Literature DB >> 25803164

Optima: A Model for HIV Epidemic Analysis, Program Prioritization, and Resource Optimization.

Cliff C Kerr1, Robyn M Stuart, Richard T Gray, Andrew J Shattock, Nicole Fraser-Hurt, Clemens Benedikt, Markus Haacker, Maxim Berdnikov, Ahmed Mohamed Mahmood, Seham Abdalla Jaber, Marelize Gorgens, David P Wilson.   

Abstract

Optima is a software package for modeling HIV epidemics and interventions that we developed to address practical policy and program problems encountered by funders, governments, health planners, and program implementers. Optima's key feature is its ability to perform resource optimization to meet strategic HIV objectives, including HIV-related financial commitment projections and health economic assessments. Specifically, Optima allows users to choose a set of objectives (such as minimizing new infections, minimizing HIV-related deaths, and/or minimizing long-term financial commitments) and then determine the optimal resource allocation (and thus program coverage levels) for meeting those objectives. These optimizations are based on the following: calibrations to epidemiological data; assumptions about the costs of program implementation and the corresponding coverage levels; and the effects of these programs on clinical, behavioral, and other epidemiological outcomes. Optima is flexible for which population groups (specified by behavioral, epidemiological, and/or geographical factors) and which HIV programs are modeled, the amount of input data used, and the types of outputs generated. Here, we introduce this model and compare it with existing HIV models that have been used previously to inform decisions about HIV program funding and coverage targets. Optima has already been used in more than 20 countries, and there is increasing demand from stakeholders to have a tool that can perform evidence-based HIV epidemic analyses, revise and prioritize national strategies based on available resources, set program coverage targets, amend subnational program implementation plans, and inform the investment strategies of governments and their funding partners.

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Year:  2015        PMID: 25803164     DOI: 10.1097/QAI.0000000000000605

Source DB:  PubMed          Journal:  J Acquir Immune Defic Syndr        ISSN: 1525-4135            Impact factor:   3.731


  40 in total

Review 1.  HIV transmission and source-sink dynamics in sub-Saharan Africa.

Authors:  Justin T Okano; Katie Sharp; Eugenio Valdano; Laurence Palk; Sally Blower
Journal:  Lancet HIV       Date:  2020-02-14       Impact factor: 12.767

Review 2.  Modeling and Cost-Effectiveness in HIV Prevention.

Authors:  Margo M Jacobsen; Rochelle P Walensky
Journal:  Curr HIV/AIDS Rep       Date:  2016-02       Impact factor: 5.071

3.  Estimates of global, regional, and national incidence, prevalence, and mortality of HIV, 1980-2015: the Global Burden of Disease Study 2015.

Authors: 
Journal:  Lancet HIV       Date:  2016-07-19       Impact factor: 12.767

4.  Modelling of HIV prevention and treatment progress in five South African metropolitan districts.

Authors:  Cari van Schalkwyk; Rob E Dorrington; Thapelo Seatlhodi; Claudia Velasquez; Ali Feizzadeh; Leigh F Johnson
Journal:  Sci Rep       Date:  2021-03-11       Impact factor: 4.379

5.  Balancing health and financial protection in health benefit package design.

Authors:  Katherine T Lofgren; David A Watkins; Solomon T Memirie; Joshua A Salomon; Stéphane Verguet
Journal:  Health Econ       Date:  2021-10-08       Impact factor: 2.395

6.  Estimating the Cost-Effectiveness of HIV Prevention Programmes in Vietnam, 2006-2010: A Modelling Study.

Authors:  Quang Duy Pham; David P Wilson; Cliff C Kerr; Andrew J Shattock; Hoa Mai Do; Anh Thuy Duong; Long Thanh Nguyen; Lei Zhang
Journal:  PLoS One       Date:  2015-07-21       Impact factor: 3.240

7.  Estimating the epidemic consequences of HIV prevention gaps among key populations.

Authors:  Sharmistha Mishra; Romain Silhol; Jesse Knight; Refilwe Phaswana-Mafuya; Daouda Diouf; Linwei Wang; Sheree Schwartz; Marie-Claude Boily; Stefan Baral
Journal:  J Int AIDS Soc       Date:  2021-07       Impact factor: 6.707

8.  HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment.

Authors:  Jessie L Juusola; Margaret L Brandeau
Journal:  Med Decis Making       Date:  2015-09-14       Impact factor: 2.749

9.  Optimizing HIV/AIDS resources in Armenia: increasing ART investment and examining HIV programmes for seasonal migrant labourers.

Authors:  Sherrie L Kelly; Andrew J Shattock; Cliff C Kerr; Robyn M Stuart; Arshak Papoyan; Trdat Grigoryan; Ruben Hovhannisyan; Samvel Grigoryan; Clemens Benedikt; David P Wilson
Journal:  J Int AIDS Soc       Date:  2016-06-07       Impact factor: 5.396

10.  Maximising HIV prevention by balancing the opportunities of today with the promises of tomorrow: a modelling study.

Authors:  Jennifer A Smith; Sarah-Jane Anderson; Kate L Harris; Jessica B McGillen; Edward Lee; Geoff P Garnett; Timothy B Hallett
Journal:  Lancet HIV       Date:  2016-07       Impact factor: 12.767

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