A reinforcement learning model to inform optimal decision paths for HIV elimination

A reinforcement learning model to inform optimal decision paths for HIV elimination

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 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corresponding optimal combinations of testing...

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Bibliographic Details
Main Authors: Seyedeh N. Khatami, Chaitra Gopalappa
Format: Article
Language:English
Published: AIMS Press 2021-09-01
Series:Mathematical Biosciences and Engineering
Subjects:
decision-making in epidemics
agent-based simulation modeling
national hiv goals
reinforcement learning
ending the hiv epidemic
artificial intelligence in public health
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2021380?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2021380?viewType=HTML

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