Our aim is to estimate the abundance of eggs of the mosquito Aedes aegypti, one of the vectors responsible for transmitting dengue fever. We have available the abundance of eggs observed at n locations, during T consecutive weeks, in Recife, a city in the Northeast of Brazil. More specifically, the abundances were observed at n=84 locations from May 2004 until May 2006. Because of fund limitations, the n locations were divided into K groups, and at each week t, samples were collected only at the locations belonging to group k=1,2,...,K, such that n=n_1+n_2+...n_K, where n_k is the number of locations in group k. Therefore, we have observations missing by design, leading to a spatio-temporal misalignment of the data. We propose a spatio-temporal model which naturally accounts for the misalignment present in the data and borrows strength among the observed locations, across the different weeks, to learn about the behavior of the abundance of eggs during this period. The temporal structure is modelled through dynamic linear models and the spatial structure is captured through Gaussian processes. Inference procedure is based on the Bayesian paradigm, which naturally provides uncertainties about our estimates, especially for the missing observations. .