Migration can influence dynamics of pathogen-host interactions. However, it is not clearly known how migration pattern, in terms of the configuration of the migration network and the synchrony of migration, affects infection prevalence. We therefore applied a discrete-time SIR model, integrating environmental transmission and migration, to various migration networks, including networks with serial, parallel, or both serial and parallel stopover sites, and with various levels of migration synchrony. We applied the model to the infection of avian influenza virus in a migratory geese population. In a network with only serial stopover sites, increasing the number of stopover sites reduced infection prevalence, because with every new stopover site, the amount of virus in the environment was lower than that in the previous stopover site, thereby reducing the exposure of the migratory population. In a network with parallel stopover sites, both increasing the number and earlier appearance of the stopover sites led to an earlier peak of infection prevalence in the migratory population, because the migratory population is exposed to larger total amount of virus in the environment, speeding-up the infection accumulation. Furthermore, higher migration synchrony reduced the average number of cumulative infection, because the majority of the population can fly to a new stopover site where the amount of virus is still relatively low and has not been increased due to virus shedding of infected birds. Our simulations indicate that a migration pattern with multiple serial stopover sites and with highly synchronized migration reduces the infection prevalence.