ArticleSignal processing2022
Decision-making algorithms for learning and adaptation with application to COVID-19 data.
Article in Signal processing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Decision support for the quickest detection of critical COVID-19 phases.Scientific reports · 2021Article
- Quickest Detection of COVID-19 Pandemic Onset.IEEE signal processing letters · 2021Article
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Authors and funding
2 authors.
Funding
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Abstract
This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are structurally different and, therefore, algorithms that have proven successful for the former need not perform well when adjusted for the latter. Exploiting classical tools from quickest detection, we propose a tailored version of Page's test, referred to as BLLR (barrier log-likelihood ratio) test, and demonstrate its applicability to real-data from the COVID-19 pandemic in Italy. The results illustrate the ability of the design tool to track the different phases of the outbreak.
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Registered trials
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