Modelling in concentrated epidemics: Informing epidemic trajectories and assessing prevention approaches

Current Opinion in HIV and AIDS

Volumn 9, Issue 2, 2014, Pages 134-149

  Boily, Marie Claude ^a   Shubber, Zara ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

concentrated epidemic; HIV AIDS; key populations; low prevalence; mathematical model

Indexed keywords

ADULT; EPIDEMICS; FEMALE; HIV INFECTIONS; HUMANS; MALE; MODELS, BIOLOGICAL; YOUNG ADULT;

EID: 84894097506     PISSN: 1746630X     EISSN: 17466318     Source Type: Journal    
DOI: 10.1097/COH.0000000000000036     Document Type: Review

References (87)

1. Joint United Nations Programme on HIV/AIDS (UNAIDS), World AIDS Day Report 2012, 2012. http://www.unaids.org/en/media/unaids/contentassets/ documents/epidemiology/2012/gr2012/jc2434-worldaidsday-results-en. pdf. [Accessed 20 October 2013]
2. Baral S, Sifakis F, Cleghorn C, Beyrer S. Elevated risk for HIV infection among men who have sex with men in low-and middleincome countries 2000-2006: a systematic review. PLoS Med 2007; 4: e339.
3. - 3.Baral S, Beyrer C, Muessig K, et al. Burden of HIV among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Infect Dis 2012; 12:538-549. A useful review of the burden of HIV among FSW.
4. Baral S, Poteat T, Strömdahl S, et al. Worldwide burden of HIV in transgender women: a systematic review and meta-analysis. Lancet Infect Dis 2013; 13:214-222.
5. Mathers B, Degenhardt L, Phillips B, et al. Global epidemiology of injecting drug use and HIV among people who inject drugs: a systematic review. Lancet 2008; 372:1733-1745.
6. Gouws E, Cuchi P. Focusing the HIV response through estimating the major modes of HIV transmission: a multicountry analysis. Sex Transm Infect 2012; 88 (Suppl 2):i76-i85.
7. - 7.Shubber Z, Mishra S, Vesga J, Boily MC. The HIV modes of transmission model: a systematic review of its findings and adherence to guidelines. J Int AIDS Soc, (provisionally accepted). A critical analytic review of the transmission model highlighting the surprising homogeneity in estimates of the burden of HIV among FSWs across settings, even where the HIV epidemic is known to be driven by sex work.
8. Prüss-Ustün A, Wolf J, Driscoll T, et al. HIV due to female sex work: regional and global estimates. PLoS ONE 2013; 8:e63476.
9. Bergenstrom AM, Adul-Quader AS. Injection drug use, HIV and the current response in selected low-income and middle-income countries. AIDS 2010; 24 (Suppl 3):S20-S29.
10. Joint United Nations Programme on HIV/AIDS (UNAIDS), GLOBAL REPORT: UNAIDS report on the global AIDS epidemic 2013, 2013: Geneva. http://www.unaids.org/en/media/unaids/contentassets/documents/epidemiology/2013/ gr2013/UNAIDS-Global-Report-2013-en.pdf. [Accessed 20 October 2013]
11. Vickerman P, Foss AM, Pickles M, et al. To what extent is the HIV epidemic in southern India driven by commercial sex? A modelling analysis. AIDS 2010; 24:2563-2572.
12. Gomez G, Borquez A, Caceres C, et al. The cost and impact of scaling up preexposure prophylaxis for HIV prevention: a systematic review of cost-effectiveness modelling studies. PLoS Med 2013; 10: e1001401. A very useful and comprehensive review of previous modelling studies on the costeffectiveness of PrEP.
13. Punyacharoensin N, Edmunds W, D De Angelis, White G. Mathematical models for the study of HIV spread and control amongst men who have sex with men. Eur J Epidemiol 2011; 26:695-709.
14. Armbruster B, Roy S, Kapur A, Schneider J. Sex role segregation and mixing among men who have sex with men: implications for biomedical HIV prevention interventions. PLoS ONE 2013; 8:e70043.
15. Juusola J, Brandeau M, Owens D, Bendavid E. The cost-effectiveness of preexposure prophylaxis for HIV prevention in the united states in men who have sex with men. Ann Intern Med 2012; 156:541-550.
16. Sood N, Wagner Z, Jaycocks A, et al. Test-and-treat in Los Angeles: a mathematical model of the effects of test-and-treat for the population of men who have sex with men in Los Angeles County. Clin Infect Dis 2013; 56:1789-1796.
17. Phillips A, Cambiano V, Nakagawa F, et al. Increased HIV incidence in men who have sex with men despite high levels of ART-induced viral suppression: analysis of an extensively documented epidemic. PLoS One 2013; 8:e55312.
18. Vriend HJ, Lugner AK, Xiridou M, et al. Sexually transmitted infections screening at HIV treatment centers for MSM can be cost-effective. AIDS 2013; 27:2281-2290.
19. Xiridou M, Vriend H, Lugner A, et al. Modelling the impact of chlamydia screening on the transmission of HIV among men who have sex with men. BMC Infect Dis 2013; 13:436.
20. Gomez G, Borquez A, Caceres C, et al. The potential impact of pre-exposure prophylaxis for HIV prevention among men who have sex with men and transwomen in Lima, Peru: a mathematical modelling study. PLoS Med 2012; 9:e1001323. A very detailed modelling and cost-effectiveness analysis of PrEP among MSM and transwomen. Given the high cost of PrEP programmes, the authors looked at a maximum coverage of 20%, which is lower than in many other studies but potentially more realistic given the high cost. Emphasizes the prohibitive cost of PrEP programmes, preventing the use of PrEP as wide-scale prevention tool.
21. Grant RM, Lama JR, Anderson PL, et al., iPrEx team. Preexposure chemoprophylaxis for HIV prevention in men who have sex with men. N Engl J Med 2010; 363:2587-2599.
22. Hoare A, Gray RT, Wilson DP. Could implementation of Australia's National Gay Men's Syphilis Action Plan have an indirect effect on the HIV epidemicSex Health 2012; 9:144-151.
23. Sorensen S, Sansom S, Brooks J, et al. A mathematical model of comprehensive test-and-treat services and HIV incidence among men who have sex with men in the United States. PLoS One 2012; 7:e29098.
24. van Sighem A, Vidondo B, Glass T, et al. Resurgence of HIV infection among men who have sex with men in Switzerland: mathematical modelling study. PLoS One [Electronic Resource] 2012; 7:e44819.
25. De Vos AS, Kretzschmar ME. The efficiency of targeted intervention in limiting the spread of HIV and Hepatitis C Virus among injecting drug users. J Theor Biol 2013; 333:126-134.
26. Vickerman P, Martin NK, Hickman M. Could low dead-space syringes really reduce HIV transmission to low levels Int J Drug Policy 2013; 24:8-14. A very detailed study reviewing available data to estimate the reduced risk of HIV associated with LDDS. Using conservative impact estimates of LDSS interventions compared with previous ones, the study still highlights its use as a strategy to reduce HIV among PWID.
27. Zule WA, Cross HE, Stover J, Pretorius C. Are major reductions in new HIV infections possible with people who inject drugs The case for low dead-space syringes in highly affected countries. Int J Drug Policy 2013; 24:1-7.
28. The Futures Institute, GOALS model. http://www.futuresinstitute.org/ spectrum2.aspx. [Accessed 21 October 2013]
29. Kwon JA, Anderson J, Kerr C, et al. Estimating the cost-effectiveness of needle-syringe programs in Australia. AIDS 2012; 26:2201-2210.
30. Boily MC, Pickles M, Lowndes C, et al. Positive impact of a large-scale HIV prevention programme among female sex workers and clients in South India. AIDS 2013; 27:1449-1460.
31. Pickles M, Boily MC, Vickerman P, et al. Assessment of the population-level effectiveness of the Avahan HIV-prevention programme in South India: a preplanned, causal-pathway-based modelling analysis. Lancet Global Health 2013; 1:e289-e299. Real-life evaluation of the largest core group intervention in the world based for the first time on a mathematical study design. The findings demonstrate that targeting high-risk groups with a tailored range of services can help control HIV among key populations and the general population. This suggests that similar programmes could have a considerable impact on HIV epidemics in many parts of the world.
32. Decker MR, Wirtz AL, Pretorius C, et al. Estimating the impact of reducing violence against female sex workers on HIV epidemics in Kenya and Ukraine: a policy modeling exercise. Am J Reprod Immunol 2013; 69:2013; Suppl 1:122-132.
33. Kato M, Granich R, Bui D, et al. The potential impact of expanding antiretroviral therapy and combination prevention in Vietnam: towards elimination of HIV transmission. J Acquir Immune Defic Syndr 2013; 63:e142-e149.
34. Kessler J, Myers J, Nucifora K, et al. Averting HIV infections in New York City: a modeling approach estimating the future impact of additional behavioral and biomedical HIV prevention strategies. PLoS One 2013; 8:e73269.
35. Gilmour S, Li J, Shibuya K. Projecting HIV transmission in Japan. PLoS One 2012; 7:e43473.
36. Li J, Gilmour S, Zhang H, et al. The epidemiological impact and costeffectiveness of HIV testing, antiretroviral treatment and harm reduction programs. AIDS 2012; 26:2069-2078.
37. Sutton AJ, House T, Hope V, et al. Modelling HIV in the injecting drug user population and the male homosexual population in a developed country context. Epidemics 2012; 4:48-56.
38. Wammes JJ, Siregar AY, Hidayat T, et al. Cost-effectiveness of methadone maintenance therapy as HIV prevention in an Indonesian high-prevalence setting: a mathematical modeling study. Int J Drug Policy 2012; 23:358-364.
39. Zhang L, Gray RT, Wilson DP. Modelling the epidemiological impact of scaling up HIV testing and antiretroviral treatment in China. Sex Health 2012; 9:261-271.
40. Brown T, Peerapatanapokin W. The Asian Epidemic Model: a process model for exploring HIV policy and programme alternatives in Asia. Sex Transm Infect 2004; 80 (Suppl 1):i19-i24.
41. Castro Sanchez AY, Aerts M, Shkedy Z, et al. A mathematical model for HIV and hepatitis C co-infection and its assessment from a statistical perspective. Epidemics 2013; 5:56-66.
42. Vickerman P, Martin NK, Roy A, et al. Is the HCV-HIV co-infection prevalence amongst injecting drug users a marker for the level of sexual and injection related HIV transmission? Drug Alcohol Depend 2013; 132:172-181. A great review and modelling exercise using data from more than 43 countries to assess how HIV-HCV co-infection prevalence can be used to quantitatively determine the amount of sexual transmission among PWID.
43. De Vos AS, Van der Helm JJ, Prins M, Kretzschmar ME. Determinants of persistent spread of HIV in HCV-infected populations of injecting drug users. Epidemics 2012; 4:57-67.
44. Vickerman P, Martin NK, Hickman M. Understanding the trends in HIV and hepatitis C prevalence amongst injecting drug users in different settings: implications for intervention impact. Drug Alcohol Depend 2012; 123:122-131.
45. Richardson L, Grund T. Modeling the impact of supra-structural network nodes: the case of anonymous syringe sharing and HIV among people who inject drugs. Soc Sci Res 2012; 41:624-636.
46. Mills HL, White E, Colijn C, et al. HIV transmission from drug injectors to partners who do not inject, and beyond: modelling the potential for a generalized heterosexual epidemic in St. Petersburg, Russia. Drug Alcohol Depend 2013; 133:242-247.
47. - 47.Alam SJ, Zhang X, Romero-Severson E, et al. Detectable signals of episodic risk effects on acute HIV transmission: strategies for analyzing transmission systems using genetic data. Epidemics 2013; 5:44-55. An interesting and innovative study combining deterministic and IBMs to understand the role of transient risk behaviour on the fraction of infections due to the acute HIV stage and the size and length of acute transmission chains in order to help interpret genetic data.
48. Zhang X, Zhong L, Romero-Severson E, et al. Episodic HIV risk behavior can greatly amplify hiv prevalence and the fraction of transmissions from acute HIV infection. Stat Commun Infect Dis 1041; 4:1041-1062.
49. Stigum H, Falck W, Magnus P. The core group revisited: the effect of partner mixing and migration on the spread of gonorrhea, Chlamydia, and HIV. Math Biosci 1994; 120:1-23.
50. - 50.Cohen MS, Dye C, Fraser C, et al. HIV treatment as prevention: debate and commentary -will early infection compromise treatment-as-prevention strategies PLoS Med 2012; 9:e1001232. An interesting debate on HIV infectivity during acute infection, highlighting that current estimates are based on few epidemiological studies.
51. Cohen MS, Shaw GM, McMichael AJ, Haynes BF. Acute HIV-1 Infection. N Engl J Med 2011; 364:1943-1954.
52. Granich RM, Gilks CF, Dye C, et al. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet 2009; 373:48-57.
53. Dodd PJ, Garnett GP, Hallet TB. Examining the promise of HIV elimination by 'test and treat' in hyperendemic settings. AIDS 2010; 24:729-735.
54. Deering KN, Vickerman P, Pickles M, et al. Differences between seven measures of self-reported numbers of clients of female sex workers in southern India: implications for individual-and population-level analysis. AIDS Behav 2013; 17:649-661.
55. Bezemer D, de Wolf F, Boerlijst MC, et al. 27 years of the HIV epidemic amongst men having sex with men in The Netherlands: an in depth mathematical model-based analysis. Epidemics 2010; 2:66-79.
56. van Sighem A, Jansen I, Bezemer D, et al. Increasing sexual risk behaviour among Dutch men who have sex with men: mathematical models versus prospective cohort data. AIDS 2012; 26:1840-1843.
57. Cohen MS. Classical sexually transmitted diseases drive the spread of HIV-1: back to the future. J Infect Dis 2012; 206:1-2.
58. Grosskurth H, Gray R, Hayes R, et al. Control of sexually transmitted diseases for HIV-1 prevention: understanding the implications of the Mwanza and Rakai trials. Lancet 2000; 355:1981-1987.
59. Ward H, Ronn M. Contribution of sexually transmitted infections to the sexual transmission of HIV. Curr Opin HIV AIDS 2010; 5:305-310.
60. Wawer MJ, Gray RH, Sewankambo NK, et al. A randomized, community trial of intensive sexually transmitted disease control for AIDS prevention, Rakai, Uganda. AIDS 1998; 12:1211-1225.
61. Boily MC, Lowndes CM, Alary M. Complementary hypothesis concerning the community sexually transmitted disease mass treatment puzzle in Rakai, Uganda. AIDS 2000; 14:2583-2592.
62. Ng BE, Butler LM, Horvath T, Rutherford G. Population-based biomedical sexually transmitted infection control interventions for reducing HIV infection. Cochrane Database Syst Rev 2011; 16:CD001220.
63. Padian NS, McCoy SI, Balkus JE, Wasserheit JN. Weighing the gold in the gold standard: challenges in HIV prevention research. AIDS 2010; 24:621-635.
64. White RG, Orroth KK, Korenromp EL, et al. Can population differences explain the contrasting results of the Mwanza, Rakai, and Masaka HIV/sexually transmitted disease intervention trials A modeling study. J Acquir Immune Defic Syndr 2004; 37:1500-1513.
65. Mesquita F, Winarso I, Atmosukarto II, et al. Public health the leading force of the Indonesian response to the HIV/AIDS crisis among people who inject drugs. Harm Reduct J 2007; 4:9.
66. Chatterjee A, Sharma M. Moving from a project to programmatic response: scaling up harm reduction in Asia. Int J Drug Policy 2010; 21:134-136.
67. Mishra S, Steen R, Gerbase A, et al. Impact of high-risk sex and focused interventions in heterosexual HIV epidemics: a systematic review of mathematical models. PLoS One [Electronic Resource] 2012; 7:e50691.
68. Boily MC, Lowndes C, Alary M. The impact of HIV epidemic phases on the effectiveness of core group interventions: insights from mathematical models. Sex Transm Infect 2002; 78 (Suppl 1):i78-i90.
69. Mishra S, Pickles M, Blanchard JF, et al. Distinguishing sources of HIV transmission from the distribution of newly acquired HIV infections: why is it important for HIV prevention planning Sex Transm Infect 2013. doi: 10.1136/sextrans-2013-051250.
70. Mishra S, Pickles M, Blanchard JF, et al. Validation of the modes of transmission model as a tool to prioritize HIV prevention targets: a comparative modeling analysis. PLoS One, in preparation.
71. Williams JR, Alary M, Lowndes C, et al., Positive impact of increases in condom use among female sex workers and clients in a medium HIV prevalence epidemic: modelling results from Project SIDA1/2/3 in Cotonou, Benin, PLoS One (submitted).
72. Joint United Nations Programme on HIV/AIDS (UNAIDS), Modelling the expected short-term distribution of incidence of HIV infections by exposure group, 2007: Geneva. http://www.unaids.org/en/dataanalysis/tools/ incidencebymodesoftransmission. [Accessed 28 September 2012]
73. Rothman K, Greenland S. Modern epidemiology. 2nd Edition Riverwoods, IL, USA: Lippincott Williams & Wilkins; 1998.
74. Mishra S, Fisman DN, Boily MC. The ABC of terms used in mathematical models of infectious diseases. J Epidemiol Commun Health 2010; 65:87-94.
75. Morris M, Kurth AE, Hamilton DT, et al. Concurrent partnerships and HIV prevalence disparities by race: linking science and public health practice. Am J Public Health 2009; 99:1023-1031.
76. Eaton JW, Hallett TB, Garnett GP. Concurrent sexual partnerships and primary HIV infection: a critical interaction. AIDS Behav 2011; 15:687-692.
77. Korenromp EL, Van Vliet C, Grosskurth H, et al. Model-based evaluation of single-round mass treatment of sexually transmitted diseases for HIV control in a rural African population. AIDS 2000; 14:573-593.
78. Van de Velde N, Boily MC, Drolet M, et al. Population-level impact of the bivalent, quadrivalent, and nonavalent human papillomavirus vaccines: a model-based analysis. J Natl Cancer Inst 2012; 104:1712-1723.
79. Althaus CL, Turner KM, Schmid BV, et al. Transmission of Chlamydia trachomatis through sexual partnerships: a comparison between three individual-based models and empirical data. J R Soc Interface 2012; 9:136-146.
80. Schackman BR, Eggman AA. Cost-effectiveness of preexposure prophylaxis for HIV: a review. Curr Opin HIV AIDS 2012; 7:587-592.
81. Baggaley RF, Powers KA, Boily MC. What do mathematical models tell us about the emergence and spread of drug-resistant HIV? Curr Opin HIV AIDS 2011; 6:131-140.
82. Wilson DP, Zhang L. Characteristics of HIV epidemics driven by men who have sex with men and people who inject drugs. Curr Opin HIV AIDS 2011; 6:94-101.
83. - 83.van de Vijver DA, Nichols BE, Abbas UL, et al. Preexposure prophylaxis will have a limited impact on HIV-1 drug resistance in sub-Saharan Africa: a comparison of mathematical models. AIDS 2013. [Epub ahead of print] An interesting side-by-side model comparison suggesting that PrEP is unlikely to be the major cause of drug resistance and should not be an obstacle for using it.
84. Dimitrov D, Boily MC, Brown ER, Hallett T. Analytic review of modeling studies of ARV based PrEP interventions reveals strong influence of drug-resistance assumptions on the population-level effectiveness. PLoS One 2013; 8:e80927. Detailed review in which the same model is used to evaluate the influence of the difference resistance assumptions used in previous published modelling studies on the number of HIV infections averted and on drug resistance. Allows a more direct and objective comparison of each assumption.
85. Boily MC, Lowndes CM, Vickerman P, et al. Evaluating large-scale HIV prevention interventions: study design for an integrated mathematical modelling approach. Sex Transm Infect 2007; 83:582-589.
86. Boily MC, Masse B, Alsallaq R, et al. HIV treatment as prevention: considerations in the design, conduct, and analysis of cluster randomized controlled trials of combination HIV prevention. PLoS Med 2012; 9: e1001250.
87. Mishra S, Sgaier SK, Thompson LH, et al. HIV epidemic appraisals for assisting in the design of effective prevention programmes: shifting the paradigm back to basics. PLoS One 2012; 7:7.