Control, intervention, and behavioral economics over human social networks against COVID-19

Abstract

In this short paper, we propose a new direction of cross-cutting research for prediction and control of spreading COVID-19 viruses over a human social network. Such a network consists of human agents whose behaviors are highly uncertain and biased. To predict and control such an uncertain network, we need to employ various researches such as control theory, signal processing, machine learning, and behavioral economics. In this article, we introduce our recent research results and propose future research topics to overcome the COVID-19 pandemic.

GRAPHICAL ABSTRACT

Keywords:

• Optimal control
• human social networks
• COVID-19
• cyber-physical-human system (CPHS)
• behavioral economics

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partly supported by JSPS KAKENHI [grant numbers JP20H02172, JP20K21008, JP19H02301, JP18K13777, JP20H02145], JST PRESTO [grant number JPMJPR1935], and JST RISTEX [grant number JPMJRX19I2].

Notes on contributors

M. Nagahara

M. Nagahara is a Full Professor with the Institute of Environmental Science and Technology, The University of Kitakyushu. He has been a Visiting Professor with Indian Institute of Technology (IIT) Bombay since 2017 and IIT Guwahati since 2020. He received the bachelor's degree in engineering from Kobe University in 1998, and the master's degree and the Doctoral degree in informatics from Kyoto University in 2000 and 2003, respectively. His research interests include control theory, machine learning, and sparse modeling. He received the Transition to Practice Award in 2012 and George S. Axelby Outstanding Paper Award in 2018 from IEEE Control Systems Society. He also received Young Authors Award in 1999, Best Paper Award in 2012, and Best Book Authors Award in 2016, and Kimura Award in 2020 from SICE, and Best Tutorial Paper Award in 2014 from IEICE Communications Society. He is a senior member of IEEE.

B. Krishnamachari

B. Krishnamachari is Professor of Electrical and Computer Engineering at the Viterbi School of Engineering at the University of Southern California. He has expertise in design and analysis of algorithms and the development and evaluation of protocols and software for the internet of things, connected vehicles, distributed computing, applied machine learning, and blockchain technologies.

M. Ogura

M. Ogura is an Associate Professor in the Graduate School of Information Science and Technology at Osaka University, Japan. Prior to joining Osaka University, he was a Postdoctoral Researcher at the University of Pennsylvania, USA and an Assistant Professor at the Nara Institute of Science and Technology, Japan. His research interests include network science, dynamical systems, and stochastic processes with applications in networked epidemiology, design engineering, and biological physics. He was a runner-up of the 2019 Best Paper Award by the IEEE Transactions on Network Science and Engineering and a recipient of the 2012 SICE Best Paper Award. He is an Associate Editor of the Journal of the Franklin Institute.

A. Ortega

A. Ortega is a Professor of Electrical and Computer Engineering at the University of Southern California (USC). He received his undergraduate and doctoral degrees from the Universidad Politecnica de Madrid, Madrid, Spain and Columbia University, New York, NY, respectively. He is a Fellow of the IEEE and EURASIP, and a member of ACM and APSIPA. He is the Editor-in-Chief of the IEEE Transactions of Signal and Information Processing over Networks and recently served as a member of the Board of Governors of the IEEE Signal Processing Society. He has received several paper awards, including the 2016 Signal Processing Magazine award. His recent research work is focusing on graph signal processing, machine learning, multimedia compression and wireless sensor networks. He is the author of an upcoming book, ”Introduction to Graph Signal Processing”, to be published by Cambridge University Press in 2021.

Y. Tanaka

Y. Tanaka is an Associate Professor in the Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan. Currently, he has a cross appointment as a PRESTO Researcher, Japan Science and Technology Agency. He is an IEEE Senior Member and served as an associate editor for the IEEE Transactions on Signal Processing from 2016 to 2020. His current research interests are in the field of high-dimensional signal processing and machine learning which includes: Graph signal processing, geometric deep learning, sensor networks, image/video processing in extreme situations, biomedical signal processing, and remote sensing.

Y. Ushifusa

Y. Ushifusa is a Professor in Faculty of Economics and Business Administration, The University of Kitakyushu, Fukuoka, Japan. He received his PhD from Kyoto University, Kyoto, Japan. His research interests include survey causal inference, field experiments, machine learning, and empirical analysis on consumer behavior.

T. W. Valente

T. W. Valente is a Professor in the Department of Preventive Medicine, Keck School of Medicine, at the University of Southern California. His research focuses on the influence of social networks on a wide variety of behaviors including tobacco and other substance use, alcohol, reproductive health, physician behavior, and policy adoption. Valente has pioneered the application of social network theory and methods to accelerate the diffusion of innovations.