Many pathogens rely on the mobility of their hosts for dispersal. In order to understand and predict how a disease can rapidly sweep across entire continents, illuminating the contributions of host movements to disease spread is pivotal. While elegant proposals have been made to elucidate the spread of human infectious diseases, the direct observation of long-distance dispersal events of animal pathogens is challenging. Pathogens like avian influenza A viruses, causing only short disease in their animal hosts, have proven exceptionally hard to study. Here, we integrate comprehensive data on population and disease dynamics for low-pathogenic avian influenza viruses in one of their main hosts, the mallard, with a novel movement model trained from empirical, high-resolution tracks ofmallardmigrations. This allowed us to simulate individualmallard migrations from a key stopover site in the Baltic Sea for the entire population and link these movements to infection simulations. Using this novel approach, we were able to estimate the dispersal of low-pathogenic avian influenza viruses by migrating mallards throughout several autumn migratory seasons and predicted areas that are at risk of importing these viruses. We found that mallards are competent vectors and on average dispersed viruses over distances of 160 km in just 3 h. Surprisingly, our simulations suggest that such dispersal events are rare even throughout the entire autumn migratory season. Our approach directly combines simulated population-level movements with local infection dynamics and offers a potential converging point for movement and disease ecology.