Here, we want to investigate whether, and in which situations, implementation of the exact shape of the transmission response function is important. Although it would be possible to mathematically model the effects of different transmission–density functions for any hypothetical combination of demographic pattern and contact–density function, the sheer number of possible functions for each demographic situation would make it almost impossible to decide which functions are biologically relevant. To inform such models in a meaningful way, we need biological background data, i.e. a species for which population dynamics, a contact–density or transmission–density function, and disease dynamics have been quantified, but until recently no such data were available. Using a combination of data from recent experiments in which we quantified contact rates across a wide range of population densities in the rodent Mastomys natalensis  and the infection parameters of Morogoro virus (MORV) in this rodent , we tested the effect of different transmission functions using a simple SIR (susceptible, infectious, recovered) transmission model in annually fluctuating host populations. By implementing a range of hypothetical combinations of infectious period, transmission rate and population size, we assessed what the effects of the contact–density function would be for infections with different characteristics.