Over the past year, statisticians and epidemiologists around the world have made it their mission to build statistical models that help understand and forecast the spread of COVID-19. Spatial models have been of particular interest because the disease spreads via person-to-person contact, meaning that regions that are close together are more likely to have similar COVID-19 case counts that regions that are far apart. However, using the actual number of trips between two regions would likely predict COVID-19 case counts much better than physical proximity. In this talk, we investigate the efficacy of using telecommunications-derived mobility data to induce spatial dependence in spatial models and apply it to two Spanish Communities’ COVID-19 case counts. We do this by extending Besag-York-Mollié models to include both physical and mobility effects. We conclude that the number of people moving between regions better explains variation in COVID-19 case counts than physical proximity data.