The number of persons newly infected with the human immunodeficiency virus (HIV) in the U.S. is about 50,000 each year and has not decreased since the late 1990s. To address this critical problem, the first National HIV/AIDS Strategy (NHAS) was developed in 2010, with a goal to reduce incidence by 25 percent by 2015; but, since that goal was never met, it was delayed until 2020. Now Professor Chaitra Gopalappa of our Mechanical and Industrial Engineering (MIE) Department is receiving a grant of $1,567,348 from the National Institutes of Health (NIH) to answer several critical questions posed by the NHAS and to develop a new model and methods necessary for analyses of these crucial problems.
One fundamental impact of Gopalappa’s research will be to help create an efficient NHAS, a national plan that serves as a “roadmap” for implementing interventions to reduce the incidence of HIV infections.
The title of Gopalappa’s NIH project is “Evaluating Portfolio Interventions for HIV Incidence Reduction in the United States: Development of a Novel Agent-Based Decision-Analytic Model for Dynamic Evaluations of Interventions.” As the principal investigator, Gopalappa is coordinating her own mathematical modeling done at UMass Amherst with a team composed of Paul Farnham, Stephanie Sansom, and Yao-Hsuan Chen at the Centers for Disease Control and Prevention in Atlanta, along with Robert McKinnon at Avenir Health in Glastonbury, Connecticut.
Gopalappa’s expertise is in advancing mathematical methodologies to derive information that may help in decision-making for public health strategies. She works closely with the Centers for Disease Control and Prevention and the World Health Organization on non-communicable diseases such as cancers and communicable diseases such as HIV.
Gopalappa does research that integrates methods from simulation modeling, stochastic processes, and optimization modeling for economic analysis of public health strategic plans. As she notes, “National and global strategic plans for disease prevention and control are extremely critical as they drive allocation of resources to intervention programs from national to local levels. Decision-makers are often faced with a challenge of developing evidence-based strategies when usually such decision-making precedes evidence availability.”
At UMass Amherst Gopalappa runs the Disease Prediction and Prevention Lab in the MIE department, which, as she says, “works on development of new methodologies and computational models for simulating the dynamics of disease incidence and spread for purposes of disease prediction, prevention, and control.”
To achieve its incidence goal, the NHAS has proposed activities aimed at reaching specific target coverages on nine key care and behavioral indicators to increase retention in HIV care from 51 percent to 90 percent. NHAS drives most HIV-related activities at local and national levels and how resources are allocated, with an annual federal spending budget of about $25 billion including about $8 billion for HIV prevention.
As Gopalappa says about her NIH research, her team will analyze three vital questions that are unresolved but form a fundamental knowledge base for developing the NHAS.
The first question is what combinations of the nine indicator targets and corresponding population-specific intervention programs can optimally achieve a certain percentile reduction in incidence? As Gopalappa explains, “NHAS aims at high coverage of cost-effective interventions evaluated independently in different studies. Due to interactions between interventions, the overall impact is not the sum of its individual parts.”
In addition, HIV funding is also not in-line with the ambitious NHAS targets. Spreading the limited resources too sparsely across too many programs could lead to little impact on each. “Our preliminary analyses indicate that intervention portfolios to break the constant trend in infections are much higher than values at baseline year,” says Gopalappa. “That is, even if interventions scale-up, unless they pass this threshold coverage, the constant incidence trend will continue. Analyses of this critical question will help better allocate resources.”
As for the second question: What are the interacting effects of population-specific intervention programs in the context of population mixing and population migration? “Due to considerable differences in HIV-risk and care disparities, intervention decisions are currently based on analyses of smaller populations or local jurisdictions,” says Gopalappa. “However, under significant population mixing and migration, along chronic stage of HIV, how these decisions impact each other is not studied.”
The third and last question asks what are appropriate ‘proxy’ metrics for dynamically measuring progress toward the NHAS goal? As Gopalappa explains, “Incidence in a specific year can only be determined four or five years later, which is a critical barrier that impedes dynamic control. NHAS suggests use of new diagnoses as proxy for incidence, but our analyses indicates this is a misleading metric.”
Gopalappa also proposes development of new models and methods necessary for analyses of the above questions, including a multi-level, real-time, decision-analytic model to enable integrated analysis (including national, local, risk-group, and individual input) of population-specific intervention portfolios under dynamics of population mixing and migration. She will also be working on new methodologies for dynamically simulating individual-level networks of multiple contact types with high clustering. Epidemiology of HIV is a result of dynamics of individual-level interacting events over the life of each individual and between individuals through a network of sexual and needle-sharing partners. Current methodologies are infeasible to apply at the national level since only specific groups or smaller populations are currently modeled at individual level.
Expected outcomes include analyses of a new methodology for simulating sexual and needle sharing contact networks; a new model for multi-level integrated analyses of population-specific portfolio interventions for HIV; and an open-access software package of MIDA-PATH for real-time decision analyses.
All this research could help create a national plan for the US, a virtual roadmap for the implementation of interventions to reduce the incidence of HIV infections. In addition, Gopalappa’s mathematical concepts, if successful, will be foundational for developing advanced disease models; e.g. for real-time decision analyses during outbreaks of emerging infectious diseases. (August 2017)