Optima

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.

Modeling and Cost-Effectiveness in HIV Prevention

“…Juusola and Brandeau designed a model to help decision makers determine the most advantageous investment in HIV treatment versus prevention for a population [41]. Kerr et al developed a model (Optima) that allows the user to specify certain program or spending objectives and then to determine the best resource allocation to meet those objectives [58]. For example, a user can define the program objective as “minimize HIV incidence by 2020” or “minimize resources needed to achieve a 15% reduction in HIV incidence.” These models are important steps towards helping policy makers allocate available funding effectively and economically.…”

Modelling the impact of migrants on the success of the HIV care and treatment program in Botswana

“…We applied the Optima HIV model, a dynamic, population-based HIV model to test our hypothesis (described in detail elsewhere [21,22]). The Optima model tracks the entire population of people living with HIV (PLHIV) in a country or region between health states: from infection to diagnosis, linkage to care, ART initiation, viral suppression, and death; and across CD4+ count stages (acute HIV infection, >500, 350-500, 200-350, 50-200, and <50 cells/μL).…”

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

“…We adapted the deterministic compartmental epidemic model structure of Optima HIV 18, making several modifications in order to better capture aspects of the care and treatment cascade (Figure 2a). Each population group included in the model has 37 possible health states: 7 treatment‐related states (susceptible, undiagnosed, diagnosed, in care, receiving ART and not virally suppressed, receiving ART and virally suppressed, lost‐to‐follow‐up), with all infected stages further disaggregated into 6 CD4‐related states (acute HIV infection, >500 cells/μL, 350 to 500 cells/μL, 200 to 350 cells/μL, 50 to 200 cells/μL, <50 cells/μL).…”

“…To understand what it will take for the City of Johannesburg to achieve the Fast‐Track targets, we adapted the Optima HIV epidemic and resource allocation model in order to capture the key aspects of the HIV care and treatment cascade 18. Although the Optima HIV model has been successfully applied in many countries to assess the impact and optimize the allocation of HIV programme spending 19, it has yet to be used for a detailed analysis of the care and treatment cascade, largely due to a lack of comprehensive data.…”