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...
Saved in:
Main Authors: | Seyedeh N. Khatami, Chaitra Gopalappa |
---|---|
Format: | article |
Language: | EN |
Published: |
AIMS Press
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/f18f1efdd24747e58680c9ce413feb68 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluación de la infección por VIH en Chile: pronunciamiento del Comité VIH de la Academia Chilena de Medicina
by: Wolff R.,Marcelo, et al.
Published: (2020) -
Tackling pandemics in smart cities using machine learning architecture
by: Desire Ngabo, et al.
Published: (2021) -
Almost periodic solutions for a SVIR epidemic model with relapse
by: Yifan Xing, et al.
Published: (2021) -
Comparison of adhesive bond strength among fiber reinforced post and core with different cementation techniques: In vitro study
by: Abdulaziz AlHelal, et al.
Published: (2021) -
Mathematical modeling of impact of eCD4-Ig molecule in control and management of HIV within a host
by: Tae Jin Lee, et al.
Published: (2021)