ArticleBMC ecology2013
A test of agent-based models as a tool for predicting patterns of pathogen transmission in complex landscapes.
Article in BMC ecology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 24 citations in OpenAlex.
- Spatial Landscape Structure Influences Cross-Species Transmission in a Rabies-like Virus Model.Microorganisms · 2025Article
- Ecosystem health appears neglected in the management of the human-macaque interface: A systematic review.One health (Amsterdam, Netherlands) · 2024Review
- Resource asynchrony and landscape homogenization as drivers of virulence evolution: The case of a directly transmitted disease in a social host.Ecology and evolution · 2024Article
- Movement can mediate temporal mismatches between resource availability and biological events in host-pathogen interactions.Ecology and evolution · 2021Article
- Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Interactive, open source, travel time scenario modelling: tools to facilitate participation in health service access analysis.International journal of health geographics · 2017Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLandscape complexity can mitigate or facilitate host dispersal, influencing patterns of pathogen transmission. Spatial transmission of pathogens through landscapes, therefore, presents an important but not fully elucidated aspect of transmission dynamics. Using an agent-based model (LiNK) that incorporates GIS data, we examined the effects of landscape information on the spatial patterns of host movement and pathogen transmission in a system of long-tailed macaques and their gut parasites. We first examined the role of the landscape to identify any individual or additive effects on host movement. We then compared modeled dispersal distance to patterns of actual macaque gene flow to both confirm our model's predictions and to understand the role of individual land uses on dispersal. Finally, we compared the rate and the spread of two gastrointestinal parasites, Entamoeba histolytica and E. dispar, to understand how landscape complexity influences spatial patterns of pathogen transmission.
resultsLiNK captured emergent properties of the landscape, finding that interaction effects between landscape layers could mitigate the rate of infection in a non-additive way. We also found that the inclusion of landscape information facilitated an accurate prediction of macaque dispersal patterns across a complex landscape, as confirmed by Mantel tests comparing genetic and simulated dispersed distances. Finally, we demonstrated that landscape heterogeneity proved a significant barrier for a highly virulent pathogen, limiting the dispersal ability of hosts and thus its own transmission into distant populations.
conclusionsLandscape complexity plays a significant role in determining the path of host dispersal and patterns of pathogen transmission. Incorporating landscape heterogeneity and host behavior into disease management decisions can be important in targeting response efforts, identifying cryptic transmission opportunities, and reducing or understanding potential for unintended ecological and evolutionary consequences. The inclusion of these data into models of pathogen transmission patterns improves our understanding of these dynamics, ultimately proving beneficial for sound public health policy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.