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      Global systemic risk and resilience for novel coronavirus in postpandemic era

      editorial

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          Abstract

          The ongoing pandemic has evolved and is posing diverse challenges for the world. Countermeasures for risks are needed to address both direct and indirect effects of disease on the healthcare system, economic and industrial sectors, governance, environment, transportation, energy, and communication systems. There are indicators of a forthcoming postpandemic era. The rethinking and reevaluation of policies adopted throughout the pandemic are ongoing to address cascading threats of emerging and reemerging infectious diseases. The first Special Issue introduced the topic. This second Special Issue describes international collaboration and innovation for pandemic risk and resilience, with a focus on future policy and operations of global systems toward a postandemic era.

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          Epidemic Propagation With Positive and Negative Preventive Information in Multiplex Networks

          Zhishuang Wang, Chengyi Xia, Zengqiang Chen … (2020)
          We propose a novel epidemic model based on two-layered multiplex networks to explore the influence of positive and negative preventive information on epidemic propagation. In the model, one layer represents a social network with positive and negative preventive information spreading competitively, while the other one denotes the physical contact network with epidemic propagation. The individuals who are aware of positive prevention will take more effective measures to avoid being infected than those who are aware of negative prevention. Taking the microscopic Markov chain (MMC) approach, we analytically derive the expression of the epidemic threshold for the proposed epidemic model, which indicates that the diffusion of positive and negative prevention information, as well as the topology of the physical contact network have a significant impact on the epidemic threshold. By comparing the results obtained with MMC and those with the Monte Carlo (MC) simulations, it is found that they are in good agreement, but MMC can well describe the dynamics of the proposed model. Meanwhile, through extensive simulations, we demonstrate the impact of positive and negative preventive information on the epidemic threshold, as well as the prevalence of infectious diseases. We also find that the epidemic prevalence and the epidemic outbreaks can be suppressed by the diffusion of positive preventive information and be promoted by the diffusion of negative preventive information.
          Article 0 comments Cited 41 times – based on 0 reviews     Review now
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            It's Politics, Isn't It? Investigating Direct and Indirect Influences of Political Orientation on Risk Perception of COVID‐19

            Youngkee Ju, Myoungsoon You (2021)
            Public response to the COVID‐19 pandemic provides a unique opportunity to study risk perception in relation to political orientation. We tested a risk perception model of how political orientation influences risk perception of an emerging infectious disease and how it moderates other influences. Two nationwide online surveys in South Korea ( N = 2,000) revealed that conservatives showed a higher risk perception regarding an emerging infectious disease, and political orientation can even moderate the influence of perceived risk characteristics on risk perception such as how a liberal orientation exhibited a greater outrage effect of perceived unfairness on COVID‐19 risk perception. Also, the frequency of media use is positively related to higher risk perception. The implications of the direct and moderating effects of political orientation are discussed in the context of the studies of political orientation as well as risk perception.
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              Assessing the Impacts of COVID‐19 on the Industrial Sectors and Economy of China

              Ling Ling Tan, Xianhua Wu, Ji Yu Guo … (2021)
              Since December 2019, the COVID‐19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative‐based approach of assessing the potential impact of the COVID‐19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision‐making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4–8% in the absence of COVID‐19, GDP growth in 2020 would be ‐8.77 to ‐12.77% after the COVID‐19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.
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                Author and article information

                Contributors
                Desheng Dash Wu: dash@risklab.ca
                Jade Mitchell: jade@msu.edu
                Journal
                Journal ID (nlm-ta): Risk Anal
                Journal ID (iso-abbrev): Risk Anal
                Journal ID (doi): 10.1111/(ISSN)1539-6924
                Journal ID (publisher-id): RISA
                Title: Risk Analysis
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Print): 0272-4332
                ISSN (Electronic): 1539-6924
                Publication date (Electronic): 12 February 2022
                Publication date (Print): January 2022
                Publication date PMC-release: 12 February 2022
                Volume: 42
                Issue: 1 , Global Systemic Risk and Resilience for Novel Coronavirus in Post‐Pandemic Era ( doiID: 10.1111/risa.v42.1 )
                Pages: 1-4
                Affiliations
                [ 1 ] University of Chinese Academy of Sciences No. 80 Zhongguancun Beijing 10010 China
                [ 2 ] Department of Biosystems and Agricultural Engineering Michigan State University East Lansing, United States 48824 USA
                [ 3 ] Department of Engineering Systems and Environment University of Virginia Charlottesville Virginia USA
                Author notes
                [*] [* ] Correspondence

                Jade Mitchell or Desheng Dash Wu, Michigan State University, University of Chinese Academy of Sciences; dash@ 123456risklab.ca , jade@ 123456msu.edu

                Article
                Publisher ID: RISA13873
                DOI: 10.1111/risa.13873
                PMC ID: 9115504
                PubMed ID: 35152452
                SO-VID: dba46201-b329-4d6b-9cae-93bfa3024c7d
                Copyright © © 2021 Society for Risk Analysis
                License:

                This article is being made freely available through PubMed Central as part of the COVID-19 public health emergency response. It can be used for unrestricted research re-use and analysis in any form or by any means with acknowledgement of the original source, for the duration of the public health emergency.

                History
                Date received : 08 December 2021
                Date accepted : 08 December 2021
                Page count
                Figures: 0, Tables: 0, Pages: 4, Words: 2804
                Categories
                Subject: Editorial
                Subject: Editorial
                Custom metadata
                source-schema-version-number 2.0
                cover-date January 2022
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.6 mode:remove_FC converted:18.05.2022

                Keywords: data analytics,pandemic,risk analysis,systems analysis
                Data availability:
                Keywords: data analytics, pandemic, risk analysis, systems analysis

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