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The transmission of infectious diseases involves complex interactions across multiple biological scales, from within-host immunological processes to between-host transmission dynamics. While multiscale models have the potential to capture these interactions more accurately, they are often hindered by increased complexity and limited data availability. In this study, we develop a multiscale epidemic model linking host-vector population-level transmission dynamics to within-host and within-vector pathogen dynamics. Our model captures key features of within-vector viral progression and allows bidirectional coupling between within-host and between-host processes. The scales are linked under the assumption of dose-dependent transmission, with the functional form informed by empirical viremia-infectiousness data obtained from mosquito feeding experiments on live hosts. Focusing on Dengue, Zika, and West Nile viruses as case studies, we assess how different functional forms of the coupling affect the number of equilibria of the epidemic model. We find that when the transmission is modeled using linear coupling functions, the multiscale model yields the same bifurcation structure of the simpler, uncoupled model, indicating that the linking of scales does not alter the range of possible long-term epidemiological states in such cases. However, nonlinear coupling can induce complex behaviors such as multiple endemic equilibria, which the uncoupled model does not capture. These results underscore the importance of carefully selecting coupling functions and provide guidance on when multiscale modeling is essential for understanding and managing vector-borne diseases.