An entropy reduction approach to continual testing
Sundara Rajan Srinivasavaradhan, Pavlos Nikolopoulos, Christina Fragouli, Suhas Diggavi
Abstract
SIR (Susceptible, Infected or Recovered) stochastic network models are commonly used to describe the progression of epidemics inside a network. A task of interest in epidemiology is to use these models to estimate the state evolution, both at an individual as well as a population level. In this paper, we propose using continual testing to improve the state estimation at the individual level. Our testing is inspired from entropy reduction principles and requires only a small number of tests.
Topics & Concepts
Computer scienceEntropy (arrow of time)PopulationReduction (mathematics)Machine learningArtificial intelligenceMathematicsSociologyGeometryQuantum mechanicsPhysicsDemographyGene Regulatory Network AnalysisComplex Network Analysis TechniquesMental Health Research Topics