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Updated: Aug 15, 2026
14:58
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
Published on: March 5, 2022
Simulating the landscape eco-evolution of host-pathogen systems with CDMetaPOP
Erin L Landguth1, Allison Williams1,2, Marissa Roseman1,2
1Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, United States of America.
Ecological Informatics
|August 14, 2026
Summary
We developed CDMetaPOP-Disease, a new computational tool to simulate eco-evolutionary dynamics between wildlife, pathogens, and landscapes. This model links genetics to disease spread, aiding wildlife disease management.
Area of Science:
- Ecological informatics
- Computational epidemiology
- Population genetics
Background:
- Simulating eco-evolutionary feedbacks between wildlife hosts and pathogens in complex landscapes is computationally challenging.
- Existing frameworks lack mechanistic links between disease dynamics, landscape demography, and population genetics.
Purpose of the Study:
- Introduce CDMetaPOP-Disease, an open-source, spatially explicit, individual-based module.
- Mechanistically link individual genotypes to epidemiological transition rates for simulating host disease response strategies (resistance and tolerance).
Main Methods:
- Developed CDMetaPOP-Disease, an individual-based demo-genetic module.
- Verified module against SIR-type models (Nash-Sutcliffe Efficiency >= 0.98).
- Simulated novel pathogen emergence in a bat-pathogen system.
Main Results:
- Achieved high fidelity to theoretical expectations in model verification.
- Demonstrated the tool's capability to track pathogen diffusion and spatial-genetic signatures of host adaptation.
- Showcased the simulation of host resistance and tolerance strategies.
Conclusions:
- CDMetaPOP-Disease provides a flexible platform for investigating complex eco-evolutionary feedbacks.
- The module aids in understanding and managing infectious diseases in realistic landscapes.
- Facilitates research at the intersection of ecological informatics, epidemiology, and population genetics.
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