TY - JOUR
T1 - A stochastic agent-based model of pathogen propagation in dynamic multi-relational social networks
AU - Khan, Bilal
AU - Dombrowski, Kirk
AU - Saad, Mohamed
N1 - Funding Information:
This work was supported by NIH/NIDA Challenge Grant (grant number 1RC1DA028476-01/02) awarded to the CUNY Research Foundation and John Jay College, CUNY. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the National Institute of Health/National Institute on Drug Abuse. We would like to acknowledge that initial funding for a pilot version of this project was provided by the NSF Office of Behavioral, Social, and Economic Sciences, Anthropology Program (grant number BCS-0752680).
PY - 2014/4
Y1 - 2014/4
N2 - We describe a general framework for modeling and stochastic simulation of epidemics in realistic dynamic social networks, which incorporates heterogeneity in the types of individuals, types of interconnecting risk-bearing relationships, and types of pathogens transmitted across them. Dynamism is supported through arrival and departure processes, continuous restructuring of risk relationships, and changes to pathogen infectiousness, as mandated by natural history; dynamism is regulated through constraints on the local agency of individual nodes and their risk behaviors, while simulation trajectories are validated using system-wide metrics. To illustrate its utility, we present a case study that applies the proposed framework towards a simulation of HIV in artificial networks of intravenous drug users (IDUs) modeled using data collected in the Social Factors for HIV Risk survey.
AB - We describe a general framework for modeling and stochastic simulation of epidemics in realistic dynamic social networks, which incorporates heterogeneity in the types of individuals, types of interconnecting risk-bearing relationships, and types of pathogens transmitted across them. Dynamism is supported through arrival and departure processes, continuous restructuring of risk relationships, and changes to pathogen infectiousness, as mandated by natural history; dynamism is regulated through constraints on the local agency of individual nodes and their risk behaviors, while simulation trajectories are validated using system-wide metrics. To illustrate its utility, we present a case study that applies the proposed framework towards a simulation of HIV in artificial networks of intravenous drug users (IDUs) modeled using data collected in the Social Factors for HIV Risk survey.
KW - Agent-based systems
KW - Social Factors for HIV Risk
KW - modeling and simulation environments
KW - network-based simulation
KW - risk network
KW - system dynamics
UR - http://www.scopus.com/inward/record.url?scp=84899087347&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899087347&partnerID=8YFLogxK
U2 - 10.1177/0037549714526947
DO - 10.1177/0037549714526947
M3 - Article
C2 - 25859056
AN - SCOPUS:84899087347
SN - 0037-5497
VL - 90
SP - 460
EP - 484
JO - SIMULATION
JF - SIMULATION
IS - 4
ER -