, Volume 37, Issue 10, pp 1219–1239 | Cite as

Structural Design and Data Requirements for Simulation Modelling in HIV/AIDS: A Narrative Review

  • Xiao Zang
  • Emanuel Krebs
  • Linwei Wang
  • Brandon D. L. Marshall
  • Reuben Granich
  • Bruce R. Schackman
  • Julio S. G. Montaner
  • Bohdan NosykEmail author
  • the Localized HIV modeling study group
Review Article


Born out of a necessity for fiscal sustainability, simulation modeling is playing an increasingly prominent role in setting priorities for combination implementation strategies for HIV treatment and prevention globally. The design of a model and the data inputted into it are central factors in ensuring credible inferences. We executed a narrative review of a set of dynamic HIV transmission models to comprehensively synthesize and compare the structural design and the quality of evidence used to support each model. We included 19 models representing both generalized and concentrated epidemics, classified as compartmental, agent-based, individual-based microsimulation or hybrid in our review. We focused on four structural components (population construction; model entry, exit and HIV care engagement; HIV disease progression; and the force of HIV infection), and two analytical components (model calibration/validation; and health economic evaluation, including uncertainty analysis). While the models we reviewed focused on a variety of individual interventions and their combinations, their structural designs were relatively homogenous across three of the four focal components, with key structural elements influenced by model type and epidemiological context. In contrast, model entry, exit and HIV care engagement tended to differ most across models, with some health system interactions—particularly HIV testing—not modeled explicitly in many contexts. The quality of data used in the models and the transparency with which the data was presented differed substantially across model components. Representative and high-quality data on health service delivery were most commonly not accessed or were unavailable. The structure of an HIV model should ideally fit its epidemiological context and be able to capture all efficacious treatment and prevention services relevant to a robust combination implementation strategy. Developing standardized guidelines on evidence syntheses for health economic evaluation would improve transparency and help prioritize data collection to reduce decision uncertainty.



We acknowledge Lindsay Pearce for assistance with manuscript preparation. The Localized HIV Modeling Study Group comprises Czarina N. Behrends, M.P.H, Ph.D., Department of Healthcare Policy and Research, Weill Cornell Medical College; Carlos Del Rio, MD, Hubert Department of Global Health, Emory Center for AIDS Research, Rollins School of Public Health of Emory University; Julia Dombrowski, MD, Department of Epidemiology, University of Washington; Daniel J. Feaster, Ph.D., Center for Family Studies, Department of Epidemiology and Public Health, Leonard M. Miller School of Medicine, University of Miami; Kelly Gebo, Ph.D., Bloomberg School of Public Health, Johns Hopkins University; Matthew Golden, MD, Division of Allergy and Infectious Diseases, University of Washington; Reuben Granich, MD, Independent Public Health Consultant, Washington, DC; Thomas Kerr, Ph.D., BC Centre for Excellence in HIV/AIDS; Faculty of Medicine, University of British Columbia; Gregory Kirk, Ph.D., Bloomberg School of Public Health, Johns Hopkins University; Brandon DL Marshall, Ph.D., Department of Epidemiology, Brown University School of Public Health, Rhode Island, United States; Shruti H. Mehta, Ph.D., Bloomberg School of Public Health, Johns Hopkins University; Lisa Metsch, Ph.D., Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University; Julio S.G. Montaner, MD, BC Centre for Excellence in HIV/AIDS; Faculty of Medicine, University of British Columbia; Bohdan Nosyk, Ph.D., BC Centre for Excellence in HIV/AIDS; Faculty of Health Sciences, Simon Fraser University; Bruce R. Schackman, Ph.D., Department of Healthcare Policy and Research, Weill Cornell Medical College; Steven Shoptaw, Ph.D., Centre for HIV Identification, Prevention and Treatment Services, School of Medicine, University of California Los Angeles; William Small, Ph.D., BC Centre for Excellence in HIV/AIDS; Faculty of Health Sciences, Simon Fraser University; and Steffanie Strathdee, Ph.D., School of Medicine, University of California.

Author Contributions

XZ, BN conceptualized and designed the study; XZ and EK developed the search strategy; XZ, EK and LW conducted the literature search, information extraction and developed tables/figures of the results; XZ wrote the first draft of the manuscript; EK, BN and LW contributed to the manuscript development; BDLM, RG, BRS and JSGM aided in the interpretation of results, and provided critical revisions to the article; all authors approved the final draft.

Compliance with Ethical Standards


This study was funded by the BC Ministry of Health-funded ‘Seek and treat for optimal prevention of HIV & AIDS’ pilot project and a Grant from the National Institutes of Health/National Institute on Drug Abuse (R01-DA-041747). The funders had no direct role in the conduct of the analysis or the decision to submit the manuscript for publication.

Conflict of interest

Xiao Zang, Emanuel Krebs, Linwei Wang, Brandon D.L. Marshall, Reuben Granich, Bruce R. Schackman, Julio S.G. Montaner, and Bohdan Nosyk have no conflicts of interest to report.

Supplementary material

40273_2019_817_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1155 kb)


  1. 1.
    Kates J, Wexler A, Lief E, UNAIDS. Donor government funding for HIV in low- and middle-income countries in 2016. 2017. Accessed 20 Aug 2017.
  2. 2.
  3. 3.
    Chang LW, Serwadda D, Quinn TC, Wawer MJ, Gray RH, Reynolds SJ. Combination implementation for HIV prevention: moving from clinical trial evidence to population-level effects. Lancet Infect Dis. 2013;13(1):65–76.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Jones A, Cremin I, Abdullah F, Idoko J, Cherutich P, Kilonzo N, et al. Transformation of HIV from pandemic to low-endemic levels: a public health approach to combination prevention. Lancet. 2014;384(9939):272–9.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Garnett GP, Cousens S, Hallett TB, Steketee R, Walker N. Mathematical models in the evaluation of health programmes. Lancet. 2011;378(9790):515–25.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Garnett GP. An introduction to mathematical models in sexually transmitted disease epidemiology. Sex Transm Infect. 2002;78(1):7–12.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Briggs A, Claxton K, Sculpher M. Decision modelling for health economic evaluation. 1st ed. London: Oxford University Press; 2006.Google Scholar
  8. 8.
    Jacobsen MM, Walensky RP. Modeling and cost-effectiveness in HIV prevention. Curr HIV AIDS Rep. 2016;13(1):64–75.CrossRefGoogle Scholar
  9. 9.
    Weinstein MC, O’Brien B, Hornberger J, Jackson J, Johannesson M, McCabe C, et al. Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices-Modeling Studies. Value Health. 2003;6(1):9–17.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Caro JJ, Briggs AH, Siebert U, Kuntz KM. Modeling good research practices—overview. A report of the ISPOR-SMDM Modeling Good Research Practices Task Force—1. Med Decis Mak Int J Soc Med Decis Mak. 2012;32(5):667–77.CrossRefGoogle Scholar
  11. 11.
    Roberts M, Russell LB, Paltiel AD, Chambers M, McEwan P, Krahn M. Conceptualizing a model: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force—2. Value Health. 2012;15(6):804–11.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Coyle D, Lee KM. Evidence-based economic evaluation: how the use of different data sources can impact results. In: Donaldson C, Mugford M, Vale L, editors. Evidence-based health economics: from effectiveness to efficiency in systematic review. London: BMJ Books; 2002.Google Scholar
  13. 13.
    Pinkerton SD. HIV transmission rate modeling: a primer, review, and extension. AIDS Behav. 2012;16(4):791–6.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Cassels S, Clark SJ, Morris M. Mathematical models for HIV transmission dynamics: tools for social and behavioral science research. J Acquir Immune Defic Syndr. 2008;01(47 Suppl 1):S34–9.CrossRefGoogle Scholar
  15. 15.
    Granich R, Crowley S, Vitoria M, Smyth C, Kahn JG, Bennett R, et al. Highly active antiretroviral treatment as prevention of HIV transmission: review of scientific evidence and update. Curr Opin HIV AIDS. 2010;5(4):298–304.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Schackman BR, Eggman AA. Cost-effectiveness of pre-exposure prophylaxis for HIV: a review. Curr Opin HIV AIDS. 2012;7(6):587–92.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Gomez GB, Borquez A, Case KK, Wheelock A, Vassall A, Hankins C. The cost and impact of scaling up pre-exposure prophylaxis for HIV prevention: a systematic review of cost-effectiveness modelling studies. PLoS Med. 2013;10(3):e1001401.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Galarraga O, Colchero MA, Wamai RG, Bertozzi SM. HIV prevention cost-effectiveness: a systematic review. BMC Public Health. 2009;9(Suppl 1):S5.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    van de Vijver DA, Nichols BE, Abbas UL, Boucher CA, Cambiano V, Eaton JW, 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;27(18):2943–51.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Eaton JW, Menzies NA, Stover J, Cambiano V, Chindelevitch L, Cori A, et al. Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models. Lancet Glob Health. 2014;2(1):e23–34.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Eaton J, Johnson L, Salomon J, Bärnighausen T, Bendavid E, Bershteyn A, et al. HIV treatment as prevention: systematic comparison of mathematical models of the potential impact of antiretroviral therapy on HIV incidence in South Africa. PLoS Med. 2012;9(7):e1001245.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Anderson SJ, Cherutich P, Kilonzo N, Cremin I, Fecht D, Kimanga D, et al. Maximising the effect of combination HIV prevention through prioritisation of the people and places in greatest need: a modelling study. Lancet. 2014;384(9939):249–56.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Panagiotoglou D, Olding M, Enns B, Feaster DJ, Del Rio C, Metsch LR, et al. Building the case for localized approaches to HIV: structural conditions and health system capacity to address the HIV/AIDS epidemic in six US cities. AIDS Behav. 2018;22(9):3071–82.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Hankins CA, de Zalduondo BO. Combination prevention: a deeper understanding of effective HIV prevention. Aids. 2010;24(Suppl 4):S70–80.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Schwartlander B, Stover J, Hallett T, Atun R, Avila C, Gouws E, et al. Towards an improved investment approach for an effective response to HIV/AIDS. Lancet. 2011;377(9782):2031–41.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Cook DJ, Mulrow CD, Haynes RB. Systematic reviews: synthesis of best evidence for clinical decisions. Ann Intern Med. 1997;126(5):376–80.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Shepherd K, Hubbard D, Fenton N, Claxton K, Luedeling E, de Leeuw J. Policy: development goals should enable decision-making. Nature. 2015;523(7559):152–4.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Greenhalgh T, Peacock R. Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. BMJ. 2005;331(7524):1064–5.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Nosyk B, Min JE, Krebs E, Zang X, Compton M, Gustafson R, et al. The cost-effectiveness of human immunodeficiency virus testing and treatment engagement initiatives in British Columbia, Canada: 2011–2013. Clin Infect Dis Off Publ Infect Dis Soc Am. 2018;66(5):765–77.CrossRefGoogle Scholar
  30. 30.
    Dias S, Welton NJ, Sutton AJ, Ades AE. Evidence synthesis for decision making 5: the baseline natural history model. Med Decis Mak. 2013;33(5):657–70.CrossRefGoogle Scholar
  31. 31.
    Cooper NJ, Sutton AJ, Ades AE, Paisley S, Jones DR. Use of evidence in economic decision models: practical issues and methodological challenges. Health Econ. 2007;16(12):1277–86.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Cooper N, Coyle D, Abrams K, Mugford M, Sutton A. Use of evidence in decision models: an appraisal of health technology assessments in the UK since 1997. J Health Serv Res Policy. 2005;10(4):245–50.CrossRefPubMedGoogle Scholar
  33. 33.
    Zechmeister-Koss I, Schnell-Inderst P, Zauner G. Appropriate evidence sources for populating decision analytic models within health technology assessment (HTA): a systematic review of HTA manuals and health economic guidelines. Med Decis Mak. 2014;34(3):288–99.CrossRefGoogle Scholar
  34. 34.
    Paisley S. Classification of evidence in decision-analytic models of cost-effectiveness: a content analysis of published reports. Int J Technol Assess Health Care. 2010;26(4):458–62.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Paisley S. Identification of evidence for key parameters in decision-analytic models of cost effectiveness: a description of sources and a recommended minimum search requirement. Pharmacoeconomics. 2016;34(6):597–608.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Coyle D, Lee KM. Evidence-based economic evaluation: how the use of different data sources can impact results. In: Donaldson C, Mugford M, Vale L, editors. Evidence-based health economics: from effectiveness to efficiency in systematic review. London: BMJ; 2002. p. 55–66.Google Scholar
  37. 37.
    Bershteyn A, Klein DJ, Eckhoff PA. Age-dependent partnering and the HIV transmission chain: a microsimulation analysis. J R Soc Interface. 2013;10(88):20130613.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Smith JA, Sharma M, Levin C, Baeten JM, van Rooyen H, Celum C, et al. Cost-effectiveness of community-based strategies to strengthen the continuum of HIV care in rural South Africa: a health economic modelling analysis. Lancet HIV. 2015;2(4):e159–68.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Hontelez JA, Nagelkerke N, Barnighausen T, Bakker R, Tanser F, Newell ML, et al. The potential impact of RV144-like vaccines in rural South Africa: a study using the STDSIM microsimulation model. Vaccine. 2011;29(36):6100–6.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Bendavid E, Brandeau ML, Wood R, Owens DK. Comparative effectiveness of HIV testing and treatment in highly endemic regions. Arch Intern Med. 2010;170(15):1347–54.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Walensky RP, Ross EL, Kumarasamy N, Wood R, Noubary F, Paltiel AD, et al. Cost-effectiveness of HIV treatment as prevention in serodiscordant couples. N Engl J Med. 2013;369(18):1715–25.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Phillips AN, Cambiano V, Miners A, Revill P, Pillay D, Lundgren JD, et al. Effectiveness and cost-effectiveness of potential responses to future high levels of transmitted HIV drug resistance in antiretroviral drug-naive populations beginning treatment: modelling study and economic analysis. Lancet HIV. 2014;1(2):e85–93.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Bärnighausen T, Bloom DE, Humair S. Economics of antiretroviral treatment vs. circumcision for HIV prevention. Proc Natl Acad Sci USA. 2012;109(52):21271–6.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Eaton JW, Hallett TB. Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence. PNAS. 2014;111(45):16202–7.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Granich RM, Gilks CF, Dye C, De Cock KM, Williams BG. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet. 2009;373(9657):48–57.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Nichols BE, Boucher CA, van Dijk JH, Thuma PE, Nouwen JL, Baltussen R, et al. Cost-effectiveness of pre-exposure prophylaxis (PrEP) in preventing HIV-1 infections in rural Zambia: a modeling study. PLoS One. 2013;8(3):e59549.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Cori A, Ayles H, Beyers N, Schaap A, Floyd S, Sabapathy K, et al. HPTN 071 (PopART): a cluster-randomized trial of the population impact of an HIV combination prevention intervention including universal testing and treatment: mathematical model. PLoS One. 2014;9(1):e84511.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Johnson LF, Hallett TB, Rehle TM, Dorrington RE. The effect of changes in condom usage and antiretroviral treatment coverage on human immunodeficiency virus incidence in South Africa: a model-based analysis. J R Soc Interface. 2012;9(72):1544–54.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Birger RB, Hallett TB, Sinha A, Grenfell BT, Hodder SL. Modeling the impact of interventions along the HIV continuum of care in Newark, New Jersey. Clin Infect Dis. 2014;58(2):274–84.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Stover J, Bollinger L, Avila C. Estimating the impact and cost of the WHO 2010 recommendations for antiretroviral therapy. AIDS Res Treat. 2011;2011:738271.PubMedPubMedCentralGoogle Scholar
  51. 51.
    Mishra S, Mountain E, Pickles M, Vickerman P, Shastri S, Gilks C, et al. Exploring the population-level impact of antiretroviral treatment: the influence of baseline intervention context. Aids. 2014;28(Suppl 1):S61–72.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Long EF, Brandeau ML, Owens DK. The cost-effectiveness and population outcomes of expanded HIV screening and antiretroviral treatment in the United States. Ann Intern Med. 2010;153(12):778–89.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Zhang L, Pham QD, Do MH, Kerr C, Wilson DP. Returns on investments of HIV prevention in Vietnam. 2013. Accessed 11 July 2017.
  54. 54.
    Lasry A, Sansom SL, Hicks KA, Uzunangelov V. A model for allocating CDC’s HIV prevention resources in the United States. Health Care Manag Sci. 2011;14(1):115–24.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2015.Google Scholar
  56. 56.
    Anderson RM, May RM, Ng TW, Rowley JT. Age-dependent choice of sexual partners and the transmission dynamics of HIV in Sub-Saharan Africa. Philos Trans R Soc Lond B Biol Sci. 1992;336(1277):135–55.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Beauclair R, Helleringer S, Hens N, Delva W. Age differences between sexual partners, behavioural and demographic correlates, and HIV infection on Likoma Island, Malawi. Sci Rep. 2016;02(6):36121.CrossRefGoogle Scholar
  58. 58.
    d’Albis H, Augeraud-Veron E, Djemai E, Ducrot A. The dispersion of age differences between partners and the asymptotic dynamics of the HIV epidemic. J Biol Dyn. 2012;6:695–717.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Marshall BD, Paczkowski MM, Seemann L, Tempalski B, Pouget ER, Galea S, et al. A complex systems approach to evaluate HIV prevention in metropolitan areas: preliminary implications for combination intervention strategies. PLoS One. 2012;7(9):e44833.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Lodi S, Phillips A, Touloumi G, Geskus R, Meyer L, Thiebaut R, et al. Time from human immunodeficiency virus seroconversion to reaching CD4 + cell count thresholds < 200, < 350, and < 500 Cells/mm(3): assessment of need following changes in treatment guidelines. Clin Infect Dis. 2011;53(8):817–25.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Hollingsworth TD, Anderson RM, Fraser C. HIV-1 transmission, by stage of infection. J Infect Dis. 2008;198(5):687–93.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, et al. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365(6):493–505.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Weller S, Davis K. Condom effectiveness in reducing heterosexual HIV transmission. Cochrane Database Syst Rev. 2002;1:CD003255.Google Scholar
  64. 64.
    Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD, et al. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–6. Value Health J Int Soc Pharmacoecon Outcomes Res. 2012;15(6):835–42.CrossRefGoogle Scholar
  65. 65.
    Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB, et al. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force—7. Value Health J Int Soc Pharmacoecon Outcomes Res. 2012;15(6):843–50.CrossRefGoogle Scholar
  66. 66.
    Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Pharmacoeconomics. 2013;31(5):361–7.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Long EF, Mandalia R, Mandalia S, Alistar SS, Beck EJ, Brandeau ML. Expanded HIV testing in low-prevalence, high-income countries: a cost-effectiveness analysis for the United Kingdom. PLoS One. 2014;9(4):e95735.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Tabana H, Nkonki L, Hongoro C, Doherty T, Ekstrom AM, Naik R, et al. A cost-effectiveness analysis of a home-based HIV counselling and testing intervention versus the standard (facility based) HIV testing strategy in rural South Africa. PLoS One. 2015;10(8):e0135048.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Hutchinson AB, Farnham PG, Sansom SL, Yaylali E, Mermin JH. Cost-effectiveness of Frequent HIV testing of high-risk populations in the United States. J Acquir Immune Defic Syndr. 2016;71(3):323–30.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Higgins DL, Galavotti C, O’Reilly KR, Schnell DJ, Moore M, Rugg DL, et al. Evidence for the effects of HIV antibody counseling and testing on risk behaviors. JAMA. 1991;266(17):2419–29.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Cleary PD, Van Devanter N, Rogers TF, Singer E, Shipton-Levy R, Steilen M, et al. Behavior changes after notification of HIV infection. Am J Public Health. 1991;81(12):1586–90.CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Weinhardt LS, Carey MP, Johnson BT, Bickham NL. Effects of HIV counseling and testing on sexual risk behavior: a meta-analytic review of published research, 1985–1997. Am J Public Health. 1999;89(9):1397–405.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Wynberg E, Cooke G, Shroufi A, Reid SD, Ford N. Impact of point-of-care CD4 testing on linkage to HIV care: a systematic review. J Int AIDS Soc. 2014;17:18809.CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Cambiano V, Rodger AJ, Phillips AN. ‘Test-and-treat’: the end of the HIV epidemic? Curr Opin Infect Dis. 2011;24(1):19–26.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Kurth AE, Celum C, Baeten JM, Vermund SH, Wasserheit JN. Combination HIV prevention: significance, challenges, and opportunities. Curr HIV AIDS Rep. 2011;8(1):62–72.CrossRefGoogle Scholar
  76. 76.
    Pitman R, Fisman D, Zaric GS, Postma M, Kretzschmar M, Edmunds J, et al. Dynamic transmission modeling: a report of the ISPOR-SMDM modeling good research practices task force-5. Value Health. 2012;15:828–34.CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ. 1999;8(3):341–64.CrossRefGoogle Scholar
  78. 78.
    Basu S, Andrews J. Complexity in mathematical models of public health policies: a guide for consumers of models. PLoS Med. 2013;10(10):e1001540.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    The HIV Modelling Consortium. Accessed 11 July 2017.
  80. 80.
    WHO. Definition of key terms. 2013. Accessed 11 July 2017.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xiao Zang
    • 1
    • 2
  • Emanuel Krebs
    • 1
  • Linwei Wang
    • 1
  • Brandon D. L. Marshall
    • 3
  • Reuben Granich
    • 4
  • Bruce R. Schackman
    • 5
  • Julio S. G. Montaner
    • 1
    • 6
  • Bohdan Nosyk
    • 1
    • 2
    Email author
  • the Localized HIV modeling study group
  1. 1.British Columbia Centre for Excellence in HIV/AIDS, St. Paul’s HospitalVancouverCanada
  2. 2.Faculty of Health SciencesSimon Fraser UniversityBurnabyCanada
  3. 3.School of Public HealthBrown UniversityProvidenceUSA
  4. 4.Independent Public Health ConsultantWashingtonUSA
  5. 5.Weill Cornell Medical CollegeNew YorkUSA
  6. 6.Faculty of MedicineUniversity of British ColumbiaVancouverCanada

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