A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.     

A probabilistic epidemiological model for infectious diseases: The case of COVID-19 at global-level

This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID-19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID-19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business-as-usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.     

生产性企业组织学习最优控制模型及其理论分析

运用最优控制理论研究生产性企业组织学习活动的动态最优决策问题.以企业的概念性学习投资率和操作性学习投资率为决策变量,累积知识量、生产率、单位成本和废品率等为状态变量,计划期内的总利润为指标函数,建立了一个最优控制模型,其特点在于规范地描绘概念性学习和操作性学习对企业累积知识量、生产率、单位成本、质量以及企业利润的动态影响.根据生产性企业组织学习和生产经营的实际情况,提出了一些定量化表达的假设和定义.在假设和定义的基础上利用最大值原理分析了所建立的模型,获得了关于动态最优概念性学习投资策略和操作性学习投资策略性质的一些结论,将这些结论与实际问题相结合,指出了在生产性企业组织学习实践上的含义.     

Strategic flexibility analysis of HRD research and practice post COVID-19 pandemic

ABSTRACT

While governments, intergovernmental organizations, non-profits, corporations are all aware that disruptions through pandemics and other natural bio-disasters like the COVID-19 pandemic can happen, barely are we proactive about them. Instead, we are always reactive. In a virtual Town Hall meeting of the Academy of Human Resource Development (AHRD) held on 9 April 2020, on the theme ‘How is the pandemic a game-changer for HRD?,’ the President of AHRD, Laura Bierema, challenged HRD scholars to determine the possible futures of HRD scholarship post-COVID-19 Pandemic. This article proposes the use of the Strategic Flexibility Framework (SFF) to determine the possible futures of HRD post-COVID-19 pandemic. I first discuss the SFF as a scenario planning and analysis tool. I then developed four scenarios of possible futures for HRD Research and Practice post-COVID-19 pandemic. These scenarios include the ‘Meaning of work,’ ‘Leadership,’ ‘Contactless Commerce & Education,’ and ‘Volunteerism.’ I conclude by discussing the important opportunities that can serve as intervention points for post-COVID-19 HRD theory, research, and practice.     

Careers after COVID-19: challenges and changes

ABSTRACT

There are few certainties in our visions of post-COVID-19 careers, but change is inevitable. This article will explore how HRD can be proactive in addressing the immediate needs of the post-pandemic workforce and workplaces, as they strive to recover and resume a productive future. Uncertainties about employment and employability, how workplaces will be configured, the future of some careers and the possibilities for new opportunities will weigh heavily on individuals as they navigate these challenges. Drawing on the career shock, resilience, and sustainable careers literature, we consider how both individual and contextual factors will impact people and their occupations moving forward.     

可及性视角下医疗组织分布特征及其对疫情防控的影响研究

基于可及性理论,从医疗服务资源的可获得性和可达性两个角度量化各地级市疫情防控定点医疗组织机构的空间分布特征以及空间组合模式,并探讨其对疫情防控效果的影响。研究结果显示,在控制了城市基本特征、人员流动程度、基础医疗资源状况和省级固定效应之后,按人口聚集分布定点医院显著负向影响各城市累计确诊病例数,而按人口聚集分布发热门诊显著增加确诊病例数。相比于其他空间组合模式,定点医院按人口聚集分布,发热门诊按空间平均分布的组合方式对于疫情防控的效果最好。随着疫情严重程度的上升,按人口聚集分布定点医院对于疫情的抑制作用以及按人口聚集分布发热门诊对疫情防控的负面影响被进一步强化。     

A novel textual track-data-based approach for estimating individual infection risk of COVID-19

With the recurrence of infectious diseases caused by coronaviruses, which pose a significant threat to human health, there is an unprecedented urgency to devise an effective method to identify and assess who is most at risk of contracting these diseases. China has successfully controlled the spread of COVID-19 through the disclosure of track data belonging to diagnosed patients. This paper proposes a novel textual track-data-based approach for individual infection risk measurement. The proposed approach is divided into three steps. First, track features are extracted from track data to build a general portrait of COVID-19 patients. Then, based on the extracted track features, we construct an infection risk indicator system to calculate the infection risk index (IRI). Finally, individuals are divided into different infection risk categories based on the IRI values. By doing so, the proposed approach can determine the risk of an individual contracting COVID-19, which facilitates the identification of high-risk populations. Thus, the proposed approach can be used for risk prevention and control of COVID-19. In the empirical analysis, we comprehensively collected 9455 pieces of track data from 20 January 2020 to 30 July 2020, covering 32 provinces/provincial municipalities in China. The empirical results show that the Chinese COVID-19 patients have six key features that indicate infection risk: place, region, close-contact person, contact manner, travel mode, and symptom. The IRI values for all 9455 patients vary from 0 to 43.19. Individuals are classified into the following five infection risk categories: low, moderate-low, moderate, moderate-high, and high risk.     

How makers responded to the Personal Protective Equipment shortage during the COVID-19 pandemic: An analysis focused on the Hauts-de-France region

The COVID-19 pandemic saw makers mobilize around the world to address a shortage of medical equipment. Our research focuses on a group of makers organized in the Hauts-de-France region. Combining interviews with tool-based and hybrid method (quantitative/qualitative), we analyzed the collaborative messaging room used to coordinate the production of face shields. That field work enabled us to update the profile of the participants, the intensity of their contribution, the nature of the innovation implemented, the coordination mechanisms, and the associated difficulties. We identified different makers' profiles given their backgrounds, expertise, and network. We evaluated the type of collaboration they developed with the local public and private actors through an online platform, and how technology facilitated the interactions and circulation of information despite the confinement. We evaluated the level of performance from the point of view of an organization, knowledge transfer, and invention. It reveals how these practices can successfully evolve from resilience to innovation. We discuss the potential of exploiting the makers’ open source practices in collaborative and innovative territory dynamics with an interest in sharing the knowledge commons.     

内部控制监督最优投资分配模型及决策研究

内部控制监督是现代企业改善经营管理、降低重大内控缺陷发生可能性的重要措施． 本文构建了一个企业内部控制监督的最优投资分配模型． 该模型提出了内部控制缺陷率和流程可靠性两个概念,认为增加内部控制监督的投资可以降低内部控制缺陷率,从而增加流程可靠性． 本文借鉴了工程领域的可靠性理论,定义了内部控制监督投资的效用函数,以企业效用最大化为决策目标,考虑在投资预算额一定的情况下资金在各个业务流程中的分配策略． 模型分析表明,以串联方式连接的流程在增加内部控制监督投资时,投资额的大小与该流程实际发生 风险的可能性相关,实际发生风险的可能性越大,投资额与该流程内控强度的乘积就越大． 而在并联系统中,流程投资额的大小与各流程的内控强度有关． 但当各个流程的内部控制强度相等时,并联系统的最优投资额在各流程间平均分配时系统的效用最大． 此外,本文还证明了并联系统的效用高于串联系统．     

Risk heatmaps as visual displays: Opening movie studios after the COVID-19 shutdown

Upon shutting down operations in early 2020 due to the COVID-19 pandemic, the movie industry assembled teams of experts to help develop guidelines for returning to operation. It resulted in a joint report, The Safe Way Forward, which was created in consultation with union members and provided the basis for negotiations with the studios. A centerpiece of the report was a set of heatmaps displaying SARS-CoV-2 risks for a shoot, as a function of testing rate, community infection prevalence, community transmission rate (R0), and risk measure (either expected number of cases or probability of at least one case). We develop and demonstrate a methodology for evaluating such complex displays, in terms of how well they inform potential users, in this case, workers deciding whether the risks of a shoot are acceptable. We ask whether individuals making hypothetical return-to-work decisions can (a) read display entries, (b) compare display entries, and (c) make inferences based on display entries. Generally speaking, respondents recruited through the Amazon MTurk platform could interpret the display information accurately and make coherent decisions, suggesting that heatmaps can communicate complex risks to lay audiences. Although these heatmaps were created for practical, rather than theoretical, purposes, these results provide partial support for theoretical accounts of visual information processing and identify challenges in applying them to complex settings.     

Three dimensions of COVID-19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis

Social media analysis provides an alternate approach to monitoring and understanding risk perceptions regarding COVID-19 over time. Our current understandings of risk perceptions regarding COVID-19 do not disentangle the three dimensions of risk perceptions (perceived susceptibility, perceived severity, and negative emotion) as the pandemic has evolved. Data are also limited regarding the impact of social determinants of health (SDOH) on COVID-19-related risk perceptions over time. To address these knowledge gaps, we extracted tweets regarding COVID-19-related risk perceptions and developed indicators for the three dimensions of risk perceptions based on over 502 million geotagged tweets posted by over 4.9 million Twitter users from January 2020 to December 2021 in the United States. We examined correlations between risk perception indicator scores and county-level SDOH. The three dimensions of risk perceptions demonstrate different trajectories. Perceived severity maintained a high level throughout the study period. Perceived susceptibility and negative emotion peaked on March 11, 2020 (COVID-19 declared global pandemic by WHO) and then declined and remained stable at lower levels until increasing once again with the Omicron period. Relative frequency of tweet posts on risk perceptions did not closely follow epidemic trends of COVID-19 (cases, deaths). Users from socioeconomically vulnerable counties showed lower attention to perceived severity and susceptibility of COVID-19 than those from wealthier counties. Examining trends in tweets regarding the multiple dimensions of risk perceptions throughout the COVID-19 pandemic can help policymakers frame in-time, tailored, and appropriate responses to prevent viral spread and encourage preventive behavior uptake in the United States.     

责任式创新的多主体演化博弈研究——以新冠疫苗研发为例

责任式创新需要多主体的共同参与,探索企业、消费者、政府等多主体的演化博弈规律,对于推动以新冠疫苗研发为典型代表的责任式创新具有重要意义。本文以新冠疫苗研发为例,构建了企业、消费者和政府三方在实施责任式创新行为中的演化博弈模型,并通过MATLAB进行数值模拟。结果表明,在责任式创新过程中,三方最终的演化稳定策略是企业实施责任式创新、公众参与责任式创新,但政府弱干预(消极提倡)责任式创新;企业的收益和成本、政府补贴均影响企业和消费者的演化结果,而对政府决策影响较小;利益相关者的初始状态对三方演化收敛速度有显著影响。     

COVID-19 and business failures: The paradoxes of experience,scale, and scope for theory and practice

In light of growing scholarly works on business failure, across the social science domains, it is surprising that past studies have largely overlooked how extreme environmental shocks and ‘black swan’ events such as those caused by the coronavirus (COVID-19) pandemic and other global crises, can precipitate business failures. Drawing insights from the current literature on business failure and the unfolding event of COVID-19, we highlight the paradoxes posed by novel exogenous shocks (that is, shocks that transcend past experiences) and the implications for SMEs. The pandemic has accelerated the reconfiguration of the relationship between states and markets, increasing the divide between those with political connections and those without, and it may pose new legitimacy challenges for some players even as others seem less concerned by such matters, whilst experiential knowledge resources may be both an advantage and a burden.     

Interactions among the human and organizational factors within the public sector regarding epidemic prevention and control

The management of human and organizational factors (HOFs) within the public sector directly concerns the efficacy of epidemic prevention and control (EPC). Insufficient examination of such HOFs has led to defective countermeasures. This study attempts to comprehensively identify the HOFs within the public sector critical to EPC and investigate their interactions with the weighted network theory. A total of 55 HOFs were identified, and their interactions were assessed and visualized in the Chinese context. Then, the established weighted network was analyzed to investigate the interactions and diagnose critical factors and sectors. The analysis shows that there are strong interactions among HOFs, and that the human and organizational risks emerging from administrative departments of public health, centers for disease control and prevention, and medical institutions act as the key risk sources in the complex interconnected EPC system, exacerbating risk and causing a significant domino effect. It is recommended that the authorities devote more resources to the core sectors and endeavor to reinforce those critical HOFs by implementing closer risk communication, collaboration, and response. This study may deepen and broaden the authorities’ awareness and understanding of interactions among HOFs regarding epidemic mitigation, and strengthen their capacity to perceive, evaluate, and manage these factors in a proactive and effective way, thereby facilitating the success of EPC.     

Perceived risk,emotions, and stress in response to COVID-19: The interplay of media use and partisanship

Relationships between risk perceptions, emotions, and stress are well-documented, as are interconnections between stress, emotion, and media use. During the early COVID-19 pandemic, the public responded psychologically to the threat posed by the pandemic, and frequently utilized media for information and entertainment. However, we lack a comprehensive picture of how perceived risk, emotion, stress, and media affected each other longitudinally during this time. Further, although response to the pandemic was highly politicized, research has yet to address how partisan affiliation moderated relationships between risk, emotion, stress, and media use over time. This three-wave (N = 1021) panel study assessed the interplay of risk, emotion, stress, and media use for Americans with different political affiliations between March and May of 2020. Findings indicate that perceived risk, emotion, and stress at Time 1 predicted media use at Time 2, with predictors varying by type of media. Use of entertainment media and social/mobile media predicted later stress (Time 3), but news consumption did not. Later risk perceptions (Time 3) were not influenced by media use at Time 2. The predictors and consequences of different types of media use were notably different for Republicans and Democrats. In particular, risk perceptions predicted greater news use among Democrats but greater entertainment media use among Republicans. Moreover, social/mobile media use resulted in perceiving the risks of COVID-19 as less serious for Republicans while increasing stress over time for Democrats.     

Media exposure,threat processing,and mitigation behaviors in Gulf Coast residents facing the co-occurring threats of COVID-19 and hurricanes

The 2020 hurricane season threatened millions of Americans concurrently grappling with COVID-19. Processes guiding individual-level mitigation for these conceptually distinct threats, one novel and chronic (COVID-19), the other familiar and episodic (hurricanes), are unknown. Theories of health protective behaviors suggest that inputs from external stimuli (e.g., traditional and social media) lead to threat processing, including perceived efficacy (self- and response) and perceived threat (susceptibility and severity), guiding mitigation behavior. We surveyed a representative sample of Florida and Texas residents (N = 1846) between April 14, 2020 and April 27, 2020; many had previous hurricane exposure; all were previously assessed between September 8, 2017 and September 11, 2017. Using preregistered analyses, two generalized structural equation models tested direct and indirect effects of media exposure (traditional media, social media) on self-reported (1) COVID-19 mitigation (handwashing, mask-wearing, social distancing) and (2) hurricane mitigation (preparation behaviors), as mediated through perceived efficacy (self- and response) and perceived threat (susceptibility and severity). Self-efficacy and response efficacy were associated with social distancing (p = .002), handwashing, mask-wearing, and hurricane preparation (ps < 0.001). Perceived susceptibility was positively associated with social distancing (p = 0.017) and hurricane preparation (p < 0.001). Perceived severity was positively associated with social distancing (p < 0.001). Traditional media exhibited indirect effects on COVID-19 mitigation through increased response efficacy (ps < 0.05), and to a lesser extent self-efficacy (p < 0.05), and on hurricane preparation through increased self-efficacy and response efficacy and perceived susceptibility (ps < 0.05). Social media did not exhibit indirect effects on COVID-19 or hurricane mitigation. Communications targeting efficacy and susceptibility may encourage mitigation behavior; research should explore how social media campaigns can more effectively target threat processing, guiding protective actions.     

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method–local linear neuro-fuzzy–adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.     

COVID-19疫情下医疗防护用品市场监管演化博弈与稳定性分析

新冠肺炎(COVID-19)疫情爆发,口罩等医疗防护用品成为硬通货,部分企业出现哄抬物价、制售假货等乱象行为增加了疫情防控工作的实施难度。为了探讨医疗防护用品市场监管动态机制,文章在地方政府—防护用品企业—消费者三方静态演化博弈模型基础上,依次引入动态惩罚、动态惩罚—补贴方案,通过系统动力学仿真分析了不同机制下各演化均衡解的稳定情况,并基于李雅普诺夫稳定性定理验证了演化稳定均衡点。研究表明：完全静态机制下,三个博弈主体的博弈过程无法趋向稳定均衡状态。引入动态惩罚、补贴等手段能够较好抑制博弈主体的行为策略波动,使得防护用具企业诚信经营成为较优选择。同时,提出了一种新型的非线性动态惩罚—补贴机制,在该机制下激励效果最佳。本研究为疫情形势下地方政府设计合理规范医疗防护用品市场方案提供一定思路。     

COVID-19疫情下一种新的地铁平峰运营策略

地铁在城市交通中发挥着重要作用。然而,在新冠肺炎(COVID-19)疫情下,地铁的运营出现了包括消毒、限流及出行独立等多重约束。错峰出行成为了众多城市地铁运营过程中的必然选择。如何既满足居民的基本出行需求,为顺利实现复工复学提供交通支持,又能有效降低乘客感染病毒的风险与追踪密切接触者的成本成为了城市地铁运营的新目标。本文通过对北京地铁运营现状及居民的出行规律分析发现,在新型冠状病毒疫情下,地铁运营过程中存在出行需求与地铁运力不匹配,复工复产与疫情防控,乘客交叉出行数量过多等难题,并针对以上难题提出了分时段复工出行与周末可复工在内的复杂指派模型。这一指派模型不仅实现了城市关键地铁站点平峰人流量、降低疫情传播风险及追踪难度的目标,同时通过模型目标函数与约束条件的灵活修改可实现更为复杂的乘坐地铁复工复产目标。本文所提出的模型在复杂周期性平峰问题中具有较强的推广应用价值。