TY - GEN
T1 - Robust Pandemic Control Synthesis with Formal Specifications
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
AU - Xu, Zhe
AU - Duan, Xiaoming
N1 - Funding Information:
Zhe Xu is with the School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287. email: {xzhe1@asu.edu}. Xiaoming Duan is with the Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Austin, TX 78712. Email: {xiaomingduan.zju@gmail.com}. This research was partially supported by NSF 1652113.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Pandemics can bring a range of devastating consequences to public health and the world economy. Identifying the most effective control strategies has been the imperative task all around the world. Various public health control strategies have been proposed and tested against pandemic diseases (e.g., COVID-19). We study two specific pandemic control models: the susceptible, exposed, infectious, recovered (SEIR) model with vaccination control; and the SEIR model with shield immunity control. We express the pandemic control requirement in metric temporal logic (MTL) formulas. We then develop an iterative approach for synthesizing the optimal control strategies with MTL specifications. We provide simulation results in two different scenarios for robust control of the COVID-19 pandemic: one for vaccination control, and another for shield immunity control, with the model parameters estimated from data in Lombardy, Italy. The results show that the proposed synthesis approach can generate control inputs such that the time-varying numbers of individuals in each category (e.g., infectious, immune) satisfy the MTL specifications with robustness against initial state and parameter uncertainties.
AB - Pandemics can bring a range of devastating consequences to public health and the world economy. Identifying the most effective control strategies has been the imperative task all around the world. Various public health control strategies have been proposed and tested against pandemic diseases (e.g., COVID-19). We study two specific pandemic control models: the susceptible, exposed, infectious, recovered (SEIR) model with vaccination control; and the SEIR model with shield immunity control. We express the pandemic control requirement in metric temporal logic (MTL) formulas. We then develop an iterative approach for synthesizing the optimal control strategies with MTL specifications. We provide simulation results in two different scenarios for robust control of the COVID-19 pandemic: one for vaccination control, and another for shield immunity control, with the model parameters estimated from data in Lombardy, Italy. The results show that the proposed synthesis approach can generate control inputs such that the time-varying numbers of individuals in each category (e.g., infectious, immune) satisfy the MTL specifications with robustness against initial state and parameter uncertainties.
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U2 - 10.1109/CDC45484.2021.9683197
DO - 10.1109/CDC45484.2021.9683197
M3 - Conference contribution
AN - SCOPUS:85108500293
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2830
EP - 2835
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2021 through 17 December 2021
ER -