Development and deployment of improved Anopheles gambiae s.l. field surveillance by adaptive spatial sampling design

Abstract

Accurate assessments of vector occurrence and abundance, particularly in widespread vector-borne diseases such as malaria, is essential for efficient deployment of disease surveillance and control interventions. This study emphasizes the need for flexible spatial sampling designs that can capture the dynamic relationships between disease vector species and the environment. Although previous studies have examined the benefits of adaptive sampling for disease hotspot identification (mostly by simulations), limited research has been conducted on field surveillance of malaria vectors. Here, an adaptive spatial sampling design targeting potential and uncertain An. gambiae hotspots, a major malaria vector in sub-Saharan Africa, is presented. The first phase of the proposed design involved ecological zone delineation and a proportional lattice with close pairs sampling design to maximise spatial coverage, representativeness of ecological zones and vector spatial autocorrelation (by the employment of close pairs). In the second phase, a spatial adaptive sampling design targeted high-risk areas with the largest uncertainty. For the second phase, the sample size was reduced compared to the first phase, but predictions improved for out-of-sample and training data. However, the overall model uncertainty increased, highlighting the trade-off in multi-criteria adaptive sampling designs. It is important that future research focuses on these trade-offs to reduce the timescale for effective malaria control and elimination efforts.