Estimating malaria transmission from humans to mosquitoes in a noisy landscape.

A basic quantitative understanding of malaria transmission requires measuring the probability a mosquito becomes infected after feeding on a human. Parasite prevalence in mosquitoes is highly age-dependent, and the unknown age-structure of fluctuating mosquito populations impedes estimation. Here, we simulate mosquito infection dynamics, where mosquito recruitment is modelled seasonally with fractional Brownian noise, and we develop methods for estimating mosquito infection rates. We find that noise introduces bias, but the magnitude of the bias depends on the 'colour' of the noise. Some of these problems can be overcome by increasing the sampling frequency, but estimates of transmission rates (and estimated reductions in transmission) are most accurate and precise if they combine parity, oocyst rates and sporozoite rates. These studies provide a basis for evaluating the adequacy of various entomological sampling procedures for measuring malaria parasite transmission from humans to mosquitoes and for evaluating the direct transmission-blocking effects of a vaccine.