<p>In both human and wildlife disease systems, temporal shifts in host immunity may shape the timing and severity of epidemics. Yet, immune responses, as well as seasonal patterns in their expression, are difficult to measure. Rather, field studies collect phenomenological data on infection outcomes. Pairing epidemic data of multiple outbreaks with models that directly parameterize immune metrics can be a powerful approach for exploring the role of time-varying immunity on disease. Field data can be used to determine how well a parameterized model can reproduce trends and differences observed among outbreaks.</p><p>Previous work in the <i>Daphnia dentifera</i>-<i>Metschnikowia bicuspidata</i> focal host-fungal pathogen disease system has not taken full advantage of coupling patterns in nature with mechanisms predicted by theory. Here, we study a mathematical model accounting for host immunity in the form of resistance to and recovery from <i>M. bicuspidata</i> infections and temporal variation in key aspects of the system’s epidemiology and ecology. Specifically, host population birth, predation and transmission rates, the fraction of recovering hosts, as well as the fungal spore yield were allowed to vary within the epidemic season. Modifying the system’s carrying capacity produces good correspondence between observed and model-estimated densities. Adjusting the transmission rate, spore yield, and the fraction of recovering hosts, captures the timing of disease outbreaks, as well as other qualitative features of outbreaks, such as the disparity between the prevalence of early- and late-stage infections. Our findings suggest that host immunological parameters are an important within-host constraint on disease dynamics.</p>