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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.
Highlights
Sampling design is a crucial step in any survey as it affects the quality of data collection and analysis [1]
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 not necessarily designed for ecological analysis
Defining a standard spatial sampling design does not affect the multitude of choices necessary for each different problem, but it requires that three elements are fully described: sample size; stratification and geographical allocation of the sampling points
Summary
Sampling design is a crucial step in any survey as it affects the quality of data collection and analysis [1]. Sampling strategies should be designed to maximize the effectiveness of the study, using any relevant preliminary and background data available [2]. Use of available informative data and the rigour with which sampling designs are reported continue to fall short of what could be achieved [3]. The amount of environmental data available from open-data platforms is often acknowledged but rarely exploited to support sampling design, while the necessary information for study repeatability, comparability or usability are often inadequately reported. Such data, even when not collected for ecological analyses, can support representativeness in investigations of population dynamics, epidemiological processes and biological studies. We use an example from malaria vector surveillance to design a sampling strategy for collecting mosquitoes for whole genome sequencing based monitoring and evaluation
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