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