SG: Density dependence and disease dynamics: moving towards a predictive framework

In humans and animals, a more active social life often means more exposure to disease. Likewise, living in high density areas increases one's chance of encountering disease-causing microorganisms ("pathogens"), thereby increasing the likelihood of getting sick. Scientists don't know, though, how often density drives the spread of infectious disease, and in what way. In many cases, animals in social groups may reduce their risk of infection - for example, by avoiding infected individuals or through improved nutrition - so perhaps population density may not matter very much. Understanding how density and disease relate to each other is necessary for predicting, modelling and controlling disease outbreaks (like COVID-19). Investigating whether pathogens restrict animals from forming more complex, denser societies will tell us how human disease burdens are likely to change as our societies become more urbanized. This is especially important in a densely populated world that is increasingly beset by novel infectious diseases. This research will investigate whether and how density drives greater infection within animal populations. Using a compiled collection of dozens of wildlife disease datasets, investigators will employ meta-analyses to ask 1) whether individuals in high-density areas have more pathogens; 2) whether specific interactions become more likely with higher host densities; and 3) whether these relationships can explain density effects for diseases spread by those interactions. In doing so, it will form the basis for predicting density-infection interactions, thereby improving general models of disease dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.