Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control

arXiv:2404.11130v2 Announce Type: replace-cross
Abstract: Since infectious pathogens start spreading into a susceptible population, mathematical models can provide policy makers with reliable forecasts and scenario analyses, which can be concretely implemented or solely consulted. In these complex epidemiological scenarios, machine learning architectures can play an important role, since they directly reconstruct data-driven models circumventing the specific modelling choices and the parameter calibration, typical of classical compartmental models. In this work, we discuss the efficacy of Kernel Operator Learning (KOL) to …

abstract architectures arxiv control cs.lg cs.na epidemic kernel machine machine learning makers math.na math.oc modelling policy population q-bio.pe role through type