A Closed-Loop Framework for Inference, Prediction and Control of SIR Epidemics on Networks

TL;DR

This paper introduces a closed-loop framework that integrates inference, parameter learning, and optimal control to manage COVID-19 spread on networks, emphasizing early testing and NPI timing.

Contribution

It presents a novel integrated framework combining data inference, epidemic modeling, and control optimization tailored for network-based SIR epidemics.

Findings

Early testing significantly reduces epidemic spread.

Premature withdrawal of NPIs can lead to a second wave.

The framework effectively balances epidemic control and intervention costs.

Abstract

Motivated by the ongoing pandemic COVID-19, we propose a closed-loop framework that combines inference from testing data, learning the parameters of the dynamics and optimal resource allocation for controlling the spread of the susceptible-infected-recovered (SIR) epidemic on networks. Our framework incorporates several key factors present in testing data, such as the fact that high risk individuals are more likely to undergo testing. We then present two tractable optimization problems to evaluate the trade-off between controlling the growth-rate of the epidemic and the cost of non-pharmaceutical interventions (NPIs). We illustrate the significance of the proposed closed-loop framework via extensive simulations and analysis of real, publicly-available testing data for COVID-19. Our results illustrate the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.