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Mô hình Dịch tễ Học của Các Can Thiệp Ngăn Ngừa HIV: Một Bài Tổng Quan Bằng Hệ Thống và Tổng Hợp Hướng Tiếp Cận Các Mô Hình Compartments
Tóm tắt
Dịch HIV vẫn là một vấn đề sức khỏe cộng đồng lớn. Các chiến lược ngăn ngừa HIV là yếu tố quan trọng trong việc kiểm soát sự lây truyền, với nhiều can thiệp mới tiếp tục được phát triển. Các mô hình toán học rất quan trọng để hiểu tác động tiềm tàng của những can thiệp này và hỗ trợ ra quyết định chính sách. Tổng quan hệ thống này nhằm trả lời câu hỏi sau: khi một can thiệp ngăn ngừa HIV mới được xem xét hoặc thiết kế, thông tin gì về nó là cần thiết để đưa vào một mô hình compartment để cung cấp những hiểu biết hữu ích cho các nhà hoạch định chính sách? Mục tiêu chính của tổng quan này là đánh giá sự phù hợp của các mô hình ngăn ngừa HIV hiện tại trong việc hỗ trợ phát triển chính sách. Các bài báo được công bố trong EMBASE, Medline, Econlit và Global Health đã được sàng lọc. Các nghiên cứu được đưa vào xác định bằng các hoán vị của (i) HIV, (ii) dự phòng trước phơi nhiễm (PrEP), cắt bao quy đầu (cả cắt bao quy đầu nam tình nguyện [VMMC] và cắt bao quy đầu cho trẻ sơ sinh [EIMC]), và tiêm chủng, và (iii) mô hình hóa. Việc trích xuất dữ liệu tập trung vào thiết kế nghiên cứu, cấu trúc mô hình và việc đưa can thiệp vào các mô hình. Chất lượng bài báo được đánh giá bằng tiêu chí TRACE (Tài liệu mô hình sinh thái TRong suốt và Toàn diện) cho các mô hình toán học. Trong số 837 bài báo được sàng lọc, 48 bài báo đã được đưa vào tổng quan, với 32 mô hình toán học độc đáo được xác định. Phần lớn các nghiên cứu bao gồm PrEP (83%), trong khi ít nghiên cứu mô hình hóa cắt bao quy đầu (54%), và chỉ một vài nghiên cứu tập trung vào tiêm chủng (10%). Đánh giá dữ liệu, xác minh thực hiện và xác nhận đầu ra mô hình được xác định là những lĩnh vực có chất lượng mô hình kém. Các tham số thường được đưa vào các mô hình toán học là sự tiếp nhận và hiệu quả của can thiệp, với các tham số chung bổ sung riêng cho từng can thiệp được xác định. Chúng tôi đã xác định các khoảng trống chính trong mô hình; quan trọng nhất, các mô hình không tích hợp đủ các can thiệp đồng thời. Thêm vào đó, các nhóm dân số phụ thường được đại diện kém - với các mô hình trong tương lai cần cải thiện việc đưa vào phân biệt chủng tộc và phân loại nhóm rủi ro tình dục - và nhiều mô hình chứa dữ liệu không thích hợp trong việc tham số hóa, điều này sẽ ảnh hưởng đến độ chính xác đầu ra. Tổng quan này đã xác định những khoảng trống trong các mô hình compartment cho đến nay và gợi ý các lĩnh vực cải tiến cho các mô hình tập trung vào các can thiệp ngăn ngừa mới. Việc giải quyết những khoảng trống như vậy trong các mô hình tương lai sẽ đảm bảo sự vững chắc và minh bạch hơn, và cho phép đánh giá chính xác hơn về tác động mà các can thiệp mới có thể có, từ đó cung cấp hướng dẫn có ý nghĩa hơn cho các nhà hoạch định chính sách.
Từ khóa
#HIV #mô hình toán học #can thiệp ngăn ngừa #dự phòng trước phơi nhiễm #cắt bao quy đầu #tiêm chủngTài liệu tham khảo
HIV.gov. The Global HIV/AIDS Epidemic [Internet]. 2021 [cited 2022 Feb 9]. Available from: https://www.hiv.gov/hiv-basics/overview/data-and-trends/global-statistics.
World health organisation. HIV/AIDS [Internet]. [cited 2022 Dec 2]. Available from: https://www.who.int/health-topics/hiv-aids#tab=tab_1
UNAIDS. 90-90-90: An ambitious treatment target to help end the AIDS epidemic [Internet]. 2014. Available from: https://www.unaids.org/sites/default/files/media_asset/90-90-90_en.pdf
UNAIDS. 2025 AIDS targets: Target-Setting, Impact and Resource Needs for the Global AIDS Response. Technical consultation on primary prevention [Internet]. 2019 [cited 2022 Feb 9]. Available from: https://www.unaids.org/sites/default/files/2025targets-PreventionMeeting_March2019.pdf
Mugo NR, Ngure K, Kiragu M, Irungu E, Kilonzo N. The preexposure prophylaxis revolution; from clinical trials to programmatic implementation. Curr Opin HIV AIDS. 2016;11:80–6.
World health organisation. HIV/AIDs: Key facts [Internet]. 2021 [cited 2022 Feb 9]. Available from: https://www.who.int/news-room/fact-sheets/detail/hiv-aids.
World health Organization. WHO recommends the dapivirine vaginal ring as a new choice for HIV prevention for women at substantial risk of HIV infection [Internet]. 2021 [cited 2021 Jul 12]. Available from: https://www.who.int/news/item/26-01-2021-who-recommends-the-dapivirine-vaginal-ring-as-a-new-choice-for-hiv-prevention-for-women-at-substantial-risk-of-hiv-infection
Viiv healthcare. FDA grants Priority Review to ViiV Healthcare’s New Drug Application for cabotegravir long-acting for prevention of HIV [Internet]. 2021 [cited 2021 Oct 7]. Available from: https://viivhealthcare.com/en-gb/media/press-releases/2021/september/viiv-healthcare-announces-fda-priority-review/
Kim J, Vasan S, Kim JH, Ake JA. Current approaches to HIV vaccine development: a narrative review. J Int AIDS Soc. 2021;24(7):25793.
Leggat DJ, Cohen KW, Willis JR, Fulp WJ, deCamp AC, Kalyuzhniy O, et al. Vaccination induces HIV broadly neutralizing antibody precursors in humans. Science. 2022;378:6502.
Burton DR. Amping up HIV antibodies. Science (80- ) [Internet]. American Association for the Advancement of Science; 2021;372:1397–8. Available from: https://doi.org/10.1126/science.abf5376
Antibodies for HIV prevention [Internet]. Available from: https://www.iavi.org/phocadownload/iavi_fact_sheet_antibodies_for_hiv_prevention.pdf
Miner MD, Corey L, Montefiori D. Broadly neutralizing monoclonal antibodies for HIV prevention. J Int AIDS Soc. 2021;24(7): e25829.
Brown JL, Sales JM, DiClemente RJ. Combination HIV prevention interventions: the potential of integrated behavioral and biomedical approaches. Curr HIV/AIDS Rep. 2014;11:363–75.
Johnson LF, White PJ. A review of mathematical models of HIV/AIDS interventions and their implications for policy. Sex Transm Infect. 2011;87:629–34.
Garnett GP, Cousens S, Hallett TB, Steketee R, Walker N. Mathematical models in the evaluation of health programmes. Lancet (London, England). 2011;378:515–25.
Liang P, Zu J, Zhuang G. A literature review of mathematical models of hepatitis B virus transmission applied to immunization strategies from 1994 to 2015. J Epidemiol. 2018;28:221–9.
Ma Z, Li J. Dynamical Modeling and Analysis of Epidemics [Internet]. World Scientific Publishing Co. Pte. Ltd; 2009. Available from: https://agus34drajat.files.wordpress.com/2010/10/dynamical-modeling-and-analysis-of-epidemics.pdf
Brisson M, Edmunds WJ. Economic evaluation of vaccination programs: the impact of herd immunity. Med Decis Making. 2003;23:76–82.
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.
Department of Health South Africa, South African National AIDS Council. South African HIV and TB Investment Case - Summary Report Phase 1 [Internet]. Available from: http://www.heroza.org/wp-content/uploads/2016/03/SA-HIV_TB-Investment-Case-Full-Report-Low-Res.pdf
The Global Fund. Step up the fight: Investment case [Internet]. 2019. Available from: https://www.theglobalfund.org/media/8279/publication_sixthreplenishmentinvestmentcase_report_en.pdf
Nepomuceno E, Resende D, Lacerda M. A Survey of the individual-based model applied in biomedical and epidemiology. 2019.
Donkin E, Dennis P, Ustalakov A, Warren J, Clare A. Replicating complex agent based models, a formidable task. Environ Model Softw [Internet]. 2017;92:142–51. Available from: https://www.sciencedirect.com/science/article/pii/S1364815216310088
Case KK, Gomez GB, Hallett TB. The impact, cost and cost-effectiveness of oral pre-exposure prophylaxis in sub-Saharan Africa: a scoping review of modelling contributions and way forward. J Int AIDS Soc. 2019;22: e25390.
Bernard CL, Brandeau ML. Structural sensitivity in HIV modeling: a case study of vaccination. Infect Dis Model. 2017;2:399–411.
Baggaley RF, Fraser C. Modelling sexual transmission of HIV: testing the assumptions, validating the predictions. Curr Opin HIV AIDS. 2010;5:269–76.
Grimm V, Augusiak J, Focks A, Frank B, Gabsi F, Johnston ASA, et al. Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE. Ecol Modell. 2014;280:129–39.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339: b2535.
Shaw GM, Hunter E. HIV transmission. Cold Spring Harb Perspect Med. 2012;2.
Public Health Agency of Canada. HIV TRANSMISSION RISK: A SUMMARY OF THE EVIDENCE [Internet]. 2010. Available from: https://www.catie.ca/sites/default/files/HIV-TRANSMISSION-RISK-EN.pdf
Glaubius RL, Hood G, Penrose KJ, Parikh UM, Mellors JW, Bendavid E, et al. Cost-effectiveness of injectable preexposure prophylaxis for HIV prevention in South Africa. Clin Infect Dis. 2016;63:539–47.
Glaubius RL, Parikh UM, Hood G, Penrose KJ, Bendavid E, Mellors JW, et al. Deciphering the Effects of Injectable Pre-exposure Prophylaxis for Combination Human Immunodeficiency Virus Prevention. Open forum Infect Dis [Internet]. Oxford University Press; 2016;3:ofw125–ofw125. Available from: https://pubmed.ncbi.nlm.nih.gov/27703992
van Vliet MM, Hendrickson C, Nichols BE, Boucher CA, Peters RP, van de Vijver DA. Epidemiological impact and cost-effectiveness of providing long-acting pre-exposure prophylaxis to injectable contraceptive users for HIV prevention in South Africa: a modelling study. J Int AIDS Soc. 2019;22: e25427.
Glaubius R, Ding Y, Penrose KJ, Hood G, Engquist E, Mellors JW, et al. Dapivirine vaginal ring for HIV prevention: modelling health outcomes, drug resistance and cost-effectiveness. J Int AIDS Soc. 2019;22: e25282.
Smith JA, Anderson S-J, Harris KL, McGillen JB, Lee E, Garnett GP, et al. Maximising HIV prevention by balancing the opportunities of today with the promises of tomorrow: a modelling study. Lancet HIV. 2016;3:e289–96.
Dimitrov D, Kublin JG, Ramsey S, Corey L. Are clade specific HIV vaccines a necessity? An analysis based on mathematical models. EBioMedicine. 2015;2:2062–9.
Punyacharoensin N, Edmunds WJ, De Angelis D, Delpech V, Hart G, Elford J, et al. Effect of pre-exposure prophylaxis and combination HIV prevention for men who have sex with men in the UK: a mathematical modelling study. lancet HIV. Netherlands; 2016;3:e94–104.
Alsallaq RA, Buttolph J, Cleland CM, Hallett T, Inwani I, Agot K, et al. The potential impact and cost of focusing HIV prevention on young women and men: A modeling analysis in western Kenya. PLoS ONE. 2017;12: e0175447.
Cepeda J, Borquez A, Farrell M, Degenhardt L, McKetin R, Tran LT, et al. Integrating HIV pre-exposure prophylaxis and harm reduction among men who have sex with men and transgender women to address intersecting harms associated with stimulant use: a modelling study. J Int AIDS Soc [Internet]. Switzerland: John Wiley and Sons Inc. (E-mail: [email protected]); 2020;23 Suppl 1:e25495. Available from: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=med18&NEWS=N&AN=32562365
Kelly SL, Martin-Hughes R, Stuart RM, Yap XF, Kedziora DJ, Grantham KL, et al. The global Optima HIV allocative efficiency model: targeting resources in efforts to end AIDS. Lancet HIV. 2018;5:e190–8.
Pharaon J, Bauch CT. The impact of pre-exposure prophylaxis for human immunodeficiency virus on gonorrhea prevalence. Bull Math Biol. 2020;82:85.
Medlock J, Pandey A, Parpia AS, Tang A, Skrip LA, Galvani AP. Effectiveness of UNAIDS targets and HIV vaccination across 127 countries. Proc Natl Acad Sci U S A. 2017;114:4017–22.
Afassinou K, Chirove F, Govinder KS. Pre-exposure prophylaxis and antiretroviral treatment interventions with drug resistance. Math Biosci. 2017;285:92–101.
Akudibillah G, Pandey A, Medlock J. Maximizing the benefits of ART and PrEP in resource-limited settings. Epidemiol Infect. 2017;145:942–56.
de Montigny S, Adamson BJS, Mâsse BR, Garrison LPJ, Kublin JG, Gilbert PB, et al. Projected effectiveness and added value of HIV vaccination campaigns in South Africa: A modeling study. Sci Rep. 2018;8:6066.
Bórquez A, Guanira JV, Revill P, Caballero P, Silva-Santisteban A, Kelly S, et al. The impact and cost-effectiveness of combined HIV prevention scenarios among transgender women sex-workers in Lima, Peru: a mathematical modelling study. Lancet Public Heal. 2019;4:e127–36.
Bernard CL, Owens DK, Goldhaber-Fiebert JD, Brandeau ML. Estimation of the cost-effectiveness of HIV prevention portfolios for people who inject drugs in the United States: a model-based analysis. PLoS Med. 2017;14: e1002312.
Bernard CL, Brandeau ML, Humphreys K, Bendavid E, Holodniy M, Weyant C, et al. Cost-effectiveness of HIV preexposure prophylaxis for people who inject drugs in the United States. Ann Intern Med. 2016;165:10–9.
Anderson S-J, Garnett GP, Enstone J, Hallett TB, Anderson SJG, Enstone J, Hallett TB. The importance of local epidemic conditions in monitoring progress towards HIV epidemic control in Kenya: a modelling study. J Int AIDS Soc. 2018;21:e25203.
Cremin I, McKinnon L, Kimani J, Cherutich P, Gakii G, Muriuki F, et al. PrEP for key populations in combination HIV prevention in Nairobi: a mathematical modelling study. Lancet HIV. 2017;4:e214–22.
McGillen JB, Stover J, Klein DJ, Xaba S, Ncube G, Mhangara M, et al. The emerging health impact of voluntary medical male circumcision in Zimbabwe: An evaluation using three epidemiological models. PLoS ONE. 2018;13: e0199453.
Mitchell KM, Prudden HJ, Washington R, Isac S, Rajaram SP, Foss AM, et al. Potential impact of pre-exposure prophylaxis for female sex workers and men who have sex with men in Bangalore, India: a mathematical modelling study. J Int AIDS Soc. 2016;19:20942.
Stuart RM, Fraser-Hurt N, Kerr CC, Mabusela E, Madi V, Mkhwanazi F, et al. The City of Johannesburg can end AIDS by 2030: modelling the impact of achieving the Fast-Track targets and what it will take to get there. J Int AIDS Soc. 2018;21.
Mitchell KM, Lépine A, Terris-Prestholt F, Torpey K, Khamofu H, Folayan MO, et al. Modelling the impact and cost-effectiveness of combination prevention amongst HIV serodiscordant couples in Nigeria. AIDS [Internet]. Lippincott Williams & Wilkins; 2015;29:2035–44. Available from: https://pubmed.ncbi.nlm.nih.gov/26355574
Ying R, Sharma M, Heffron R, Celum CL, Baeten JM, Katabira E, et al. Cost-effectiveness of pre-exposure prophylaxis targeted to high-risk serodiscordant couples as a bridge to sustained ART use in Kampala. Uganda J Int AIDS Soc. 2015;18:20013.
Marukutira T, Scott N, Kelly SL, Crowe S, Stoove M, Hellard M, et al. Modelling the impact of migrants on the success of the HIV care and treatment program in Botswana. PLoS ONE. 2020;15: e0226422.
Awad SF, Sgaier SK, Lau FK, Mohamoud YA, Tambatamba BC, Kripke KE, et al. Could circumcision of HIV-positive males benefit voluntary medical male circumcision programs in Africa? Mathematical Modeling Analysis. PLoS ONE. 2017;12: e0170641.
Awad SF, Sgaier SK, Tambatamba BC, Mohamoud YA, Lau FK, Reed JB, et al. Investigating voluntary medical male circumcision program efficiency gains through subpopulation prioritization: insights from application to Zambia. PLoS ONE. 2015;10: e0145729.
Awad SF, Sgaier SK, Ncube G, Xaba S, Mugurungi OM, Mhangara MM, et al. A reevaluation of the voluntary medical male circumcision scale-up plan in Zimbabwe. PLoS ONE. 2015;10: e0140818.
Blaizot S, Huerga H, Riche B, Ellman T, Shroufi A, Etard J-F, et al. Combined interventions to reduce HIV incidence in KwaZulu-Natal: a modelling study. BMC Infect Dis. 2017;7:522.
Blaizot S, Maman D, Riche B, Mukui I, Kirubi B, Ecochard R, et al. Potential impact of multiple interventions on HIV incidence in a hyperendemic region in Western Kenya: a modelling study. BMC Infect Dis. 2016;16:189.
Blaizot S, Riche B, Maman D, Mukui I, Kirubi B, Etard J-F, et al. Estimation and short-term prediction of the course of the HIV epidemic using demographic and health survey methodology-like data. PLoS ONE. 2015;10: e0130387.
MacFadden DR, Tan DH, Mishra S. Optimizing HIV pre-exposure prophylaxis implementation among men who have sex with men in a large urban centre: a dynamic modelling study. J Int AIDS Soc. 2016;19:20791.
Cremin I, Hallett TB. Estimating the range of potential epidemiological impact of pre-exposure prophylaxis: run-away success or run-away failure? AIDS 20155;29:733–8
Drabo EF, Hay JW, Vardavas R, Wagner ZR, Sood N. A cost-effectiveness analysis of preexposure prophylaxis for the prevention of HIV among Los Angeles county men who have sex with men. Clin Infect Dis United States. 2016;63:1495–504.
Cremin I, Morales F, Jewell BL, O’Reilly KR, Hallett TB. Seasonal PrEP for partners of migrant miners in southern Mozambique: a highly focused PrEP intervention. J Int AIDS Soc. 2015;18:19946.
Gromov D, Bulla I, Silvia Serea O, Romero-Severson EO. Numerical optimal control for HIV prevention with dynamic budget allocation. Math Med Biol. 2018;35:469–91.
Smith JA, Garnett GP, Hallett TB. The Potential Impact of Long-Acting Cabotegravir for HIV Prevention in South Africa: A Mathematical Modeling Study. J Infect Dis [Internet]. United States; 2021;224:1179–86. Available from: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=prem&NEWS=N&AN=32492704
Shen M, Xiao Y, Rong L, Meyers LA, Bellan SE. The cost-effectiveness of oral HIV pre-exposure prophylaxis and early antiretroviral therapy in the presence of drug resistance among men who have sex with men in San Francisco. BMC Med. 2018;16:58.
Adamson B, Garrison L, Barnabas RV, Carlson JJ, Kublin J, Dimitrov D. Competing biomedical HIV prevention strategies: potential cost-effectiveness of HIV vaccines and PrEP in Seattle, WA. J Int AIDS Soc. 2019;22: e25373.
Li J, Peng L, Gilmour S, Gu J, Ruan Y, Zou H, et al. A mathematical model of biomedical interventions for HIV prevention among men who have sex with men in China. BMC Infect Dis. 2018;18:600.
Foss AM, Vickerman PT, Heise L, Watts CH. Shifts in condom use following microbicide introduction: should we be concerned? AIDS [Internet]. 2003;17. Available from: https://journals.lww.com/aidsonline/Fulltext/2003/05230/Shifts_in_condom_use_following_microbicide.15.aspx
What Is the Impact of HIV on Racial and Ethnic Minorities in the U.S.? [Internet]. HIV.gov. [cited 2022 Jun 7]. Available from: https://www.hiv.gov/hiv-basics/overview/data-and-trends/impact-on-racial-and-ethnic-minorities
CDC. HIV and African American People [Internet]. 2022. Available from: https://www.cdc.gov/hiv/pdf/group/racialethnic/africanamericans/cdc-hiv-africanamericans.pdf
Huang Y-LA, Zhu W, Smith DK, Harris N, Hoover KW. HIV Preexposure Prophylaxis, by Race and Ethnicity - United States, 2014-2016. MMWR Morb Mortal Wkly Rep. 2018;67:1147–50.
Brantley ML, Rebeiro PF, Pettit AC, Sanders A, Cooper L, McGoy S, et al. Temporal trends and sociodemographic correlates of PrEP uptake in Tennessee, 2017. AIDS Behav. 2019;23:304–12.
Kanny D, Jeffries WL 4th, Chapin-Bardales J, Denning P, Cha S, Finlayson T, et al. Racial/ethnic disparities in HIV preexposure prophylaxis among men who have sex with men-23 Urban Areas, 2017. MMWR Morb Mortal Wkly Rep. 2019;68:801–6.
Sidebottom D, Ekström AM, Strömdahl S, Sidebottom D, Ekstrom AM, Stromdahl S. A systematic review of adherence to oral pre exposure prophylaxis for HIV—how can we improve uptake and adherence? BMC Infect Dis. 2018;18(1):581.
Seekaew P, Pengnonyang S, Jantarapakde J, Meksena R, Sungsing T, Lujintanon S, et al. Discordance between self-perceived and actual risk of HIV infection among men who have sex with men and transgender women in Thailand: a cross-sectional assessment. J Int AIDS Soc. 2019;22: e25430.
Baidoobonso S, Bauer GR, Speechley KN, Lawson E. HIV risk perception and distribution of HIV risk among African, Caribbean and other Black people in a Canadian city: mixed methods results from the BLACCH study. BMC Public Health. 2013;13:184.
Stringer EM, Sinkala M, Kumwenda R, Chapman V, Mwale A, Vermund SH, et al. Personal risk perception, HIV knowledge and risk avoidance behavior, and their relationships to actual HIV serostatus in an urban African obstetric population. J Acquir Immune Defic Syndr. 2004;35:60–6.
Kibombo R, Neema S, Ahmed FH. Perceptions of risk to HIV infection among adolescents in Uganda: are they related to sexual behaviour? Afr J Reprod Health. 2007;11:168–81.
Maughan-Brown B, Venkataramani AS. Accuracy and determinants of perceived HIV risk among young women in South Africa. BMC Public Health. 2017;18:42.
Warren EA, Paterson P, Schulze WS, Lees S, Eaklel R, Stadler J, et al. Risk perception and the influence on uptake and use of biomedical prevention interventions for HIV in sub-Saharan Africa: a systematic literature review. PLoS ONE. 2018;13:6.
Rozhnova G, Heijne J, Bezemer D, van Sighem A, Presanis A, De Angelis D, et al. Elimination prospects of the Dutch HIV epidemic among men who have sex with men in the era of preexposure prophylaxis. AIDS [Internet]. Lippincott Williams & Wilkins; 2018;32:2615–23. Available from: https://pubmed.ncbi.nlm.nih.gov/30379687
Barclay TR, Hinkin CH, Castellon SA, Mason KI, Reinhard MJ, Marion SD, et al. Age-associated predictors of medication adherence in HIV-positive adults: health beliefs, self-efficacy, and neurocognitive status. Health Psychol. 2007;26:40–9.
Mallayasamy S, Chaturvedula A, Fossler MJ, Sale ME, Hendrix CW, Haberer JE. Assessment of demographic and socio-behavioral factors on adherence to HIV pre-exposure prophylaxis using a markov modeling approach. Front Pharmacol. 2019;10:785.
Madrasi K, Chaturvedula A, Haberer JE, Sale M, Fossler MJ, Bangsberg D, et al. Markov mixed effects modeling using electronic adherence monitoring records identifies influential covariates to HIV preexposure prophylaxis. J Clin Pharmacol England. 2017;57:606–15.
Spinelli MA, Glidden DV, Anderson PL, Gandhi M, Cohen S, Vittinghoff E, et al. Brief report: short-term adherence marker to PrEP predicts future nonretention in a large PrEP demo project: implications for point-of-care adherence testing. J Acquir Immune Defic Syndr. 2019;81:158–62.
Long EF, Stavert RR. Portfolios of biomedical HIV interventions in South Africa: a cost effectiveness analysis. J Gen Intern Med. 2013;28:1294–301.
Walensky RP. Combination HIV prevention: the value and interpretation of mathematical models. Curr HIV/AIDS Rep. 2013;10:195–8.
Wingood GM, Rubtsova A, DiClemente RJ, Metzger D, Blank M. A new paradigm for optimizing HIV intervention synergy: the role of interdependence in integrating HIV prevention interventions. J Acquir Immune Defic Syndr. 2013;63(Suppl 1):S108–13.
Excler J-L, Rida W, Priddy F, Gilmour J, McDermott AB, Kamali A, et al. AIDS vaccines and preexposure prophylaxis: is synergy possible? AIDS Res Hum Retroviruses. 2011;27:669–80.
UNAIDS. FAST-TRACKING COMBINATION PREVENTION: TOWARDS REDUCING NEW HIV INFECTIONS TO FEWER THAN 500 000 BY 2020 [Internet]. 2015. Available from: https://www.unaids.org/sites/default/files/media_asset/20151019_JC2766_Fast-tracking_combination_prevention.pdf
Egger M, Johnson L, Althaus C, Schöni A, Salanti G, Low N, et al. Developing WHO guidelines: time to formally include evidence from mathematical modelling studies. F1000Research. 2017;6:1584.
Turnovsky SJ. On the role of small models in macrodynamics. J Econ Dyn Control. 2011;35:1605–13.
Dorratoltaj N, Nikin-Beers R, Ciupe SM, Eubank SG, Abbas KM. Multi-scale immunoepidemiological modeling of within-host and between-host HIV dynamics: systematic review of mathematical models. PeerJ. 2017;5: e3877.
Akpa OM, Oyejola BA. Modeling the transmission dynamics of HIV/AIDS epidemics: an introduction and a review. J Infect Dev Ctries Italy. 2010;4:597–608.
Birkegård AC, Halasa T, Toft N, Folkesson A, Græsbøll K. Send more data: a systematic review of mathematical models of antimicrobial resistance. Antimicrob Resist Infect Control. 2018;7:117. https://doi.org/10.1186/s13756-018-0406-1.
Harris RC, Sumner T, Knight GM, White RG. Systematic review of mathematical models exploring the epidemiological impact of future TB vaccines. Hum Vaccin Immunother [Internet]. 2016/07/22. Taylor & Francis; 2016;12:2813–32. Available from: https://pubmed.ncbi.nlm.nih.gov/27448625
Grant H, Foss AM, Watts C, Medley GF, Mukandavire Z. Is modelling complexity always needed? Insights from modelling PrEP introduction in South Africa. J Public Health (Oxf). 2020;42:e551–60.
Jenness SM, Sharma A, Goodreau SM, Rosenberg ES, Weiss KM, Hoover KW, et al. Individual HIV risk versus population impact of risk compensation after HIV preexposure prophylaxis initiation among men who have sex with men. PLoS ONE. 2017;12: e0169484.
Jenness SM, Goodreau SM, Rosenberg E, Beylerian EN, Hoover KW, Smith DK, et al. Impact of the centers for disease control’s HIV preexposure prophylaxis guidelines for men who have sex with men in the United States. J Infect Dis. 2016;214:1800–7.
Goodreau SM, Rosenberg ES, Jenness SM, Luisi N, Stansfield SE, Millett GA, et al. Sources of racial disparities in HIV prevalence in men who have sex with men in Atlanta, GA, USA: a modelling study. Lancet HIV. 2017;4:e311–20.
Müller B, Balbi S, Buchmann CM, de Sousa L, Dressler G, Groeneveld J, et al. Standardised and transparent model descriptions for agent-based models: Current status and prospects. Environ Model Softw [Internet]. 2014;55:156–63. Available from: https://www.sciencedirect.com/science/article/pii/S1364815214000395
Rueda ST, Terris-Prestholt F, et al. Health Economics Research on Non-Surgical, Biomedical HIV Prevention: Identifying Gaps and Proposing a Way Forward. Pharmacoeconomics. 2023;In Print.
Richardson BC. Richardson BC, Joscelyn KB, Saalberg JH, editors. Limitations on the use of mathematical models in transportation policy analysis1979. 1979.
Rojas-Vallejos J. Strengths and limitations of mathematical models in pandemicsthe case of COVID-19 in Chile. Medwave. 2020;20: e7876.
Saltelli A, Bammer G, Bruno I, Charters E, Di Fiore M, Didier E, et al. Five ways to ensure that models serve society: a manifesto. Nature England. 2020;582:482–4.