Coupling models of within-human, human-to-mosquito, and within-mosquito malaria parasite dynamics to identify key drivers of malaria transmission

Malaria is a public health burden affecting billions of people worldwide. Transmission of malaria parasites from the human host to the mosquito during a blood meal is an essential step in the life cycle of the parasite. Quantitative estimation of the contributions of different host factors to human-tomosquito transmission is essential for a mechanistic understanding of the biological processes under-pinning malaria transmission. In this study, we develop a multi-scale stochastic model of human-to-mosquito transmission by coupling a model of within-human parasite dynamics with a model describing malaria transmission and sexual development within the mosquito host. We fit the model to data from a direct feeding assay that formed part of a human challenge study. We use a Bayesian statistical approach to estimate several biological parameters that have until now been difficult to quantify in the literature and conducted a sensitivity analysis to identify the critical factors that influence the probability of human-to-mosquito transmission. Our results show that (1) the number of viable male gametes developing within a mosquito is approximately 80% (95% uncertainty: 13%–290%) of the total number of male gametocytes taken in a blood meal and the probability of successful fertilization between a female gamete and a viable male gamete is approximately 2.90% (0.57%–10.92%); and (2) the leading factors influencing the probability of human-to-mosquito transmission are the multiplication factor of asexual parasites and the maturation rate of gametocytes within the human host. We also show that, for asymptomatic infections where asexual parasitemia oscillates around an approximate level after the exponential growth phase, the probability of parasite transmission from an asymptomatic individual to mosquitoes is strongly correlated with the parasitemia level, validating the link between asexual parasitemia and the transmission probability. Our work not only provides a better mechanistic understanding of the biological processes underpinning malaria transmission but also provides a framework that—by combining data and mathematical and statistical approaches—can be integrated into a multi-scale epidemiological transmission model to evaluate and develop more effective intervention strategies in support of efforts to eliminate malaria.