View all models by country

Select a country
Name of Model Research group Area of research Country Population Type of model Reference
ASSA

Leigh Johnson and Rob Dorrington (University of Cape Town)

Impact of prevention and treatment programmes, demographic impact of HIV, understanding epidemic drivers Botswana, South Africa General population (includes sex workers, but not MSM or PWID) Deterministic, compartmental, cohort component projection models
  • Johnson LF, Dorrington RE. Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions. Demographic Research. 2006;14:541-574.
BBH (Bärnighausen, Bloom, Humair)

Till Bärnighausen, David Bloom (Harvard School of Public Health) and Salal Humair (unaffiliated)

Impact of HIV-related biomedical and behavioural interventions on population health outcomes, targeted towards investigating resource allocation issues in HIV (e.g., cost-effectiveness of combination interventions, optimal allocation of a fixed budget across different interventions) Malawi, Rwanda, South Africa, Swaziland, Zambia Heterosexual couples, MSMs, FSWs Deterministic, analytical
  • Bärnighausen, T., D. E. Bloom and S. Humair (2012). "Economics of antiretroviral treatment vs. circumcision for HIV prevention." Proceedings of the National Academy of Sciences 109(52): 21271-21276.

Birger Saigon IDU Model

Ruthie Birger (Princeton University)

HIV-HCV coinfection, prevention, mortality Ho Chi Minh City, Viet Nam IDU Deterministic, compartmental
Eaton, Hallett (submitted)

Jeff Eaton, Tim Hallett (Imperial College London)

Impact of biomedical prevention interventions, drivers of epidemics, modelling methodology South Africa, Zambia General population Deterministic population-based model
Eaton, Hallett, Garnett 2011

Jeff Eaton, Tim Hallett, Geoff Garnett (Imperial College London)

Theoretical model exploring the interaction between concurrent sexual partnerships and elevated infectiousness during primary HIV infection. Sub-Saharan Africa General heterosexual population Deterministic population-based model
Gems

Olivia Keiser, Janne Estill, Luisa Salazar and Nello Blaser (University of Bern)

Modelling methodology, resource allocation / decision making, optimising treatment and patient management South Africa General population Individual based cohort model
  • Estill J, Aubrière C, Egger M, Johnson L, Wood R, Garone D, Gsponer T, Wandeler G, Boulle A, Davies MA, Hallett TB, Keiser O; IeDEA Southern Africa. Viral load monitoring of antiretroviral therapy, cohort viral load and HIV transmission in Southern Africa: a mathematical modelling analysis. AIDS. 2012;26(11):1403-13.
Goals Model

John Stover, Carel Pretorius, Chaitra Gopalappa, Kyeen Anderson and Lori Bollinger (Futures Institute)

HIV epidemic dynamics, impacts of prevention interventions (both biomedical and behavior change, adults and children), impacts of new prevention technologies, resource allocation Botswana, Brazil, Cambodia, Cameroon, China, Côte d'Ivoire, Ethiopia, India, Indonesia, Kenya, Lesotho, Malawi, Mexico, Mozambique, Nigeria, Russian Federation, Rwanda, South Africa, Swaziland, Tanzania, United Republic of, Uganda, Ukraine, Zambia, Zimbabwe General population Deterministic, population-based
  • Schwartlander B, Stover J, Hallett T, Atun R, Avila C, Gouws E, et al. Towards and improved investment approach for an effective response to HV/AIDS. Lancet. 2011;377(9782): 2031-2041.
Goodreau et al.

Steven Goodreau et al. (University of Washington)

Impact of biomedical prevention interventions, Drivers of epidemics (any region), Impact of combination prevention approaches, Impact evaluation, Impact of behaviour-based prevention interventions in sub-Saharan Africa, Modelling methodology, Epidemic Surveillance / Projections / Estimates, Demographic impact of epidemic India, Kenya, Peru, United States MSM Stochastic network-based model
  • Goodreau SM, Carnegie NB, Vittinghoff E, Lama JR, Sanchez J, Grinsztejn B, Koblin BA, Mayer KH, Buchbinder SP. What drives the US and Peruvian HIV epidemics in men who have sex with men (MSM)? PLoS One. 2012;7(11): e50522.
  • Sullivan PS, Carballo-Dieguez A, Coates T, Goodreau SM, McGowan I, Sanders E, Smith A, Goswami P, Sanchez J. HIV Prevention Successes and Challenges for MSM. The Lancet. 2012;380(9839): 388-399.
HIV Care Cascade

Timothy Hallett, Jeffrey Eaton and Jack Olney (Imperial College London)

Impact of interventions directed at strengthening ART programmes in sub-Saharan Africa Kenya General population Individual-based stochastic model
  • Hallett T and Eaton JW. A Side Door Into Care Cascade for HIV-Infected Patients? J Acquir Immune Defic Syndr. 2013;63:S228–S232.
HIV Synthesis Transmission Model for MSM

Andrew Phillips, Valentina Cambiano, Alec Miners, Fiona Lampe, Alison Rodger, Fumiyo Nakagawa, Alison Brown, Noel Gill, Daniela De Angelis, Jonathan Elford, Graham Hart, Anne Johnson, Jens Lundgren, Simon Collins and Valerie Delpech

Questions relating to treatment and prevention in MSM in the UK, cost effectiveness analysis United Kingdom Men who have sex with men in UK Individual-based dynamic stochastic simulation model including transmission
  • Phillips AN, Cambiano V, Nakagawa F, Brown AE, Lampe F, Rodger A, 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(2).
HIV Synthesis Transmission Model for Sub-Saharan Africa

Andrew Phillips, Valentina Cambiano, Debbie Ford, Fumiyo Nakagawa, Loveleen Bansi (University College London), Alec Miners (LSHTM) and Paul Revill (University of York)

Impact of biomedical prevention interventions, impact of behavioural intervention approaches, impact of combination prevention approaches, Optimization of treatment and patient management (monitoring),Impact of introduction of new diagnostic technologies (e.g. new testing devices, resistance test, POC CD4 and VL), Resistance development and transmission Sub-Saharan Africa South Africa - General population: aged 15 to 65 years old. Sub Saharan Africa - General population: aged 15 to 65 years old. Zimbabwe - General population: aged 15 to 65 years old. Lesotho - General population: aged 15 to 65 years old. Individual-based dynamic stochastic simulation model including transmission. Model of progression is similar to the other Synthesis models, except features such as drug availability and diagnostic testing.
  • Phillips AN, Pillay D, Garnett G, Bennett D, Vitoria M, Cambiano V, et al. Effect on transmission of HIV-1 resistance of timing of implementation of viral load monitoring to determine switches from first to second-line antiretroviral regimens in resource-limited settings. AIDS. 2011; 25(6):843-850. 
  • Cambiano V, Bertagnolio S, Jordan M, Pillay D, Perriens J, Venter F, et al. Predicted levels of HIV drug resistance: potential impact of expanding diagnosis, retention, and eligibility criteria for antiretroviral therapy initiation. AIDS. 2014, 28 (Suppl 1):S15–S23 
  • Cambiano V, Bertagnolio , Jordan M, et al. Transmission of Drug Resistant HIV and Its Potential Impact on Mortality and Treatment Outcomes in Resource-Limited Settings. J Infect Dis. 2013; 207: S57-62
HIV-HEP

Natasha Martin (University of Bristol), Peter Vickerman (LSHTM) and the Social and Mathematical Epidemiology Group.

Impact of ART on HIV/HCV or HIV/HBV progression and vertical or sexual transmission India, South Africa, Zambia Individuals coinfected with HIV and hepatitis C virus, individuals coinfected with HIV and hepatitis B virus Coinfection disease progression cohort model with sexual and vertical transmission
ICRC HIV Transmission Model

Roger Ying (University of Washington International Clinical Research Center)

Biomedical interventions - ART as Treatment and as PrEP South Africa All ages, heterosexual transmission Compartmental, deterministic
  • Ying R, Celum C, Baeten J, Murnane P, Hong T, Krows M, van Rooyen H, Humphries H, Hughes JP, and Barnabas R. Pre-Exposure Prophylaxis (PrEP) is Estimated to Be a Cost-Effective Addition to Antiretroviral Therapy (ART) For HIV Prevention in a Generalised Epidemic Setting. Sex Transm Infect. 2013;89: A219
KZN Model

Íde Cremin, Timothy Hallett (Imperial College London)

Impact of combination HIV prevention KwaZulu-Natal, South Africa Heterosexual transmission, age, sex and risk structured Deterministic, compartmental, age, sex and risk structured
  • Cremin I, Alsallaq R, Dybul M, Piot P, Garnett G, Hallett TB. The new role of antiretrovirals in combination HIV prevention: a mathematical modelling analysis. Aids 2013,27:447-458.
Menzies TB-HIV

Ted Cohen, Megan Murray, Joshua Salomon, Hsein-ho Lin, Leonid Chindelevitch, and Nick menzies (Harvard School of Public Health)

TB and HIV, and effects (epidemiology, disease burden, resource utilization) of control interventions directed at these diseases Sub-Saharan Africa General population Deterministic state-transition model
  • Menzies NA, Cohen T, Lin H-H, Murray M, Salomon JA. Population Health Impact and Cost-Effectiveness of Tuberculosis Diagnosis with Xpert MTB/RIF: A Dynamic Simulation and Economic Evaluation. PLoS Med. 2012; 9(11): e1001347.
MOT-Dynamical Model

Marie-Claude Boily, Sharmistha Mishra, Elisa Mountain and Michael Pickles (Imperial College London)

Validation of the "Modes of Transmission model" using simulation (with dynamical models) India Various districts of Karnataka in South-India; Kisumu, Kenya; Lesotho (county-level) Dynamical deterministic; Modes of Transmission Model (generic and tailored)
  • Mishra S, Pickles M, Blanchard JF, Moses S, Boily MC. Distinguishing sources of HIV transmission from the distribution of newly acquired HIV infections: why is it important for HIV prevention planning? Sex Transm Infect. 2014;90(1):19-25.
  • Mishra S, Pickles M, Blanchard JF, Moses S, Shubber Z, Boily MC. Validation of the Modes of Transmission Model as a Tool to Prioritize HIV Prevention Targets: A Comparative Modelling Analysis. PLoS One. 2014;9(7):e101690.
Mozambique Model

Íde Cremin, Timothy Hallett (Imperial College London)

Impact of a Pre-Exposure Prophylaxis Intervention Gaza province, Mozambique Heterosexual transmission, sex and risk structured Deterministic, compartmental, general population, seasonal labour migration
MSM/Trans Model

Gabriela Gómez, Annick Bórquez, Tim Hallett, Geoff Garnett (Imperial College London), Carlos Cáceres and Eddy Segura (Universidad Cayetano Heredia), Bob Grant (UCSF).

Impact of potential PrEP intervention/ Evaluation of national HIV programme (assessing whether prevalence declines are likely to be due to interventions as opposed to natural epidemic dynamics) Peru MSM, MSW (male sex workers) and Trans Deterministic compartmental
  • Gomez GB, Borquez A, Caceres CF, Segura ER, Grant RM, Garnett GP, Hallett TB. 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 Medicine. 2012;9(10):1-15

Nairobi Model

Íde Cremin, Timothy Hallett (Imperial College London)

Impact of combination HIV prevention Nairobi, Kenya Includes male-male transmission as well as male-female transmission. It distinguishes the general population (by sex: male/female) and key populations (FSW, MSM, MSW) Deterministic, compartmental, general population and FSW, MSM, MSW
Nyanza Model

Íde Cremin, Timothy Hallett (Imperial College London)

Impact of combination HIV prevention Nyanza, Kenya Heterosexual transmission, sex and risk structured Deterministic, compartmental, sex and risk group structured
Optima

Wilson group, Kirby Institute, UNSW Australia

Allocative efficiency, impact evaluation, cost-effectiveness, epidemic and financial commitment projections, incidence estimation, cascade evaluation Global Region Any risk-based and demographic-based stratifications of populations are included Compartmental population-based model with optimization routines
  • Kerr et al. Optima: a model for HIV epidemic analysis, program prioritization, and resource optimization. J Acquir Immune Defic Syndr. 2015;69(3):365-76
  • Wilson et al. Allocating resources efficiently to address strategic objectives: optimisation of HIV responses. Under review.
  • Fraser et al. Sudan’s HIV response: Value for money in a low-level HIV epidemic; Findings from the HIV allocative efficiency study. Report published by the World Bank and UNSW Australia. 2014
  • Fraser et al. Niger’s HIV response: Targeted investments for a health future. Findings from the HIV allocative efficiency and financial sustainability study. Report published by the World Bank. 2014
PopART (population effects of antiretroviral therapy to reduce HIV transmission)

Christophe Fraser et al (Imperial College London)

Impact of universal testing and universal "test and treat" (delivered in the PopART trial) on the HIV incidence at the community level South Africa General population Deterministic compartmental model
Populate

Michael Bracher, Michael Bracher, Gigi Santow (Stockholm University)

Drivers of epidemics (any region), impact of combination prevention approaches, impact of behaviour-based prevention interventions in Sub-Saharan Africa, modelling methodology, epidemic surveillance / projections / estimates, demographic impact of epidemic Sub-Saharan Afria General population Microsimulation models
SIMPACT Cyan

Wim Delva, Fei Meng, Jori Liesenborgs, Niel Hens

Biological and behavioural drivers of HIV transmission in sub-Saharan Africa, and their interactions. Population impact of earlier ART initiation. Interventions that alter sexual network structure, in particular age-mixing patterns and concurrency level. Sub-Saharan Africa General population, pregnant women, sex workers Microsimulation model
SSOPHIE - Synthesis Progression Model

SSOPHIE project working group in EuroCoord (University College London)

Reconstruction of HIV epidemics in Europe to then characterise the HIV-infected population in detail, assessment of effects of interventions, projections Europe General population but currently adults only (plan to include children) initially in EU countries but approach can be used anywhere with case-based surveillance data available. Individual-based stochastic simulation model
  • Bansi L, Sabin C, Delpech V, et al. Trends over calendar time in antiretroviral treatment success and failure in HIV clinic populations. HIV Med. 2010;11(7):432-8 
  • Nakagawa F, Lodwick RK, Smith CJ, Smith R, Cambiano V, Lundgren JD, et al. Projected life expectancy of people with HIV according to timing of diagnosis. AIDS. 2012, 26:335–343
STDSIM

Jan Hontelez (Department of Public Health, Erasmus MC, Rotterdam, The Netherlands; and Africa Centre for Health and Population Studies, University of KwaZulu-Natal, South Africa)

HIV elimination, epidemiological impact of antiretroviral therapy, population level impact of health systems and cascade of care, cost-effectiveness of different modes of treatment delivery Ghana, Kenya, South Africa General population; female sex workers and male clients Stochastic microsimulation model
  • Steen R, Hontelez JA, Veraart A, White RG, de Vlas SJ. Looking upstream to prevent HIV transmission: can interventions with sex workers alter the course of HIV epidemics in Africa as they did in Asia? AIDS. 2014; 28: 891-9.
  • Eaton JW, Menzies NA, Stover J, Cambiano V, Chindelevitch L, Cori A, Hontelez JA, (...) Hallett TB. 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-e24
STI-HIV

Leigh Johnson, Rob Dorrington (University of Cape Town) and Debbie Bradshaw (South African Medical Research Council)

Understanding epidemic drivers (especially sexual behaviour and the role of other STIs), impact of prevention and treatment programmes, demographic impact of HIV South Africa General population (including sex workers) Deterministic, compartmental, cohort component projection models
  • Johnson LF, Dorrington RE, Bradshaw D, Pillay-Van Wyk V, Rehle TM. Sexual behaviour patterns in South Africa and their association with the spread of HIV: insights from a mathematical model. Demographic Research. 2009;21:289-340.
Strategic Epi-ART in India Model 

Marie-Claude Boily, Sharmistha Mishra, Elisa Mountain, Michael Pickles (Imperial College London) and Peter Vickerman (University of Bristol)

Epidemiological impact of ARV treatment and ARV resistance in India India Karnataka state in South India:  Belgaum, Shimoga, Mysore districts Deterministic model of heterosexual transmission 
  • Mishra S, Mountain E, Pickles M, Vickerman P, Shastri S, Gilks C, Dhingra NK, Washington R, Becker ML, Blanchard JF, Alary M, Boily MC; Strategic Epi-ART in India Modelling Team. Exploring the population-level impact of antiretroviral treatment: the influence of baseline intervention context. AIDS. 2014;28 Suppl 1:S61-72.
THEMBISA

Leigh Johnson and Rob Dorrington (University of Cape Town)

Impact of prevention and treatment programmes, demographic impact of HIV, understanding epidemic drivers South Africa General population (including sex workers) Deterministic, compartmental, cohort component projection models
  • Johnson L. THEMBISA version 1.0: A model for evaluating the impact of HIV/AIDS in South Africa. Centre for Infectious Disease Epidemiology and Research, University of Cape Town; 2014. 
Trans Sex Workers Model

Annick Bórquez (Imperial College London), Poteat T. and Wirtz A. (Johns Hopkins University), Radix A. (Callen Lorde Community Health Center), Silva-Santisteban A. (Universidad Cayetano Heredia, Peru), Deutsch M. (UCSF),  Islam Khan S. (Global Fund Bangladesh), Winter S. (University of Hong Kong), Operario D. (Brown University).

Impact of biomedical or behavioural interventions, patient monitoring): Impact of HIV combination prevention (PrEP, early ART treatment, increase in condom use with clients and stable partners, reduction in the number of commercial transactions) Peru, United States Trans sex workers, their clients and stable partners Deterministic compartmental
  • Poteat T, Wirtz AL, Radix A, Borquez A, Silva-Santisteban A, Deutsch MB, Khan SI, Winter S, Operario D. HIV risk and preventive interventions in transgender women sex workers. The Lancet. 2014;6736(14):60833-3