Nanophotonic reservoir computing for COVID-19 pandemic forecasting

Bocheng Liu; Yiyuan Xie; Weichen Liu; Xiao Jiang; Yichen Ye; Tingting Song; Junxiong Chai; Manying Feng; Haodong Yuan

doi:10.1007/s11071-022-08190-z

Nanophotonic reservoir computing for COVID-19 pandemic forecasting

Nonlinear Dyn. 2023;111(7):6895-6914. doi: 10.1007/s11071-022-08190-z. Epub 2022 Dec 27.

Authors

Bocheng Liu¹, Yiyuan Xie^{1

2

3}, Weichen Liu⁴, Xiao Jiang¹, Yichen Ye¹, Tingting Song⁵, Junxiong Chai¹, Manying Feng¹, Haodong Yuan¹

Affiliations

¹ School of Electronics and Information Engineering, Southwest University, Chongqing, 400715 China.
² Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing, Chongqing, 400715 China.
³ Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Chongqing, 400715 China.
⁴ School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore.
⁵ School of Computer and Information Science, Chongqing Normal University, Chongqing, 401331 China.

Abstract

The coronavirus disease 2019 (COVID-19) has spread worldwide in unprecedented speed, and diverse negative impacts have seriously endangered human society. Accurately forecasting the number of COVID-19 cases can help governments and public health organizations develop the right prevention strategies in advance to contain outbreaks. In this work, a long-term 6-month COVID-19 pandemic forecast in second half of 2021 and a short-term 30-day daily ahead COVID-19 forecast in December 2021 are successfully implemented via a novel nanophotonic reservoir computing based on silicon optomechanical oscillators with photonic crystal cavities, benefitting from its simpler learning algorithm, abundant nonlinear characteristics, and some unique advantages such as CMOS compatibility, fabrication cost, and monolithic integration. In essence, the nonlinear time series related to COVID-19 are mapped to the high-dimensional nonlinear space by the optical nonlinear properties of nanophotonic reservoir computing. The testing-dataset forecast results of new cases, new deaths, cumulative cases, and cumulative deaths for six countries demonstrate that the forecasted blue curves are awfully close to the real red curves with exceedingly small forecast errors. Moreover, the forecast results commendably reflect the variations of the actual case data, revealing the different epidemic transmission laws in developed and developing countries. More importantly, the daily ahead forecast results during December 2021 of four kinds of cases for six countries illustrate that the daily forecasted values are highly coincident with the real values, while the relevant forecast errors are tiny enough to verify the good forecasting competence of COVID-19 pandemic dominated by Omicron strain. Therefore, the implemented nanophotonic reservoir computing can provide some foreknowledge on prevention strategy and healthcare management for COVID-19 pandemic.

Keywords: COVID-19 pandemic spreading forecasting; Nanophotonic reservoir computing; Nonlinear time series; Optical nonlinearity; Silicon optomechanical oscillators.

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