Mosquito

Since 2003 some Arizona counties have followed mosquito surveillance protocols to trap the West Nile Virus vector, Culex spp., using CO2 traps. Despite low sensitivity of these traps to detect Aedes spp., one out of seven CO2 traps deployed in Santa Cruz County detected Aedes aegypti in 2014. Enhancing surveillance for Aedes spp. in this region is critical, given that local transmission of dengue has occured across the border in Nogales, Sonora. Limited resources in Santa Cruz County have previously inhibited efforts to enhance mosquito surveillance . To broaden the reach of county surveillance, we implemented a community participatory project by engaging residents to conduct ovitrapping, a non-technical trap that attracts Aedes spp.

Objective

The objective of this work is to develop an efficient communitybased strategy to enhance mosquito surveillance for Aedes spp., vector for chikungunya and dengue viruses, in Santa Cruz County on the U.S.-Mexico border. We aim to determine vector presence, distribution, and seasonality by using ovitraps maintained by community members.

Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan (PFSS)3 provides a flexible framework for adding soft constraints to the fast subset scan, rewarding or penalizing inclusion of individual points into a cluster with additive point-specific penalty terms. We propose the support vector subset scan (SVSS), a novel method that iteratively assigns penalties according to distance from the separating hyperplane learned by a kernel support vector machine (SVM). SVSS efficiently detects disease clusters that are geometrically compact and irregular.

Objective

We present the support vector subset scan (SVSS), a new method for detecting localized and irregularly shaped patterns in spatial data. SVSS integrates the penalized fast subset scan3 with a kernel support vector machine classifier to accurately detect disease clusters that are compact and irregular in shape.

As part of a statewide effort to enhance surveillance for Aedes spp.mosquitoes the Office of Border Health (OBH) took the lead inproviding technical assistance on surveillance in counties bordering Mexico. In 2016, OBH sought ways to enhance surveillance in a wider geographic area. Trap locations closer to the border were established as a priority, given high amount of traffic across the international line, high border Aedesmosquito activity, and native cases of dengue reported at the border in Mexico.

Objective

This surveillance project aims to increase and broaden coverage o fAedes spp. ovitrap locations in Arizona’s U.S.- Mexico border region through interagency collaboration.

There were several stand-alone vector surveillance applications being used by the New York State Department of Health (NYSDOH) to support the reporting of mosquito, bird, and mammal surveillance and infection information implemented in early 2000s in response to West Nile virus. In subsequent years, the Electronic Clinical Laboratory Reporting System (ECLRS) and the Communicable Disease Electronic Surveillance System (CDESS) were developed and integrated to be used for surveillance and investigations of human infectious diseases and management of outbreaks.

An integrated vector surveillance system project was proposed to address the migration of the stand-alone vector surveillance applications into a streamlined, consolidated solution to support operational, management, and technical needs by using the national standards with the existing resources and technical environment.

Objective

To develop a mosquito surveillance module to collect mosquito information testing for West Nile, East Equine Encephalitis (EEE) and Zika viruses using national standards. To provide a common set of data for local health departments (LHDs) and state users to report and share information. To monitor the type of mosquito species that carry diseases.