Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control
This work addresses the need for data-driven epidemic modeling to inform policy decisions, though it is incremental as it builds on existing kernel learning methods.
The authors tackled the problem of forecasting epidemic dynamics and optimizing intervention strategies by introducing two surrogate models, KOL-m and KOL-∂, using Kernel Operator Learning. They demonstrated that these models are competitive for fast, robust forecasts and optimal control, outperforming classical neural network methods in generalization with synthetic data.
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 reconstruct population dynamics during epidemic outbreaks, where the transmission rate is ruled by an input strategy. In particular, we introduce two surrogate models, named KOL-m and KOL-$\partial$, which reconstruct in two different ways the evolution of the epidemics. Moreover, we evaluate the generalization performances of the two approaches with different kernels, including the Neural Tangent Kernels, and compare them with a classical neural network model learning method. Employing synthetic but semi-realistic data, we show how the two introduced approaches are suitable for realizing fast and robust forecasts and scenario analyses, and how these approaches are competitive for determining optimal intervention strategies with respect to specific performance measures.