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Transportation Networks Optimized for Various Income Groups and their Impact on the Spread of Airborne Disease

With growing reliance on mass transit systems in American cities, the question of access
becomes more important. This study aims to explore the spread of an infectious disease across
a transportation network created to optimize access to most frequented destinations for distinct
socioeconomic groups. First, we develop a theoretical model of a city, based on the Kohl
model for urban growth which assumes distinct regions where income groups live and work.
It is assumed that all income groups in this city are transit-dependent. In this framework,
we maximize “satisfaction,” a measure of how easily the population of a neighborhood can
travel to desirable destinations, through placement of bus routes. Within this framework we
connect a single-outbreak multi-patch SIR model of Influenza A, incorporating the effects
of attraction and travel time into the incidence rate. We track the populations’ interactions
through contact within their neighborhoods, within the transit network, and with other transitconnected
neighborhoods. We observe how the basic reproductive number is affected by the
layout of the optimized transportation network. Results show that use of public transportation
largely does not affect the global epidemic but that more equal time spent in transit leads to
less disparate patch-specific epidemic outcomes.

 

Article Number: MTBI-14-04M

Year: 2017

Authors:

Jaysha Camacho - University of Florida, Gainesville, Florida

Rachel Matheson - Vassar College, Poughkeepsie, New York

Juliana Noguera - Los Andes University, Bogotá D.C., Colombia

Brandon Summers - North Carolina State University, Raleigh, North Carolina

Nanda Mallapragada - Arizona State University, Tempe, Arizona

Dr. Baojun Song - Montclair State University, Montclair, New Jersey

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