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Connecting network properties of rapidly disseminating epizoonotics
Ariel L Rivas1, Folorunso O Fasina, Almira L Hoogesteyn
1Center for Global Health, Health Sciences Center, University of New Mexico, Albuquerque, New Mexico, United States of America. alrivas@unm.edu
Plos One
|July 5, 2012
Summary
Understanding disease spread requires analyzing networks. This study used network theory to model infectious disease outbreaks, revealing key properties like connectivity and directionality for better control.
Area of Science:
- Epidemiology
- Network Theory
- Disease Ecology
Background:
- Effective infectious disease control necessitates understanding geographical spread patterns.
- Rapid microbial dispersal depends on susceptible hosts and pre-existing networks, including road infrastructure.
Purpose of the Study:
- To explore network properties influencing disease spread using road structure.
- To compare a connectivity model with a contact-based model for disease dissemination.
Main Methods:
- Utilized geo-temporal data from Foot-and-mouth disease (Uruguay, 2001) and Avian Influenza (Nigeria, 2006) epizoonotics.
- Developed a 'connectivity' model integrating bio-physical concepts and road topology.
- Compared the 'connectivity' model with a 'contacts' model that focused solely on infected individuals.
Main Results:
- The connectivity model identified five key network properties: spatial aggregation, assortativity, synchronicity, directionality, and a "20:80" case distribution.
- Both epizoonotics showed connected primary cases, with highly connected nodes accounting for most infections.
- The connectivity model identified twice as many cases as the contact model, with synchronicity and directionality explaining spread dynamics.
Conclusions:
- Geo-temporal network constructs (nodes and links) were validated for rapid infectious disease spread.
- Network analysis distinguished case classes, nodes, and networks, offering insights for theory revision and control optimization.
- Prospective studies incorporating pre-outbreak network predictors are recommended for enhanced disease management.
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