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Updated: Jul 12, 2026
07:40
Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
Published on: October 29, 2016
Host-parasite interactions between the local and the mean-field: how and when does spatial population structure
Steven D Webb1, Matt J Keeling, Mike Boots
1Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, UK. sdw@maths.strath.ac.uk
Journal of Theoretical Biology
|August 28, 2007
Summary
Local interactions in host-parasite systems reduce disease persistence and increase extinction risk. Even in mostly mixed populations, spatial effects significantly alter disease dynamics, challenging the mean-field approximation.
Area of Science:
- Ecology
- Epidemiology
- Mathematical Biology
Background:
- Population mixing assumptions are common but often oversimplify real-world spatial and social interactions.
- Understanding the impact of local interactions on disease dynamics is crucial for ecological and epidemiological modeling.
Purpose of the Study:
- To investigate how the strength of local interactions influences host-parasite system dynamics.
- To determine the conditions under which spatial effects significantly alter disease persistence and population cycles.
Main Methods:
- Utilized a multi-scale pair-approximation model to simulate host-parasite interactions across a spectrum from local to completely mixed.
- Analyzed the effects of varying infection scales and host reproduction patterns on disease dynamics.
Main Results:
- Systems with strong local interactions exhibit reduced disease persistence and increased parasite-driven extinctions and limit cycles.
- These effects persist across a wide range of infection scales, even in predominantly mixed populations.
- Deterministic extinctions and oscillations are more likely in highly spatial systems with local host reproduction.
Conclusions:
- Local spatial structure significantly impacts host-parasite dynamics, leading to less persistent diseases.
- The mean-field approximation may be valid for many systems, but its applicability is highly dependent on the ecological context and degree of spatial interaction.
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