Innovative tools for modeling infectious agents are essential for better understanding disease spread given the inherent complexity of changing and interacting ecological, environmental, and demographic factors. We leveraged fine-scale satellite data on urban areas to build a road-connected geospatial network upon which to model disease spread. This model was tested by simulating the spread of the 2009 pandemic influenza in Rwanda and also used to determine the effects of vaccination regimens on outbreak spread and impact. Our results were comparable to data collected during the actual pandemic in Rwanda, determining the initial places affected after outbreak introduction in Kigali. They also highlighted the effectiveness of preventing outbreaks by targeting mitigation efforts at points of outbreak origin. This modeling approach can be valuable for planning and control purposes in real-time disease situations, providing helpful baseline scenarios during initial phases of outbreaks, and can be applied to other infectious diseases where high population mobility promotes rapid disease propagation.