount of risk. The approach is demonstrated using a case study where optimal locations for sentinel surveillance sites are selected for the purposes of detecting eastern equine encephalitis virus in a county in the state of Florida. Optimal sentinel sites were selected under a number of scenarios that modelled different target populations (horses or humans), coverage distances (0.5, 1.0, and 1.5 km), and numbers of sites to select (1–12). Sentinel site selections for the horse and human models displayed different spatial patterns, with horse sites located largely in the west-central region and human ones in the north-central. Minor amounts of spatial overlap between the horse and human sites were observed, especially as coverage distances and numbers of sites were increased. Additionally, a near linear increase in risk coverage was observed as sites were incrementally added to the scenarios. This finding suggests that the number of sentinel sites within the ranges explored should be based on the maximum that can be funded, since they provide similar levels of benefit.
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
Annals of GIS, v. 26, issue 1, p. 13-23
Scholar Commons Citation
Downs, Joni A.; Vaziri, Mehrdad; Deskins, George; Kellner, William; Miley, Kristi M.; and Unnasch, Thomas R., "Optimizing Arbovirus Surveillance using Risk Mapping and Coverage Modelling" (2020). School of Geosciences Faculty and Staff Publications. 2240.
https://digitalcommons.usf.edu/geo_facpub/2240
