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Abstract
Environment drives the host movements that shape pathogen transmission through three mediating processes: host density, host mobility, and contact. These processes combine with pathogen life-history to give rise to an “epidemiological landscape” that determines spatial patterns of pathogen transmission. Yet despite its central role in transmission, strategies for predicting the epidemiological landscape from real-world data remain limited. Here, we develop the epidemiological landscape as an interface between movement ecology and spatial epidemiology. We propose a movement-pathogen pace-of-life heuristic for prioritizing the landscape’s central processes by positing that spatial dynamics for fast pace-of-life pathogens are best-approximated by the spatial ecology of host contacts; spatial dynamics for slower pace-of-life pathogens are best approximated by host densities; and spatial dynamics for pathogens with environmental reservoirs reflect a convolution of those densities with the spatial configuration of environmental reservoir sites. We then identify mechanisms that underpin the epidemiological landscape and match each mechanism to emerging tools from movement ecology. Finally, we outline workflows for describing the epidemiological landscape and using it to predict subsequent patterns of pathogen transmission. Our framework links transmission to environmental context, providing a scaffold for mechanistically understanding how the environment can generate and shift existing patterns in spatial epidemiology.
Introduction
Environment shapes the spatial, temporal, and behavioral structure of host movements and pathogen persistence, leading transmission to occur in some locations but not in others. The particular role the environment plays depends on the movement ecology of the host and pathogen: wildlife hosts can shed pathogens as they track resources across the landscape; livestock pathogens can jump abruptly between habitat patches as hosts are transported from farm to feedlot; and transmission of human pathogens often shows spatial signatures associated with work, caregiving, and social engagement. Environment also structures disease management efforts targeting specific locations that could give rise to super-spreading events or transmission hotspots, curtail the spread of invading epidemic “waves”, or capitalize on predictable fluctuations in local transmission patterns.
However, despite these strong implicit connections, our understanding of the environment-movement-transmission interface remains in its infancy. Although movement ecology offers an array of tools that could help integrate environment, host movement, and pathogen deposition, the tools’ outputs are somewhat detached from critical aspects of spatial transmission and some important components of spatial transmission are largely overlooked. Moreover, it is unclear how to best integrate the various processes each tool infers for optimal epidemiological predictions. Quantifying the environment’s role in spatial dynamics of transmission, appropriately combining constituent processes, and identifying relevant mechanisms will require a stronger integration of movement ecology into conventional epidemiological frameworks.
There is a reason why movement and disease ecology developed separately to this point: they are interested in fundamentally different relationships. Movement ecology usually explores how individuals interact with their environments (Nathan et al. 2008), whereas disease ecology focuses on how individuals interact with one another to allow for pathogen transmission. Scaling individual-environment interactions up to predict individual-individual interactions is a long-standing challenge, and one that potentially distracts from other transmission-related insights that movement ecology has to offer. That lacuna is symptomatic of a broader interdisciplinary divide: the spatial modeling community lacks a systematic way to incorporate mechanistic and empirical insights from movement ecology into spatial models of transmission while retaining tractability and transportability beyond the specific system or landscape at hand. This gap impedes our ability to identify general principles about how the environment – both the local habitat and its broader scale landscape structure — shapes spatial patterns of pathogen transmission in real-world environments.
Spatial transmission dynamics depend on three central processes: host density processes that describe where hosts are located across the landscape, mobility processes that describe when and how hosts move among sites, and contact processes that describe the frequency, duration and form of host-host interactions and how those interactions relate to rates of pathogen transmission. Together, the host density, mobility, and contact processes combine with pathogen life-history to form an epidemiological landscape that interacts with the local volume of infected hosts to determine epidemiological dynamics through space and time (Fig. 1). Because it does not incorporate the volume of infected individuals directly, the epidemiological landscape exists apart from any specific disease event. Instead, it summarizes the potential for particular spatial transmission patterns to arise in a given environment on the basis of local host density, mobility, and contact. Identifying how density, mobility, and contact relate to the environment, however, is an open challenge that movement ecology can help to address.
Here, we propose a strategy to strengthen the interface between environment and transmission by focusing explicitly on the mechanisms and traits surrounding the epidemiological landscape. First, we use the epidemiological landscape to formalize the mechanistic relations that underpin transportable models. We then propose an heuristic that we refer to as the movement-pathogen pace-of-life hypothesis that suggests how to prioritize the density, mobility, and contact processes according to important pathogen life history attributes. Next, we identify a set of critical mechanisms determining the epidemiological landscape’s structure, and match both the epidemiological landscape’s central processes and our proposed mechanisms to metrics and inferential tools from movement ecology in sections organized around two research priorities (Fig. 2). The first priority is to infer specific aspects of pathogen transmission using existing movement ecology tools; the second is to determine how social context contributes to patterns of density, mobility, and contact. Finally, we outline three workflows that blend mechanisms with movement and transmission data to generate epidemiological forecasts that are transportable across landscapes and populations (Fig. 3), and end by identifying existing challenges and open questions.