The Macroecology of Infectious Disease:  Drawing on the breadth of expertise in the network, one of our first aims is to develop a paper that explores the challenge and promise of applying a macroecological perspective to disease ecology. Macroecology is the field of ecology that deals with regional scale patterns in the distribution and diversity of species. Predicting the emergence of novel infectious diseases is a critical global health and economic issue. It follows that understanding the basic rules that govern the distribution and transmission of diseases at large spatial scales is one of the most important topics facing modern science. Despite this, patterns of disease biodiversity have rarely been explored at macroecological scales. We still know next to nothing about fundamental questions of large scale disease ecology such as how global patterns of disease biodiversity vary among different host groups, what traits facilitate transmission among host species, and what ecological and environmental factors drive regional variation in disease diversity. Though answering these questions will require significant effort in data collection and methodological development, for the first time in history it seems feasible to construct accurate global maps of disease biodiversity. We will review these and related topics in a paper we aim to publish this year.

The Globalization of Disease: Some diseases have benefitted greatly from human globalization while others have barely been affected by it. As the globalization continues to create homogenous human and non-human communities, which diseases can we expect to become globally ubiquitous and which will remain locally distributed? We are developing an empirical and theoretical framework to identify the characteristics of pathogens that we can expect to flourish as human globalization continues its inevitable march forward.

Using Machine Learning to Predict Host-Parasite Associations: One of the greatest challenges that studies of large scale disease diversity face is accurately quantifying host parasite associations among species that have been studied to greatly different degrees. For example, rabies has been the subject of hundreds of studies, whereas many other parasites are known from only a single study. A similar dichotomy applies to different host species (e.g., white tailed deer versus muntjaks). A class of machine learning methods has been developed for dealing with presence absence data with similar quality concerns, but have not yet been applied in disease ecology. Current activities are focused on using machine learning to place confidence limits on the completeness of host parasite association networks that Research Coordination Network (RCN) participants have constructed from literature reviews.

Mapping Parasite Biodiversity: One of the primary goals of the RCN is to generate accurate global maps of parasite biodiversity. A number of methodological and data quality concerns will have to be addressed before this will be possible. Focusing on one of the most complete disease data sets available to the group, the Global Mammal Parasite Database (GMPD), we will attempt to generate some of the first regional and global maps of parasite species richness. Current activities are focused on the final georeferencing of the latest GMPD entries.