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

ABSTRACT: 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,

SUBMITTER: Reiner RC 

PROVIDER: S-EPMC4614487 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature