Accurate assessments of vector occurrence and abundance, particularly in widespread vector-borne diseases such as malaria, are crucial for the efficient deployment of disease surveillance and control interventions. Although previous studies have explored the benefits of adaptive sampling for identifying disease hotspots (mostly through simulations), limited research has been conducted on field surveillance of malaria vectors.
We developed and implemented an adaptive spatial sampling design in southwestern Benin, specifically targeting potential and uncertain Anopheles gambiae hotspots, a major malaria vector in sub-Saharan Africa. The first phase of our proposed design involved delineating ecological zones and employing a proportional lattice with close pairs sampling design to maximize spatial coverage, representativeness of ecological zones, and account for spatial dependence in mosquito counts. In the second phase, we employed a spatial adaptive sampling design focusing on high-risk areas with the greatest uncertainty.
The adaptive spatial sampling design resulted in a reduced sample size from the first phase, leading to improved predictions for both out-of-sample and training data. Collections of Anopheles gambiae in high-risk and low-uncertainty areas were nearly tripled compared to those in high-risk and high-uncertainty areas. However, the overall model uncertainty increased.
While the adaptive sampling design allowed for increased collections of Anopheles gambiae mosquitoes with a reduced sample size, it also led to a general increase in uncertainty, highlighting the potential trade-offs in multi-criteria adaptive sampling designs. It is imperative that future research focuses on understanding these trade-offs to expedite effective malaria control and elimination efforts.