2006, 3(3): 459-466. doi: 10.3934/mbe.2006.3.459

Modeling the potential impact of rectal microbicides to reduce HIV transmission in bathhouses

1. 

Department of Biomathematics, David Ge®en School of Medicine at UCLA, Los Angeles, California, 90024, United States

2. 

Center for HIV and Digestive Diseases, Division of Digestive Diseases, The UCLA AIDS Institute, David Ge®en School of Medicine at UCLA, Los Angeles, California, 90020, United States

3. 

Department of Mathematics, UCLA, Los Angeles, California, 90020, United States

4. 

Department of Biomathematics and Department of Biostatistics, David Geffen School of Medicine at UCLA, Los Angeles, California, 90020, United States

5. 

Center for HIV and Digestive Diseases, Division of Digestive Diseases, The UCLA AIDS Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90020, United States

6. 

Department of Biomathematics & the UCLA AIDS Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90024, United States

Received  March 2005 Revised  January 2006 Published  May 2006

We evaluate the potential impact of rectal microbicides for reducing HIV transmission in bathhouses. A new mathematical model describing HIV transmission dynamics among men who have sex with men (MSM) in bathhouses is constructed and analyzed. The model incorporates key features affecting transmission, including sexual role behavior (insertive and receptive anal intercourse acts), biological transmissibility of HIV, frequency and efficacy of condom usage, and, most pertinently, frequency and efficacy of rectal microbicide usage. To evaluate the potential impact of rectal microbicide usage, we quantify the effect of rectal microbicides (ranging in efficacy from 10% to 90%) on reducing the number of HIV infections in the bathhouse. We conduct uncertainty analyses to assess the effect of variability in both biological and behavioral parameters. We find that even moderately effective rectal microbicides (if used in 10% to 50% of the sex acts) would substantially reduce transmission in bathhouses. For example, a 50% effective rectal microbicide (used in 50% of sex acts) would reduce the number of secondary infections by almost 13% at disease invasion. Our modeling analyses show that even moderately effective rectal microbicides could be very effective prevention tools for reducing transmission in bathhouses and also potentially limit the spread of HIV in the community.
Citation: Romulus Breban, Ian McGowan, Chad Topaz, Elissa J. Schwartz, Peter Anton, Sally Blower. Modeling the potential impact of rectal microbicides to reduce HIV transmission in bathhouses. Mathematical Biosciences & Engineering, 2006, 3 (3) : 459-466. doi: 10.3934/mbe.2006.3.459
[1]

Jinhu Xu, Yicang Zhou. Bifurcation analysis of HIV-1 infection model with cell-to-cell transmission and immune response delay. Mathematical Biosciences & Engineering, 2016, 13 (2) : 343-367. doi: 10.3934/mbe.2015006

[2]

Tinevimbo Shiri, Winston Garira, Senelani D. Musekwa. A two-strain HIV-1 mathematical model to assess the effects of chemotherapy on disease parameters. Mathematical Biosciences & Engineering, 2005, 2 (4) : 811-832. doi: 10.3934/mbe.2005.2.811

[3]

Bing Li, Yuming Chen, Xuejuan Lu, Shengqiang Liu. A delayed HIV-1 model with virus waning term. Mathematical Biosciences & Engineering, 2016, 13 (1) : 135-157. doi: 10.3934/mbe.2016.13.135

[4]

Rachid Ouifki, Gareth Witten. A model of HIV-1 infection with HAART therapy and intracellular delays. Discrete & Continuous Dynamical Systems - B, 2007, 8 (1) : 229-240. doi: 10.3934/dcdsb.2007.8.229

[5]

Jinliang Wang, Lijuan Guan. Global stability for a HIV-1 infection model with cell-mediated immune response and intracellular delay. Discrete & Continuous Dynamical Systems - B, 2012, 17 (1) : 297-302. doi: 10.3934/dcdsb.2012.17.297

[6]

Xia Wang, Shengqiang Liu, Libin Rong. Permanence and extinction of a non-autonomous HIV-1 model with time delays. Discrete & Continuous Dynamical Systems - B, 2014, 19 (6) : 1783-1800. doi: 10.3934/dcdsb.2014.19.1783

[7]

Shengqiang Liu, Lin Wang. Global stability of an HIV-1 model with distributed intracellular delays and a combination therapy. Mathematical Biosciences & Engineering, 2010, 7 (3) : 675-685. doi: 10.3934/mbe.2010.7.675

[8]

Huiyan Zhu, Xingfu Zou. Dynamics of a HIV-1 Infection model with cell-mediated immune response and intracellular delay. Discrete & Continuous Dynamical Systems - B, 2009, 12 (2) : 511-524. doi: 10.3934/dcdsb.2009.12.511

[9]

Yun Tian, Yu Bai, Pei Yu. Impact of delay on HIV-1 dynamics of fighting a virus with another virus. Mathematical Biosciences & Engineering, 2014, 11 (5) : 1181-1198. doi: 10.3934/mbe.2014.11.1181

[10]

Jie Lou, Tommaso Ruggeri, Claudio Tebaldi. Modeling Cancer in HIV-1 Infected Individuals: Equilibria, Cycles and Chaotic Behavior. Mathematical Biosciences & Engineering, 2006, 3 (2) : 313-324. doi: 10.3934/mbe.2006.3.313

[11]

Gesham Magombedze, Winston Garira, Eddie Mwenje. Modelling the immunopathogenesis of HIV-1 infection and the effect of multidrug therapy: The role of fusion inhibitors in HAART. Mathematical Biosciences & Engineering, 2008, 5 (3) : 485-504. doi: 10.3934/mbe.2008.5.485

[12]

Oluwaseun Sharomi, Chandra N. Podder, Abba B. Gumel, Baojun Song. Mathematical analysis of the transmission dynamics of HIV/TB coinfection in the presence of treatment. Mathematical Biosciences & Engineering, 2008, 5 (1) : 145-174. doi: 10.3934/mbe.2008.5.145

[13]

Helen Moore, Weiqing Gu. A mathematical model for treatment-resistant mutations of HIV. Mathematical Biosciences & Engineering, 2005, 2 (2) : 363-380. doi: 10.3934/mbe.2005.2.363

[14]

Aditya S. Khanna, Dobromir T. Dimitrov, Steven M. Goodreau. What can mathematical models tell us about the relationship between circular migrations and HIV transmission dynamics?. Mathematical Biosciences & Engineering, 2014, 11 (5) : 1065-1090. doi: 10.3934/mbe.2014.11.1065

[15]

Zindoga Mukandavire, Abba B. Gumel, Winston Garira, Jean Michel Tchuenche. Mathematical analysis of a model for HIV-malaria co-infection. Mathematical Biosciences & Engineering, 2009, 6 (2) : 333-362. doi: 10.3934/mbe.2009.6.333

[16]

Xinyue Fan, Claude-Michel Brauner, Linda Wittkop. Mathematical analysis of a HIV model with quadratic logistic growth term. Discrete & Continuous Dynamical Systems - B, 2012, 17 (7) : 2359-2385. doi: 10.3934/dcdsb.2012.17.2359

[17]

Shingo Iwami, Shinji Nakaoka, Yasuhiro Takeuchi. Mathematical analysis of a HIV model with frequency dependence and viral diversity. Mathematical Biosciences & Engineering, 2008, 5 (3) : 457-476. doi: 10.3934/mbe.2008.5.457

[18]

Tyson Loudon, Stephen Pankavich. Mathematical analysis and dynamic active subspaces for a long term model of HIV. Mathematical Biosciences & Engineering, 2017, 14 (3) : 709-733. doi: 10.3934/mbe.2017040

[19]

Najat Ziyadi, Said Boulite, M. Lhassan Hbid, Suzanne Touzeau. Mathematical analysis of a PDE epidemiological model applied to scrapie transmission. Communications on Pure & Applied Analysis, 2008, 7 (3) : 659-675. doi: 10.3934/cpaa.2008.7.659

[20]

Yingke Li, Zhidong Teng, Shigui Ruan, Mingtao Li, Xiaomei Feng. A mathematical model for the seasonal transmission of schistosomiasis in the lake and marshland regions of China. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1279-1299. doi: 10.3934/mbe.2017066

2017 Impact Factor: 1.23

Metrics

  • PDF downloads (5)
  • HTML views (0)
  • Cited by (0)

[Back to Top]