Gregory Albery

High population density is thought to exacerbate parasite exposure rates, leading to increased transmission and greater disease burdens. Different types of interactions exhibit different relationships with density, and therefore so do parasites that are spread by these interactions. Epidemiological models often assume a given density-transmission relationship, and the validity of this assumption impacts the accuracy of a model’s predictions. Despite its foundational relevance to epidemiology and disease ecology, density-transmission functions are generally identified post hoc rather than being predictable in advance. Developing a framework for predicting the shape and slope of these relationships could expedite epidemiological responses and improve ecological understanding. Such a framework must allow for both positive and negative correlations between density and infection, originating from non-linear changes in exposure, susceptibility, and a range of other confounders. Here, I argue that a general predictive framework is possible, built “bottom-up” from analyses of spatial and social behaviours. To lay the foundation, I define density dependence according to both spatial and social dimensions of behaviour, I present a series of challenges to address, and I outline a coherent integrative framework to conceptualise and understand density dependence of infection. Finally, I present suggestions for future work, including the collection, mining, and collation of cross-system behavioural and infection data, and experimental approaches that would allow us to extricate density’s effects from those of population size and a range of other confounders. Implementing these investigations may allow us to anticipate the epidemiological properties of a wide range of known and unknown parasites, as well as informing the uncertain future of human and animal disease in a rapidly changing and ever-densifying world.