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Stock networks, which are constructed from stock price time series, are useful tools for analyzing complex behaviors in stock markets. Following former researches, the epidemic model has been usually used to detect dynamic characteristics in a stock price complex systems. Recently, multilayer networks have been demonstrated well when working on heterogeneous nodes rather than integrated networks. In this paper, we proposed a two-layer SIR propagation model with an infective medium to analyze the spread of financial shocks. In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng(medium), we established a two-layer Granger networks. Betweenness showed that the Hong Kong stock market had a promoting transition function of financial shocks between the US stock markets and the mainland China stock markets. In addition, with a big basic reproduction number, stock markets system appeared to be vulnerable during extreme financial shock such as the outbreak of COVID-19 epidemic and the meltdown of stock markets. Furthermore, sensitivity analysis and the spreading simulation indicated that the US stock markets were much more robust to financial shocks than the mainland China stock markets. • New two-layer SIR propagation model with an infected medium for financial shocks. • Two-layer Granger networks based on the US stock markets, the mainland China stock markets, and the Hong Kong stock market. • Focusing on economic influence caused by COVID-19 epidemic. • Monte Carlo algorithm simulates the model in different conditions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, an exceptional crisis, sparked the introduction of new digital infrastructure to halt the novel coronavirus's spread. This paper explores how such digital infrastructure's impact might reverberate over the long term, by comparing Singapore, Hong Kong, and mainland China's utilization of digital technology in response to the 2003 SARS outbreak, and their responses to the 2020 COVID-19 pandemic. We find that advancements in digital technology since 2003 have boosted governments' surveillance and segregation abilities substantially—most dramatically so in China. Even though some of these new digital interventions are ostensibly designed to be temporary ones to address the needs of the immediate crisis, we argue that the resultant extensions of state power experienced during COVID-19 are likely to have profound long-term effects because they fundamentally affect sociopolitical contexts, institutional capabilities, and digital cultures. We also find that the extent to which governments can extend digital surveillance and segregation abilities during the pandemic is contingent on their respective sociopolitical, institutional, and digital cultural contexts. • COVID-19 pandemic sparked calls for a new digital infrastructure. • We compare 2003 SARS outbreak to COVID-19 pandemic. • New digital technologies increased governments' ability to surveil and segregate. • Their impact on surveillance and segregation will persist post COVID-19. [ABSTRACT FROM AUTHOR]