Simulation of HIV Infection Propagation Networks: A Review of the State of the Art in Agent-Based Approaches

Simulation of HIV Infection Propagation Networks: A Review of the State of the Art in Agent-Based Approaches

Alfredo Tirado-Ramos (School of Medicine, Emory University, Atlanta, GA, USA) and Chris Kelley (School of Medicine, Emory University, Atlanta, GA, USA)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/jats.2013010104
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Abstract

Simulating the transmission of HIV requires a model framework that can account for the complex nature of HIV transmission. In this paper the authors present the current state of the art for simulating HIV with agent-based models and highlight some of the significant contributions of current research. The authors then propose opportunities for future work including their plan that involves identifying and monitoring high-risk drug users that can potentially initiate high-risk infection propagation networks.
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2. Background

According to the World Health Organization, in 2011 there are 34.2 million people living with AIDS, 1.7 million AIDS deaths, and 2.5 million people newly infected with HIV in 2011. Since the 1980s, scientists have used simulation models to learn more about HIV and to aid in predicting the spread of the HIV epidemic. HIV is unique, as it does not fit traditional epidemiological models for disease. In other words, it is not transmitted by air or casual contact. Instead, the transmission of HIV is mainly a result of human behavior, with the exception of mother-to‐child infection and other lower risk routes such as blood transfusions. Infection typically occurs through human behaviors such as unprotected sexual intercourse or sharing intravenous drug needles. The ability to simulate human behavior makes agent-based models an effective model for HIV transmission. Agent-based simulations consist of individual autonomous agents that can be designed to have human characteristics and exhibit multi-faceted behaviors of human beings. Agents can interact with other agents and agents and therefore transmit disease and influence behavior. This interaction simulates the complex social and sexual networks of individuals. Agents attributes that impact behavior can also be calibrated by environmental or population parameters. Agent-based models are typically defined by a common structure that consist of some or all of the following components for simulation:

  • Agents that with demographic, behavioral, and other parameters;

  • HIV transmission algorithms or equations that execute the transmission of HIV between agents;

  • A unit of time that is used to simulate the passing of time in the model;

  • A network structure that represent relationships between agents;

  • A function for updating dynamic network structures as agents enter and leave the population;

  • A notion of HIV disease progression. Some stages besides not infected and infected are sometimes represented in order to represent details that impact transmission;

  • Population input parameters to incorporate specific characteristics of the population.

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