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A Mouse Model for Pathogen-induced Chronic Inflammation at Local and Systemic Sites

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Published on: August 8, 2014

Percolation models of pathogen spillover.

Alex D Washburne¹, Daniel E Crowley¹, Daniel J Becker^1,2,3

¹Department of Microbiology and Immunology, Montana State University, Bozeman, MT, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|August 13, 2019

Summary

This summary is machine-generated.

We introduce percolation models to understand pathogen spillover dynamics. These models link statistical analysis with mechanistic insights, improving predictions of cross-species transmission events.

Keywords:

generalized linear model multilevel model percolation probability regression spillover

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Area of Science:

Ecology and Evolutionary Biology
Epidemiology
Mathematical Biology

Background:

Predicting pathogen spillover requires linking event counts to covariates.
Mechanistic models are needed to understand pathogen transmission from reservoir to recipient hosts.

Purpose of the Study:

To present percolation models for pathogen spillover.
To connect statistical analysis of spillover data with mechanistic modeling.
To inform predictions of spillover risk.

Main Methods:

Representing spillover pathways as directed graphs.
Modeling spillover events as percolation of infectious units.
Utilizing piecewise linear statistical models (e.g., GAMs) for analysis.

Main Results:

Percolation models formalize concepts like multilevel transmission and covariate covariance.
Linear models for parameters yield nonlinear spillover rates, approximately piecewise linear.
Piecewise linearity impacts statistical inference on covariates like vector density.

Conclusions:

Percolation models offer a framework linking statistical and mechanistic approaches to spillover.
Statistical analysis should employ piecewise linear models to match percolation model predictions.
Findings have implications for forecasting spillover risk, especially in the Anthropocene.