We show that the simulation results have the same propagation shape as the weekly influenza rates asrecorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show thata diminishing relative humidity of 10% produces an increment of about 1.6% in the final infection rate. The effect oftemperature changes on the infection spread is also noticeable, with a decrease of 1.1% per extra degree.Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and wouldpermit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We makeEpiGraph source code and epidemic data publicly available