Defining an epidemiological landscape by connecting host movement to pathogen transmission

Manlove, Kezia R.; Wilber, M.; White, Lauren A.; Bastille‐Rousseau, Guillaume; Yang, Anni; Gilbertson, Marie L. J.; Craft, Meggan E.; Cross, Paul C.; Wittemyer, George; Pepin, Kim M.

doi:10.22541/au.163458112.22651398/v1

Cited by 4 publications

(3 citation statements)

References 93 publications

(112 reference statements)

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“…There is a burgeoning recognition that our ability to quantify individual, spatial and temporal heterogeneity in transmission risk would benefit from a tighter link between movement and disease ecology (Dougherty et al, 2018; Manlove et al, 2021). Here, we developed MoveSTIR, a model that leverages commonly observed movement data to build direct and indirect contact networks across a continuum, derive spatially explicit metrics of transmission risk that can be linked to landscape variables, and decompose the temporal dynamics of potential infection hazard.…”

Section: Discussionmentioning

confidence: 99%

“…without accounting for variation in contact duration) might misspecify transmission risk. Despite the recent call to better incorporate movement data into disease ecology (Dougherty et al, 2018; Manlove et al, 2021), we still lack a generalizable, mechanistic model that can (i) leverage the diversity of available movement data to estimate the distinct contribution of each process to aggregate patterns of transmission relevant contact and (ii) determine how inferred patterns of spatial, temporal and individual‐level variability in transmission risk can affect population‐level pathogen invasion and persistence on heterogeneous landscapes. Such a model could not only improve our ability to account for realistic sources of variation when predicting transmission risk across landscapes for wildlife but also help leverage high‐resolution mobility data in human systems to better forecast outbreak dynamics and effects of interventions (Meekan et al, 2017; Miller et al, 2019; Wesolowski et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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A model for leveraging animal movement to understand spatio‐temporal disease dynamics

et al. 2022

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Host contact and transmission processes are fundamental drivers of pathogen emergence and spread (Begon et al., 2002;Hopkins et al., 2020; Mc-Callum et al., 2001), but the environmental forces shaping these drivers remain poorly understood. Determining how and why transmission rates vary across the landscape can identify potential transmission hotspots, determine which individuals are involved in their generation, and optimize disease control strategies (Parratt et al., 2016;Paull et al., 2012). However, obtaining precise, accurate and spatially explicit transmission metrics remains a major challenge in epidemiology and disease ecology (Albery et al., 2022).The increasing ubiquity of movement data in livestock and wildlife systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk (Dougherty et al., 2018;Jacoby & Freeman, 2016). The data range from highresolution GPS locations recording animal movements (Hooten et al., 2017), proximity loggers, camera traps or acoustic monitors that detect hosts in the vicinity of

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A model for leveraging animal movement to understand spatio‐temporal disease dynamics

et al. 2022

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“…This study addresses this by leveraging tools and analytical approaches from movement and disease ecology to reconcile the hierarchical structure of resource selection with variation in spatial behaviors exhibited by individual animals, rarely attempted before in an urban setting where the outcome of habitat selection impacts zoonotic hazard. With increased attention on translating movement mechanisms to spatial epidemiological modeling (Manlove et al 2022), we hope this work provides a foundation to formalize integrating movement and epidemiological datasets.…”

Section: Discussionmentioning

confidence: 99%

Resource selection by New York City deer reveals the effective interface between wildlife, zoonotic hazards, and humans

VanAcker

DeNicola²,

DeNicola³

et al. 2022

Preprint

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Although the role of host movement in shaping infectious disease dynamics is widely acknowledged, methodological separation between animal movement and disease ecology has prevented researchers from leveraging empirical insights from movement data to advance landscape-scale understanding of infectious disease risk. To address this knowledge gap, we examine how movement behavior and resource utilization by white-tailed deer (Odocoileus virginianus) determine the distribution of blacklegged ticks (Ixodes scapularis) which depend on this host for dispersal in a highly fragmented New York City borough. Multi-scale hierarchical resource selection analysis and movement modeling provide insight into how deer’s individual movements construct the risk landscape for human exposure to the Lyme disease zoonotic hazard – infected I. scapularis. We conclude the distribution of tick-borne disease risk is the result of individual resource selection by deer across spatial scales in response to habitat fragmentation and anthropogenic disturbances.

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Selection of movement rules to simulate species dispersal in a mosaic landscape model

Gold,

Croft,

Budgey

et al. 2024

Preprint

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Dispersal is an ecological process central to population dynamics, representing an important driver of movement between populations and across landscapes. In spatial population models for terrestrial vertebrates, capturing plausible dispersal behaviour is of particular importance when considering the spread of disease or invasive species. The distribution of distances travelled by dispersers, or the dispersal kernel, is typically highly skewed, with most individuals remaining close to their origin but some travelling substantially further. Using mechanistic models to simulate individual dispersal behaviour, the dispersal kernel can be generated as an emergent property. Through stepwise simulation of the entire movement path, models can also account for the influence of the local environment, and contacts during the dispersal event which may spread disease. In this study, we explore a range of simple rules to emulate individual dispersal behaviour within a mosaic model generated using irregular geometry. Movement rules illustrate a limited range of behavioural assumptions and when applied across these simple synthetic landscapes generated a wide range of emergent kernels. Given the variability in dispersal distances observed within species, our results highlight the importance of considering landscape heterogeneity and individual-level variation in movement, with simpler rules approximating random walks providing less plausible emergent kernels. As a case study, we demonstrate how rule sets can be selected by comparison to an empirical kernel for a study species (red fox;Vulpes vulpes). These results provide a foundation for the selection of movement rules to represent dispersal in spatial agent-based models, however, we also emphasise the need to corroborate rules against the behaviour of specific species and within chosen landscapes to avoid the potential for these rules to bias predictions.

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Defining an epidemiological landscape by connecting host movement to pathogen transmission

Cited by 4 publications

References 93 publications

A model for leveraging animal movement to understand spatio‐temporal disease dynamics

A model for leveraging animal movement to understand spatio‐temporal disease dynamics

Resource selection by New York City deer reveals the effective interface between wildlife, zoonotic hazards, and humans

Selection of movement rules to simulate species dispersal in a mosaic landscape model

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