Applying the N-mixture model approach to estimate mosquito population absolute abundance from monitoring data

Estimating population abundance is a key objective of surveillance programs, particularly for vector species of public health interest. For mosquitos, which are vectors of human pathogens, established methods to measure absolute population abundance such as mark‐release‐recapture are difficult to implement and usually spatially limited. Typically, regional monitoring schemes assess species relative abundance (counting captured individuals) to prioritize control efforts and study species distribution. However, assessing absolute abundance is crucial when the focus is on pathogen transmission by contacts between vectors and hosts. Here, we applied the N‐mixture model approach to estimate mosquito abundance from standard monitoring data. We extended the N‐mixture model approach in a Bayesian framework by considering a beta‐binomial distribution for the detection process. We ran a simulation study to explore model performance under a low detection probability, a time‐varying population and different sets of independent variables. When informative priors were used and the model was well specified, estimates by N‐mixture model well correlated (>0.9) with synthetic data and had a mean absolute deviation of about 20%. Correlation decreased and biased increased with uninformative priors or model misspecification. When fed with field monitoring data to estimate the absolute abundance of the mosquito arbovirus vector Aedes albopictus within the metropolitan city of Rome (Italy), N‐mixture model showed higher population size in residential neighbourhoods than in large green areas and revealed that traps located adjacent to vegetated sites have a higher probability of capturing mosquitoes. Synthesis and applications. Our results show that, if supported by a good knowledge of the target species biology and by informative priors (e.g. from previous studies of capture rates), the N‐mixture model represents a valuable tool to exploit field monitoring data to estimate absolute abundance of disease vectors and to assess vector‐related health risk on a wide spatial and temporal scale. For mosquitoes specifically, it is also valuable to invest in increased efficiency of trapping devices to improve estimates of absolute abundance from the models