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

Abstract

Vector-borne disease control relies on efficient vector surveillance, mostly carried out using traps whose number and locations are often determined by expert opinion rather than a rigorous quantitative sampling design. In this work we propose a framework for ecological sampling design which in its preliminary stages can take into account environmental conditions obtained from open data (i.e. remote sensing and meteorological stations) not necessarily designed for ecological analysis. These environmental data are used to delimit the area into ecologically homogeneous strata. By employing Bayesian statistics within a model-based sampling design, the traps are deployed among the strata using a mixture of random and grid locations which allows balancing predictions and model-fitting accuracies. Sample sizes and the effect of ecological strata on sample sizes are estimated from previous mosquito sampling campaigns open data. Notably, we found that a configuration of 30 locations with four households each (120 samples) will have a similar accuracy in the predictions of mosquito abundance as 200 random samples. In addition, we show that random sampling independently from ecological strata, produces biased estimates of the mosquito abundance. Finally, we propose standardizing reporting of sampling designs to allow transparency and repetition/re-use in subsequent sampling campaigns.

Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii

“…The next phase of the Ag1000G Project will expand the resource to cover 18 countries and will include another major malaria vector species, Anopheles arabiensis, and so will address some of these gaps. Looking beyond the Ag1000G Project, genomic surveillance of insecticide resistance will require new sampling frameworks that incorporate spatial and ecological modeling of vector distributions to guide sampling at appropriate spatial scales (Sedda et al 2019). Fortunately, mosquitoes are easy to transport, and the costs of sequencing continue to decrease, so it is reasonable to consider a mixed strategy that includes both whole-genome sequencing and targeted assays.…”

Human practices promote presence and abundance of disease-transmitting mosquito species

“…Sampling points within a region were selected based on multiple criteria. The primary selection criterion was to sample from waterbodies that were representative of the region, including a diversity of wetlands rather than those with the highest catch rates 28 . Additional criteria stipulated that the water bodies were at least 1 km away from one another to avoid sampling mosquitoes from adjacent water bodies, as mean mosquito dispersal distances range from 35 m to 1.4 km 31 .…”

“…Mosquito abundance and composition can also vary across locations and land use types 22 - 27 . However, many comparisons rely on opportunistic sampling across different time periods or targeted sampling at locations to maximize collections 28 , but not always 29 , 30 . Here, we use a paired sampling design to show that human activities beyond climate are strongly associated with high abundances of known vectors across large spatial extents.…”

A protocol for a longitudinal, observational cohort study of infection and exposure to zoonotic and vector-borne diseases across a land-use gradient in Sabah, Malaysian Borneo: a socio-ecological systems approach

“…To determine the locations of entomological sampling sites, we will use a simulation-based approach designed to optimise habitat fragmentation studies 40 . This approach extends commonly used geostatistical survey designs, in which survey locations are positioned to maximise predictive abilities and minimise survey effort by leveraging spatial autocorrelation between survey points 41 , 42 . Geostatistical sampling designs will be adapted to maximise habitat differences between sampling locations by integrating metrics on land cover, fragment size and distance from forest edges 40 .…”