A statistical calibration tool for methods used to sample outdoor-biting mosquitoes

Background: Improved methods for sampling outdoor-biting mosquitoes are urgently needed to improve surveillance of vector-borne diseases. Such tools could potentially replace the human landing catch (HLC), which, despite being the most direct option for measuring human exposures, raises significant ethical and logistical concerns. Several alternatives are under development, but detailed evaluation still requires common frameworks for calibration relative to HLC. The aim of this study was to develop and validate a statistical framework for predicting human-biting rates from different exposure-free alternatives. Methods: We obtained mosquito abundance data (Anopheles arabiensis, Anopheles funestus and Culex spp.) from a year-long Tanzanian study comparing six outdoor traps [Suna Trap (SUN), BG Sentinel (BGS), M-Trap (MTR), M-Trap + CDC (MTRC), Ifakara Tent Trap-C (ITT-C) and Mosquito Magnet-X Trap (MMX)] and HLC. Generalised linear models were developed within a Bayesian framework to investigate associations between the traps and HLC, taking intra- and inter-specific density dependence into account. The best model was used to create a calibration tool for predicting HLC-equivalents. Results: For An. arabiensis, SUN catches had the strongest correlation with HLC (R-2 = 19.4), followed by BGS (R-2 = 17.2) and MTRC (R-2 = 13.1) catches. The least correlated catch was MMX (R-2 = 2.5). For An. funestus, BGS had the strongest correlation with the HLC (R-2 = 53.4), followed by MTRC (R-2 = 37.4) and MTR (R-2 = 37.4). For Culex mosquitoes, the traps most highly correlated with the HLC were MTR (R-2 = 45.4) and MTRC (R-2 = 44.2). Density dependence, both between and within species, influenced the performance of only BGS traps. An interactive Shiny App calibration tool was developed for this and similar applications. Conclusion: We successfully developed a calibration tool to assess the performance of different traps for assessing outdoor-biting risk, and established a valuable framework for estimating human exposures based on the trap catches. The performance of candidate traps varied between mosquito taxa; thus, there was no single optimum. Although all the traps tested underestimated the HLC-derived exposures, it was possible to mathematically define their representativeness of the true biting risk, with or without density dependence. The results of this study emphasise the need to aim for a consistent and representative sampling approach, as opposed to simply seeking traps that catch the most mosquitoes.

Automated summary

Improved methods for sampling outdoor-biting mosquitoes are crucial for monitoring vector-borne diseases. This study developed a statistical framework for predicting human-biting rates from different exposure-free alternatives and established a valuable framework for estimating human exposures based on trap catches.