Plant epidemiological models are used in a range of applications, from detailed simulation models that closely follow pathogen infection and dispersal, to generic template-based models for rapid assessment of invasive species. There is increasing interest in applying small scale models - e.g., based on tissue, organ or whole plants - using remotely collected daily data, to generate regional risk information (e.g., maps). The assumption made is that such small scale models “scale-up” appropriately to regional, continental or even global scale. However, these models are often constructed using locally collected, hourly data. By necessity data available are often at much coarser scale, both temporally and spatially, than the data used to develop the model. Computational requirements increase considerably when more detailed models that require fine resolution data (if available) are applied to large areas, while small scale models often add little useful information at these scales and may lead to error propagation. Ideally, detailed models should be used at small temporal and spatial scales and less detailed models used for larger temporal and spatial scales. This paper presents examples of different approaches for changing scales - including upscaling models, downscaling data, and developing new models - and the issues that these approaches create or solve, along with ideas about how we can ensure that the scale of model and data match the desired application.