2019
DOI: 10.1080/19475683.2019.1688391
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Optimizing arbovirus surveillance using risk mapping and coverage modelling

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 candidat… Show more

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Cited by 9 publications
(6 citation statements)
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“…Overall, the model created in this study further refines the risk index models created previously to predict EEEV transmission, as Maxent is better equipped to handle the lack of data points in southern Florida. In addition, while risk modelling can be utilized to evaluate areas where there is a perceived risk of future transmission (Downs et al 2019), Maxent has the capability to do this at a larger scale. The model aligns with previous studies that emphasize the Florida panhandle as a hotspot for EEEV transmission, and this hotspot location has now been verified by both sentinel chicken infection data and equine fatality data.…”
Section: Model Resultsmentioning
confidence: 99%
“…Overall, the model created in this study further refines the risk index models created previously to predict EEEV transmission, as Maxent is better equipped to handle the lack of data points in southern Florida. In addition, while risk modelling can be utilized to evaluate areas where there is a perceived risk of future transmission (Downs et al 2019), Maxent has the capability to do this at a larger scale. The model aligns with previous studies that emphasize the Florida panhandle as a hotspot for EEEV transmission, and this hotspot location has now been verified by both sentinel chicken infection data and equine fatality data.…”
Section: Model Resultsmentioning
confidence: 99%
“…In addition, this model will likely provide effective surveillance for SLEV due to the overlap of WNV and SLEV vectors. Optimal locations for a statewide network of surveillance sites could be determined using risk models and spatial optimization modeling (Downs et al 2020), so future work might explore optimal configurations of sites to cover both WNV and EEEV in Florida based on the results presented here. Improvements in the sentinel chicken surveillance program in Florida will provide public health benefits through earlier detection of circulating arboviruses in the environment allowing for a more rapid, targeted response by MCPs.…”
Section: Discussionmentioning
confidence: 99%
“…Disease surveillance aims to collect data at different times or locations, to improve our understanding of the spatial and/or temporal distribution of a disease [1][2][3]. Surveillance data assist public health authorities to distribute control measures, vaccines and medical resources where and when they are needed [2,4]. Disease surveillance is typically limited by budget, time and resources [5][6][7].…”
Section: Introductionmentioning
confidence: 99%
“…In a spatial context, the targeted allocation of public health resources should incorporate knowledge of the spatial distribution of the disease [ 2 ]. There is high uncertainty in many such estimated distributions: sampling bias [ 8 ], diagnostic or geographical limitations in an underlying case dataset [ 9 ], poor understanding around modes of disease transmission or specific reservoir or vector species [ 10 ], and model covariate choice all contribute to uncertainty in a final model estimate.…”
Section: Introductionmentioning
confidence: 99%