![]() ![]() artificial intelligence in public healthĬitation: Seyedeh N.reinforcement learning, ending the HIV epidemic,.RL is suitable for evaluating phased public health decisions for infectious disease control. The results from the model demonstrated convergence. However, testing and retention-in-care alone did not achieve the 2030 EHE targets, suggesting the need for additional interventions. Results were mainly robust to the uncertainty in costs. The model suggests to scale-up retention-in-care programs to achieve and maintain high annual retention-rates while initiating with a high testing-frequency but relaxing it over a 10-year period as incidence decreases. As there is variability in support-programs needed to address barriers to care-access, we evaluated the sensitivity of optimal decisions to three cost-functions. We used a stochastic agent-based simulation to train the RL algorithm. We show that solving for testing and retention-in-care rates through appropriate reformulation using proxy decision-metrics overcomes the computational challenges of RL. We defined optimality as a sequence that maximizes quality-adjusted-life-years lived and minimizes HIV-testing and care-and-treatment costs. We trained an RL algorithm to identify a 'sequence' of combinations of HIV-testing and retention-in-care rates at 5-year intervals over 2015-2070 that optimally leads towards HIV elimination. We evaluate its feasibility in the context of the EHE goal. Reinforcement learning (RL), an artificial intelligence method, is ideal however, training RL algorithms and ensuring convergence to optimality are computationally challenging for large-scale stochastic problems. Considering the dynamic and stochastic complexity of the disease and the time dynamics of decision-making, solving for optimal combinations using commonly used methods of parametric optimization or exhaustive evaluation of pre-selected options are infeasible. Diagnosis and treatment are two most effective interventions, and thus, identifying corresponding optimal combinations of testing and retention-in-care rates would help inform implementation of relevant programs. The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 20 by 2030. ![]()
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