Decision-Making Algorithms for Learning and Adaptation with Application to COVID-19 Data
This work addresses the problem of developing tailored decision-making algorithms for real-world applications like pandemic tracking, which could benefit public health officials.
This paper introduces BLLR, a new family of decision-making algorithms based on first principles from decision theory. Applied to COVID-19 data from Italy, the algorithm successfully tracked different phases of the outbreak.
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 decision problems. We propose a new scheme, referred to as BLLR (barrier log-likelihood ratio algorithm) 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.