  Stochastic epidemic models, generally more realistic than deterministic
counterparts, have often been seen too complex for rigorous mathematical
analysis because of level of details it requires to comprehensively capture the
dynamics of diseases. This problem further becomes intense when complexity of
diseasees increases as in the case of vector-borne diseases (VBD). The VBDs are
human illnesses caused by pathogens transmitted among humans by intermediate
species, which are primarily arthropods. In this study, a stochastic VBD model
is developed and novel mathematical methods are described and evaluated to
systematically analyze the model and understand its complex dynamics. The VBD
model incorporates some relevant features of the VBD transmission process
including demographical, ecological and social mechanisms. The analysis is
based on dimensional reductions and model simplications via scaling limit
theorems. The results suggest that the dynamics of the stochastic VBD depends
on a threshold quantity R_0, the initial size of infectives, and the type of
scaling in terms of host population size. The quantity R_0 for deterministic
counterpart of the model, interpreted as threshold condition for infection
persistence as is mentioned in the literature for many infectious disease
models, can be computed. Different scalings yield different approximations of
the model, and in particular, if vectors have much faster dynamics, the effect
of the vector dynamics on the host population averages out, which largely
reduces the dimension of the model.
