Using Community Structure Networks to Model Heterogeneous Mixing in Epidemics, and a Potential Application to HIV in Washington, D.C.
Year: 2015
Type: article
Abstract: Using models, mathematicians can better understand and analyze the factors that influence the dynamic spread of infectious disease through a population. The most fundamental epidemiological model is the SIR model, originally proposed by Kermack and McKendrick. In this model individuals in a population are categorized as Susceptible (S), Infected (I), or Removed (R), and differential equations are used to analyze the flow of people from one compartment to another. Many epidemiological models use the SIR model as a foundation, building complexities into it. Modeling HIV, for example, is complex because not all people in a population are at equal risk for infection. Many SIR-like models assume that each member of a population is equally likely to interact with any other member of the population. However, in reality, there are many factors such as race, neighborhood, profession, socio-economic status, religion, education, and more that impact how likely one person is to interact with another. One increasingly popular way to analyze the effects of heterogeneous mixing is by using networks. Inspired by data suggesting that HIV incidence in Washington, D.C. is skewed by race and neighborhood, this paper explores the effects of community structure in networks on transmission dynamics.
Source: SCARAB (Bates College)
Author: Katherine Ragland Paulson
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1127986287