The COVID-19 pandemic has been a source of ongoing challenges and presents an increased risk of illness in group environments, including jails, long-term care facilities, schools, and residential college campuses. Early reports that the SARS-CoV-2 virus was detectable in wastewater in advance of confirmed cases sparked widespread interest in wastewater-based epidemiology (WBE) as a tool for mitigation of COVID-19 outbreaks. One hypothesis was that wastewater surveillance might provide a cost-effective alternative to other more expensive approaches such as pooled and random testing of groups. In this paper, we report the outcomes of a wastewater surveillance pilot program at the University of North Carolina at Charlotte, a large urban university with a substantial population of students living in on-campus dormitories. Surveillance was conducted at the building level on a thrice-weekly schedule throughout the university's fall residential semester. In multiple cases, wastewater surveillance enabled the identification of asymptomatic COVID-19 cases that were not detected by other components of the campus monitoring program, which also included in-house contact tracing, symptomatic testing, scheduled testing of student athletes, and daily symptom reporting. In the context of all cluster events reported to the University community during the fall semester, wastewater-based testing events resulted in the identification of smaller clusters than were reported in other types of cluster events. Wastewater surveillance was able to detect single asymptomatic individuals in dorms with resident populations of 150–200. While the strategy described was developed for COVID-19, it is likely to be applicable to mitigation of future pandemics in universities and other group-living environments.

Animal movement is a complex spatiotemporal phenomenon that has intrigued researchers from many disciplines. Interactions among animals, and between animals and the environments that they traverse, play an important role in the development of the complex ecological and social systems in which they are embedded. Agent-based models have been increasingly applied as a computational approach to the study of animal movement across landscapes. In this article, we present a review of agent-based models in which the simulation of animal movement processes and patterns is the central theme. Our discussion of these processes is focused on four key components: internal states, external factors, motion capacities, and navigation capacities. These four components have been identified in the emerging movement ecology research paradigm and are important for modeling animal movement behavior. Because agent-based models allow for an individual-based approach that encapsulates these four components, the underlying processes that drive animal behavior can be deeply explored using this technique. A set of challenges and issues remain, however, for agent-based models of animal movement. In this article, we review the existing literature and identify potential research directions that could help address these challenges.

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