Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study.
Michael F RayoDaria FaulknerDavid M KlineThomas A ThornhillSamuel MalloyDante Della VellaDane A MoreyNet ZhangGregg S GonsalvesPublished in: JMIR public health and surveillance (2023)
This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV.
Keyphrases
- sars cov
- machine learning
- deep learning
- respiratory syndrome coronavirus
- antiretroviral therapy
- hiv infected
- public health
- randomized controlled trial
- human immunodeficiency virus
- hiv positive
- systematic review
- hepatitis c virus
- hiv aids
- hiv testing
- coronavirus disease
- men who have sex with men
- quantum dots
- clinical evaluation
- sensitive detection