References¶
Disease overview¶
- Ref1
Doitsh et al., 2013. Cell death by pyroptosis drives CD4 T-cell depletion in HIV-1 infection. Nature, doi:10.1038/nature12940
- Ref2
Monroe et al., 2013. IFI16 DNA sensor is required for death of lymphoid CD4 T cells abortively infected with HIV. Science, doi:10.1126/science.1243640
- Ref3
aidsmap, http://www.aidsmap.com/HIV-1-and-HIV-2/page/1322970/
- Ref4
Sharp, P. M.; Hahn, B. H., 2011. Origins of HIV and the AIDS Pandemic. Cold Spring Harbor Perspectives in Medicine. 1 (1): a006841–a006835. doi:10.1101/cshperspect.a006841. PMC 3234451 Freely accessible. PMID 22229120.
- Ref5
http://book.bionumbers.org/how-many-virions-result-from-a-single-viral-infection/
- Ref6
Roach JC, Glusman G, Smit AF, et al., 2010. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. 328 (5978): 636–9. doi:10.1126/science.1186802. PMC 3037280 Freely accessible. PMID 20220176
- Ref7
Reeves, Jacqueline D. and Derdeyn, Cynthia A. Entry Inhibitors in HIV Therapy. Boston: Birkhauser Verlag, 2007.
- Ref8
Domingo, Esteban; Parrish, Colin R.; and Holland, John J. Origin and Evolution of Viruses. New York: Elsevier, 2008.
- Ref9
Strategic Timing of AntiRetroviral Treatment (START) study: https://www.nih.gov/news-events/news-releases/starting-antiretroviral-treatment-early-improves-outcomes-hiv-infected-individuals
- Ref10
Rodger et al, 2016. Sexual activity without condoms and risk of HIV transmission in serodifferent couples when the HIV-positive partner is using suppressive antiretroviral therapy. JAMA 316(2): 171-181.
- Ref11
UNAIDS, Children and HIV fact sheet. http://www.unaids.org/sites/default/files/media_asset/FactSheet_Children_en.pdf
- Ref12
Avert.org, Prevention of Mother-to-Child Transmission (PMTCT) of HIV. https://www.avert.org/professionals/hiv-programming/prevention/prevention-mother-child
- Ref13
UNAIDS, 90-90-90 - An ambitious treatment target to help end the AIDS epidemic http://www.unaids.org/en/resources/documents/2017/90-90-90
- Ref14
UNAIDS, HIV fact sheet http://www.unaids.org/en/resources/campaigns/globalreport2013/factsheet
- Ref15
Eaton JW, Johnson LF, Salomon JA, 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 Medicine 2012,9:e1001245.
- Ref16
Tanser F, Bärnighausen T, Grapsa E, Zaidi J, Newell M-L. High Coverage of ART Associated with Decline in Risk of HIV Acquisition in Rural KwaZulu-Natal, South Africa. Science 2013,339:966-971.
- Ref17
Akullian A, Bershteyn A, Jewell B, Camlin CS. The Missing 27%. AIDS 2017.
- Ref18
World Health Organization (WHO), Global Health Observatory (GHO) data. http://www.who.int/gho/hiv/en/
- Ref19
The Global HIV/AIDS Epidemic, https://www.hiv.gov/hiv-basics/overview/data-and-trends/global-statistics
- Ref20
World Health Organization (WHO) HIV/AIDS Fact Sheet http://www.who.int/mediacentre/factsheets/fs360/en/
- Ref21
UNAIDS, AIDS by the numbers http://www.unaids.org/en/resources/documents/2015/AIDS_by_the_numbers_2015
- Ref22
Kim, S.B, et al., 2014. Mathematical Modeling of HIV Prevention Measures Including Pre-Exposure Prophylaxis on HIV Incidence in South Korea. PLOS One. March 24, 2014. https://doi.org/10.1371/journal.pone.0090080
- Ref23
Eaton, J. W., et al. 2014. Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models. The Lancet Global Health. V2. e23-234. http://www.thelancet.com/journals/langlo/article/PIIS2214-109X(13)70172-4/fulltext
- Ref24
Meyer-Rath, G., Over, M., Klein, D., and Bershteyn, A., 2015. The Cost and Cost-Effectiveness of Alternative Strategies to Expand Treatment to HIV-Positive South Africans: Scale Economies and Outreach Costs - Working Paper 401. Center for Global Development. https://www.cgdev.org/publication/cost-and-cost-effectiveness-alternative-strategies-expand-treatment-hiv-positive-south
- Ref25
Bershyteyn, A., Klein, D.J., and Eckhoff, P.A., 2016. AGe-targeted HIV treatment and primary prevention as a ‘ring fence’ to efficiently intterupt the age patterns of transmission in generalized epidemic settings in South Africa. International Health. 8(4): 277-285. https://doi.org/10.1093/inthealth/ihw010
- Ref26
Klein, D., Eckhoff, P., and Bershteyn, A., 2015. Targeting HIV Services to Male Migrant Workers in Southern Africa Would Not Reverse Generalized HIV Epidemics. International Health. 7(2): 107-113. https://doi.org/10.1093/inthealth/ihv011
- Ref27
Rivadeneira, P.S, et al., 2014. Mathematical Modeling of HIV Dynamics After Antiretroviral Therapy Initiation: A Review. Biores Open Access v.3(5): 233-241. doi: 10.1089/biores.2014.0024
- Ref28
Ogunlaran, O.M., and Noutchie, C.O., 2016. Mathematical Model for an Effective Management of HIV Infection. BioMed Research International. Volume 2016, Article ID 217548, 6 pp. http://dx.doi.org/10.1155/2016/4217548
Model overview¶
- Ref29
Jun-jie, et al, 2010. Dynamic mathematical models of HIV/AIDS transmission in China. Chin Med J. 123(15): 2120-2127. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5523934/
- Ref30
Williams. 2014. Fitting and projecting HIV epidemics: Data, structure, and parsimony. https://arxiv.org/ftp/arxiv/papers/1412/1412.2788.pdf
Intrahost¶
- Ref31
Johnson LF, Dorrington R. Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions. Demographic Research 2006; 14:541–574.
Interventions¶
- Ref32
May M, Boulle A, Phiri S, et al. Prognosis of patients with HIV-1 infection starting antiretroviral therapy in sub-Saharan Africa: a collaborative analysis of scale-up programmes. The Lancet 2010; 376:449–457