Disease control as an optimization problem

TL;DR

This paper introduces a convex optimization framework using machine learning techniques to optimize complex, uncertain disease control policies over many parameters, improving upon traditional methods for epidemic management.

Contribution

It presents a novel optimization approach capable of handling high-dimensional, uncertain, and stochastic disease policies, surpassing previous control theory methods.

Findings

Optimized COVID-19 eradication time in SEIR model

Framework handles uncertainties and stochastic models

Allows for policies with many parameters and constant over weekly periods

Abstract

In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set. This scheme runs the risk of leaving out more complex (and perhaps counter-intuitive) policies for disease control that could tackle the disease more efficiently. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease, and stochastic models. In addition, our methods allow for optimization over policies which remain constant over weekly periods, specified by either continuous or discrete (e.g.: lockdown on/off) government measures. We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler \emph{et al.} (March, 2020).