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Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

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Heterogeneous node responses to multi-type epidemics on networks.

S Moore¹, T Rogers¹

¹Centre for Networks and Collective Behaviour, Department of Mathematical Sciences, University of Bath, Bath BA27AY, UK.

Proceedings. Mathematical, Physical, and Engineering Sciences

|December 28, 2020

Summary

This summary is machine-generated.

Understanding infection spread in networks allows predicting individual infection risks. This study models multi-strain epidemics, revealing how network position and strain interactions influence infection likelihood for different nodes.

Keywords:

complex contagions epidemics networks

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Area of Science:

Epidemiology
Network Science
Computational Biology

Background:

Infection spread dynamics are often studied on contact networks.
Predicting individual susceptibility to infection is crucial for targeted interventions.
Simultaneous spread of multiple infection strains complicates prediction models.

Purpose of the Study:

To investigate heterogeneous outcomes in multi-type epidemic spreading processes.
To analyze how network structure and infection characteristics affect individual node infection likelihood.
To develop predictive models for complex epidemic scenarios.

Main Methods:

Derivation of message-passing equations for co-infection models.
Analysis of competing epidemic models where co-infection is impossible.
Investigating the factorization of node vulnerability in competing epidemics.

Main Results:

For co-infection models, message-passing equations predict infection likelihood based on node position and strain interactions.
For competing epidemics, node vulnerability simplifies to a product of network topology and infection parameters.
Demonstrated heterogeneity in infection outcomes across different network nodes.

Conclusions:

Network position and infection type interactions significantly influence individual infection risk.
Models of multi-type epidemics can predict heterogeneous infection probabilities.
Understanding these factors is key for effective epidemic control strategies.