Convex Optimization of the Basic Reproduction Number

Review Automation & Control Systems

Multitask learning and nonlinear optimal control of the COVID-19 outbreak: A geometric programming approach

Mikhail Hayhoe et al.

Summary: The study proposes a multitask learning approach to learn parameters of an epidemic model and design optimal control strategies for human-mobility restrictions to curb the spread of COVID-19 with minimum economic costs. By using real data, the study demonstrates the effectiveness of the approach in the Philadelphia metropolitan area during the pandemic.

ANNUAL REVIEWS IN CONTROL (2021)

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Article Engineering, Multidisciplinary

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

Ashish R. Hota et al.

Summary: Motivated by the COVID-19 pandemic, a closed-loop framework is proposed to control the spread of the epidemic on networks by incorporating key factors in testing data and evaluating the trade-off between controlling the growth-rate of the epidemic and the cost of NPIs. Results show the importance of early testing and the risk of a second wave of infections if NPIs are prematurely withdrawn.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2021)

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Article Automation & Control Systems

Sparse Resource Allocation for Control of Spreading Processes via Convex Optimization

Vera L. J. Somers et al.

Summary: This study presents a method for sparse allocation of resources to control spreading processes using convex optimization, particularly exponential cone programming. By introducing a risk model to optimize the product of the likelihood and future impact of an outbreak, the method can provide more targeted resource allocation. Experimental results with a simplified wildfire example show the effectiveness of this approach compared to previous geometric programming methods.

IEEE CONTROL SYSTEMS LETTERS (2021)

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Article Automation & Control Systems