A Novel Sampling Method to Measure Socioeconomic Drivers of Aedes albopictus Distribution in Mecklenburg County, North Carolina

Abstract

Climate change, urbanization, and globalization have facilitated the spread of Aedes mosquitoes into regions that were previously unsuitable, causing an increased threat of arbovirus transmission on a global scale. While numerous studies have addressed the urban ecology of Ae. albopictus, few have accounted for socioeconomic factors that affect their range in urban regions. Here we introduce an original sampling design for Ae. albopictus, that uses a spatial optimization process to identify urban collection sites based on both geographic parameters as well as the gradient of socioeconomic variables present in Mecklenburg County, North Carolina, encompassing the city of Charlotte, a rapidly growing urban environment. We collected 3645 specimens of Ae. albopictus (87% of total samples) across 12 weeks at the 90 optimized site locations and modelled the relationships between the abundance of gravid Ae. albopictus and a variety of neighborhood socioeconomic attributes as well as land cover characteristics. Our results demonstrate that the abundance of gravid Ae. albopictus is inversely related to the socioeconomic status of the neighborhood and directly related to both landscape heterogeneity as well as proportions of particular resident races/ethnicities. We present our results alongside a description of our novel sampling scheme and its usefulness as an approach to urban vector epidemiology. Additionally, we supply recommendations for future investigations into the socioeconomic determinants of vector-borne disease risk.

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

“…While it is biologically capable of transmitting dengue fever [6], outbreaks that can be directly attributed to this species are rare [35,39–41]. The lack of contribution of socioeconomic variables is also interesting, given socioeconomic conditions have been found to influence vector distribution [42–44]. However, no clear connection has been found between dengue risk and particular socioeconomic conditions [45], thus supporting our results.…”

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

“…A similar, although less detailed, approach was taken in North Italy [68] for West Nile Virus. However, a recent study [69], employed an a priori G*Power analysis to determine the sample size, and then allocated the samples to (a) maximize the spatial spreading of mosquito sample sites, and (b) to sample at locations that would reflect a large range of socioeconomic conditions, since the objective was to estimate the effect of socio-economic drivers in Aedes albopictus distribution. While conceptually similar to the one proposed here (although our goal was to find ecological homogeneous areas instead of mapping socioeconomic differences), the authors do not consider the spatial autocorrelation in their sample size and design (which for diseases transmission by Aedes species is very important, see for example [27] and [70]), although they do use a maximum coverage approach.…”

Detecting space-time clusters of dengue fever in Panama after adjusting for vector surveillance data

“…Another district, San Miguelito in metropolitan Panama City, contained the most observed cases during our study period, despite being only 49.9km 2 . This district can be characterized by high density housing and residents of relatively low socioeconomic status, which has previously been linked to increased vector-borne disease risk [45–48]. The staggering number of cases should be a cause for concern, yet its small geographic area may facilitate public health interventions such as vector control and community education.…”