The world is changing rapidly, accelerating our need to understand how factors like climate, land use, and social structure affect the ecology of disease on a global stage. Interrogating these processes requires broad analyses to ensure that results are generalisable and complexities are well-understood. However, often, these analyses use relatively coarse data that are fraught with sampling biases and are restricted in their ability to inform finer-scale processes. These problems can be addressed by aggregating large-scale meta-datasets of fine-scale disease sampling, allowing researchers to draw generalisable conclusions across a range of hosts, parasites, and environments.
I describe a number of efforts to aggregate and analyse such datasets, including meta-datasets of individual-level infections, between-individual behavioural interactions, and population-level prevalence data, each of which has been used to understand dimensions of fundamental disease dynamics and the consequences of global change. I compare these approaches to more common and coarser analyses, identifying the benefits they convey and the data infrastructure that they require. Ultimately, I outline an integrative framework for “bottom-up” mechanistic prediction of disease dynamics in novel systems, using fine-scale analyses to inform the most important global-scale problems.