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A reinforcement learning model to inform optimal decision paths for HIV elimination
Seyedeh N Khatami1, Chaitra Gopalappa1
1Mechanical and Industrial Engineering Department, University of Massachusetts Amherst, Amherst, MA 01003, USA.
Mathematical Biosciences and Engineering : MBE
|November 24, 2021
Summary
Reinforcement learning models optimal HIV testing and retention-in-care rates to guide the Ending the HIV Epidemic initiative. While effective, these strategies alone may not meet 2030 targets, indicating a need for additional interventions.
Area of Science:
- Public Health
- Infectious Disease Modeling
- Artificial Intelligence in Healthcare
Background:
- The Ending the HIV Epidemic (EHE) initiative aims to significantly reduce HIV incidence in the US by 2030.
- Optimal combinations of HIV testing and retention-in-care are crucial for achieving EHE goals, but traditional optimization methods are infeasible due to disease complexity.
- Reinforcement learning (RL) offers a potential solution for complex public health decision-making, despite computational challenges.
Purpose of the Study:
- To evaluate the feasibility of using RL to identify optimal sequences of HIV testing and retention-in-care rates for HIV elimination.
- To determine if RL can overcome computational challenges in large-scale stochastic public health problems.
- To inform the implementation of EHE programs by providing data-driven strategies.
Main Methods:
- Trained an RL algorithm using a stochastic agent-based simulation to find optimal testing and retention-in-care rates over 5-year intervals (2015-2070).
- Defined optimality by maximizing quality-adjusted life-years and minimizing costs associated with HIV testing and treatment.
- Evaluated the sensitivity of optimal decisions to cost-function variations and addressed computational challenges through proxy decision-metrics.
Main Results:
- The RL model identified an optimal sequence, suggesting scaling up retention-in-care programs and initially high testing frequency, gradually decreasing over time as incidence declines.
- Results were robust to cost uncertainties, demonstrating the model's stability.
- The study confirmed RL's convergence and suitability for phased public health decision-making in infectious disease control.
Conclusions:
- HIV testing and retention-in-care strategies, optimized via RL, are essential but may be insufficient to meet the 2030 EHE targets alone.
- Additional interventions are likely necessary to achieve comprehensive HIV elimination.
- RL is a viable and powerful tool for optimizing complex, dynamic public health strategies for infectious disease control.
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