An Agent-Based Epidemic Simulation of Social Behaviors Affecting HIV Transmission among Taiwanese Homosexuals

Computational simulations are currently used to identify epidemic dynamics, to test potential prevention and intervention strategies, and to study the effects of social behaviors on HIV transmission. The author describes an agent-based epidemic simulation model of a network of individuals who participate in high-risk sexual practices, using number of partners, condom usage, and relationship length to distinguish between high- and low-risk populations. Two new concepts—free links and fixed links—are used to indicate tendencies among individuals who either have large numbers of short-term partners or stay in long-term monogamous relationships. An attempt was made to reproduce epidemic curves of reported HIV cases among male homosexuals in Taiwan prior to using the agent-based model to determine the effects of various policies on epidemic dynamics. Results suggest that when suitable adjustments are made based on available social survey statistics, the model accurately simulates real-world behaviors on a large scale.

[1]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[2]  Yiming Shao,et al.  Changing the Landscape of the HIV Epidemic among MSM in China: Results from Three Consecutive Respondent-Driven Sampling Surveys from 2009 to 2011 , 2014, BioMed research international.

[3]  T. Yamamoto,et al.  Fighting a rising tide: the response to AIDS in East Asia. , 2006 .

[4]  SF Lai,et al.  Molecular Epidemiology of HIV-1 Infection among Injecting Drug Users in Taiwan-Report of an Emergent Situation , 2005 .

[5]  M. Handcock,et al.  An assessment of preferential attachment as a mechanism for human sexual network formation , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[6]  P. Bearman,et al.  Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks1 , 2004, American Journal of Sociology.

[7]  Herbert W. Hethcote,et al.  Epidemic models: Their structure and relation to data , 1996 .

[8]  C. Dolea,et al.  World Health Organization , 1949, International Organization.

[9]  R. Rothenberg,et al.  Risk network structure in the early epidemic phase of HIV transmission in Colorado Springs , 2002, Sexually transmitted infections.

[10]  M. Aliyu,et al.  Dynamics of the HIV Epidemic in MSM , 2014, BioMed research international.

[11]  M. Newman.  Models of the Small World: A Review , 2000, cond-mat/0001118.

[12]  L. Amaral,et al.  The web of human sexual contacts , 2001, Nature.

[13]  H. Eugene Stanley,et al.  Distributions of number of sexual partnerships have power law decaying tails and finite variance , 2003 .

[14]  S C Brailsford,et al.  Simulation modelling for HIV infection and AIDS. , 1992, International journal of bio-medical computing.

[15]  S. Gupta,et al.  Preliminary studies of sexual networks in a male homosexual community in Iceland. , 1992, Journal of acquired immune deficiency syndromes.

[16]  Catherine H Mercer,et al.  Scale-Free Networks and Sexually Transmitted Diseases: A Description of Observed Patterns of Sexual Contacts in Britain and Zimbabwe , 2004, Sexually transmitted diseases.

[17]  Zhou Hui.  Research of the Small-World Character During Rumor’s Propagation , 2005 .

[18]  W. O. Kermack,et al.  Contributions to the mathematical theory of epidemics—I , 1991, Bulletin of mathematical biology.

[19]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[20]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[21]  Luís A. Nunes Amaral,et al.  Sexual networks: implications for the transmission of sexually transmitted infections. , 2003, Microbes and infection.

[22]  Duncan J. Watts,et al.  Six Degrees: The Science of a Connected Age , 2003 .

[23]  J. Wylie,et al.  Patterns of Chlamydia and Gonorrhea Infection in Sexual Networks in Manitoba, Canada , 2001, Sexually transmitted diseases.

[24]  W. McIsaac,et al.  Effect of an Explicit Decision-Support Tool on Decisions to Prescribe Antibiotics for Sore Throat , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.

[25]  D Peterson,et al.  Monte Carlo simulation of HIV infection in an intravenous drug user community. , 1990, Journal of acquired immune deficiency syndromes.

[26]  Chung-Yuan Huang,et al.  Influence of Local Information on Social Simulations in Small-World Network Models , 2005, J. Artif. Soc. Soc. Simul..

[27]  P. V. Marsden,et al.  NETWORK DATA AND MEASUREMENT , 1990 .

[28]  Chung-Yuan Huang,et al.  Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments , 2004, J. Artif. Soc. Soc. Simul..

[29]  T. Snijders.  Statistical Models for Social Networks , 2011 .

[30]  M. Kretzschmar,et al.  Sexual Network Structure and Sexually Transmitted Disease Prevention: A Modeling Perspective , 2000, Sexually transmitted diseases.

[31]  J. Catania,et al.  Condom use in multi-ethnic neighborhoods of San Francisco: the population-based AMEN (AIDS in Multi-Ethnic Neighborhoods) Study. , 1992, American journal of public health.

[32]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[33]  L. Amaral,et al.  Sexual contacts and epidemic thresholds , 2003 .

[34]  J. Potterat,et al.  Social networks and infectious disease: the Colorado Springs Study. , 1994, Social science & medicine.

[35]  SF Lai,et al.  Molecular epidemiology of HIV-1 in men who have sex with men from gay saunas in Taiwan from 2000 to 2003 , 2004 .

[36]  M. Morris,et al.  Sexual networks and HIV. , 1997, AIDS.

[37]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Mark Newman,et al.  Models of the Small World , 2000 .

[39]  M. Newman,et al.  Scaling and percolation in the small-world network model. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[40]  S. Shiboski,et al.  Population- and individual-based approaches to the design and analysis of epidemiologic studies of sexually transmitted disease transmission. , 1996, The Journal of infectious diseases.

[41]  Tshilidzi Marwala,et al.  Agent-based modelling: a case study in HIV epidemic , 2004, Fourth International Conference on Hybrid Intelligent Systems (HIS'04).

[42]  Gong Xiao.  Study on Multi-Agent Simulation of New Product Market Diffusion , 2003 .