Authors

Discussion

Transmission-blocking interventions (TBIs) aim to eliminate malaria by interrupting transmission of the parasite between hosts and mosquito vectors. Accurate methods to assess TBI efficacy are key to ensure that the best candidate TBI drugs or vaccines progress to clinical trials. This is particularly vital for novel population assays (PA) where efficacy is measured over successive transmission cycles. We present a method for estimating TBI efficacy from PA data by fitting a hierarchical Bayesian model to multiple life stages of the parasite. This enables both host-to-vector and vector-to-host transmission to be density-dependent processes whilst accounting for stochastic fluctuations driven by superinfection and small sample sizes. This improves the precision of intervention efficacy estimates and demonstrates that TBI impact is not sufficiently captured by changes in prevalence alone because TBIs also suppress parasite density in secondarily infected hosts. Partially effective TBIs require multiple generations before substantial reductions in prevalence are observed whilst

immediately suppressing parasite density. This has valuable implications for assessing the performance of TBI candidates in field and clinical trials.