Reduced modelling and optimal control of epidemiological individual-based models with contact heterogeneity

TL;DR

This paper introduces a neural network-based reduced model combined with optimal control to efficiently determine health policies in complex, contact-heterogeneous epidemiological models, capturing super-spreader effects.

Contribution

It presents a novel approach integrating reinforcement learning and reduced modeling to optimize epidemic control strategies in large, contact-heterogeneous graphs.

Findings

Reduced models can accurately mimic complex graph dynamics.

Optimal policies derived are effective in containing epidemics.

Method is applicable to real-world health policy planning.

Abstract

Modelling epidemics via classical population-based models suffers from shortcomings that so-called individual-based models are able to overcome, as they are able to take heterogeneity features into account, such as super-spreaders, and describe the dynamics involved in small clusters. In return, such models often involve large graphs which are expensive to simulate and difficult to optimize, both in theory and in practice. By combining the reinforcement learning philosophy with reduced models, we propose a numerical approach to determine optimal health policies for a stochastic epidemiological graph-model taking into account super-spreaders. More precisely, we introduce a deterministic reduced population-based model involving a neural network, and use it to derive optimal health policies through an optimal control approach. It is meant to faithfully mimic the local dynamics of the original, more complex, graph-model. Roughly speaking, this is achieved by sequentially training the network until an optimal control strategy for the corresponding reduced model manages to equally well contain the epidemic when simulated on the graph-model. After describing the practical implementation of this approach, we will discuss the range of applicability of the reduced model and to what extent the estimated control strategies could provide useful qualitative information to health authorities.