Mosquito salivary protein antibodies as efficient endpoints for vector control trials: A mechanistic comparison with clinical and entomological outcomes.

Evaluating vector control interventions through randomized trials is often challenging because clinical endpoints, such as infection incidence, are rare and heterogeneous even areas at high risk, resulting in large required sample sizes. Antibodies to mosquito salivary proteins (MSPs) have emerged as promising markers of exposure to mosquito bites, yet their utility as trial endpoints is not fully understood. To address this gap, we developed a mechanistic modeling framework to describe MSP antibody dynamics in response to mosquito biting and to compare the statistical power of serological, clinical, and entomological trial endpoints. We introduce a new stochastic model for anti-MSP antibody dynamics-the Antibody Non-Homogeneous Poisson Process (ANPP) model-which incorporates seasonal variation in exposure and inter-individual heterogeneity. With a temperature-driven SEIR-SEI transmission model to create a unified simulation platform. Using this framework, we systematically compare sample size requirements for each endpoint under a range of simulated vector control strategies. Our results reveal a pronounced efficiency hierarchy: serological endpoints can reduce sample size needs by several orders of magnitude compared with clinical endpoints, especially in low-incidence settings. We also show that endpoints tied directly to mosquito population dynamics, such as antibody levels or trap counts, display seasonal patterns that mirror fluctuations in mosquito abundance, whereas infection-based endpoints remain comparatively flat. These findings provide a quantitative foundation for incorporating serological markers into trial design and highlight their potential to accelerate the evaluation and deployment of vector control tools.