Mitigating the "Cobra Effect" when pursuing organizational resilience

This paper is a systematic literature review of organizational resilience. It aims to identify the reasons for the unintended consequences that may occur when organizations pursue resilience and how these unintended consequences could be mitigated. The analysis of 68 articles published between 2017 and 2022 indicates that organizations could have unintended consequences when pursuing organizational resilience, resulting from the organizational resilience conceptualization, models, practices, levels and the paradox of change. Consequently, several unintended consequences may arise when implementing a resilience strategy. This includes lessened leadership effectiveness, the pursuit of unrealistic objectives, low organizational creativity and innovation, overreliance on a single strategy, compromised values, fragile relationships, a short-term focus and organizational culture. Therefore, the overall construct aspects of organizational resilience should be researched and analyzed further by gathering additional empirical data that sheds more light on these issues. Aside from the challenge of defining and measuring organizational resilience, there is variability in how organizational resilience is developed. It has also been operationalized in various ways, with limited insight into empirical methods to identify resilience against future hardships. Although the notion is promising, it has been criticized for being ambiguous and lacking a uniform explanation, diminishing its relevance for practice.

The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate.

The social contact rate has influenced the transmission of COVID-19, with more social contact resulting in more contagion cases. We chose 18 countries with the most confirmed cases in the first 200 days after the Wuhan lockdown. This was the first study using the dynamic social contact rate to simulate the epidemic under diverse restriction policies over 500 days since the COVID-19 outbreak. The developed General Dynamic Model suggested that the probability of contagion ranged from 12.52% to 39.39% in the epidemic. The geometric mean of the social contact rates differed from 18.21% to 96.00% between countries. The restriction policies in developed economies were 3.5 times more efficient than in developing economies. We compare the effectiveness of different policies for disease prevention and discuss the influence of policy adjustment frequency for each country. Maintaining the tightest restriction or alternate tightening and loosening restrictions was recommended, with each having an average 72.45% and 79.78% reduction in maximum active cases, respectively.

Design and modeling of a waiter robot

The science of robotics is considered one of the most practical sciences in all fields. The application of this science is visible in all kinds of work fields and related fields, from construction activities to activities in the fields of medicine or even social services. One of the social services that are very widely used, is delivering items and orders to customers. This work is the duty of people who are called waiters. This job has very few benefits for people working in this field. Also, things like illness can cause some delay in the employer's work or not complete his work in some cases, also in situations such as when contagious diseases have spread, the direct communication of people within a short distance can cause more spread of the disease. The devices ordered by the customers could increase the speed of work and have a low-risk connection, the costs of the employer could be reduced, perfect service could be given to the customers, and the workforce could be employed for more useful work. This robot is specifically designed to use for reception in the conference hall of the growth center of Kharazmi University to receive the people present in this conference hall, but as mentioned above, these robots can be used in other places such as hospitals for delivering medicines to patients, also can be used in restaurants to deliver customer's orders to them. With this replacement, the speed of catering increases, at the same time, there is no lack of accuracy, and the issue that becomes more important with the spread of the contagious disease Covid-19 is hygiene, which can achieve several important goals in this field with this replacement. Specifically, during the reception, the distance between the host and the guest is less than one meter and is unsafe. Also, there is a possibility that each of the parties is a carrier of contagious diseases, and these problems are solved by this replacement. © 2022 IEEE.

Output and Energy Prices Fluctuations in Response to Market Shocks: System Dynamic Modeling

Regardless of the fact, whether governments of particular country implemented the strong lockdown measures to prevent the spread of COVID-19 or not, the economies of each country all over the word have been suffered considerably due to the shocks caused by the pandemic. We observed slowdown of economic activity, macroeconomic instability and shifts in consumption preferences supplemented by rising unemployment as well as significant fluctuation of demand and production capability. The research problem addressed in this paper focuses on dynamic properties of output and inflation fluctuations that occur in response to economic shocks different magnitudes and types. We use a system dynamic approach and constructs two system dynamic models to examine the dynamics of output, prices, wage and inflation. The paper indicates ranges of relevant parameters' values that correspond with sensitivity of output to demand and production capability changes related to possibility of reaching new equilibrium point. To explore the variety of prices and wage behavior in response to shocks we evaluate distinguish possible phase diagrams associated with stable node, stable focus, circle, unstable focus and unstable node. The results is a contribution to discussion of the policy issues related to mitigation of recession caused by unpredictable and strong shocks.

Educational management from the integrated input-output methodology of the dynamic model

The purpose is to analyze management in educational organizations through the integrated input-output methodology of the dynamic model with emerging technologies, in the institutions "Camilo Torres Restrepo, San José and San Isidro”, municipality of Curumaní (Cesar-Colombia). The research was of an analytical type;supported by the qualitative macro-ethnographic approach. According to the epistemological framework, the research is considered phenomenological. The technique used was the in-depth interview, applying the interview script directed to nine (9) key informant teachers of the mentioned institutions. In basic secondary education, the integrated input-output methodology of the dynamic model using emerging technologies does not constitute a teacher's priority to respond to learning mediated by virtuality during Covid-19. Finally, it is necessary for classroom managers to effectively apply the theoretical-practical elements related to the integrated input-output methodology in order to assertively guide students in their learning process. © 2023, Universidad del Zulia. All rights reserved.

Asymmetric volatility connectedness between cryptocurrencies and energy: Dynamics and determinants

We explore the dynamics and determinants of volatility connectedness between cryptocurrencies and energy. We employed a block dynamic equicorrelation model and a group volatility connectedness measurement to measure the cross-equicorrelation and volatility connectedness between cryptocurrencies and energy. We also adopted dynamic model averaging to identify the time-varying drivers. The results suggest that changes in cross-equicorrelation between the two groups were affected by influential global events and increased after the COVID-19 pandemic. Volatilities were transmitted in both directions between cryptocurrencies and energy, but the transmission from energy to cryptocurrencies is by far the strongest. The driver identification implies that the factors related to cryptocurrencies and global financial markets had important roles in explaining the volatility connectedness from cryptocurrencies to energy in some periods after the COVID-19 pandemic, but the effects were marginal. In contrast, factors such as electricity consumption, cryptocurrency turnovers, and VIX were important in affecting the volatility connectedness from energy to cryptocurrencies, and the effects depended on factors and changed over time. Copyright © 2023 Wan, Song, Zhang and Yin.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including > 40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 hours. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3,706 patients (2,587 in the development and 1,119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79 - 0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors. This article is protected by copyright. All rights reserved.

Modelling the impact of the Omicron BA.5 subvariant in New Zealand.

New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.

Impact of combination preventative interventions on hospitalization and death under the pandemic of SARS-CoV-2 Omicron variant in China.

With a large population most susceptible to Omicron and emerging SARS-CoV-2 variants, China faces uncertain scenarios if reopening its border. Thus, we aimed to predict the impact of combination preventative interventions on hospitalization and death. An age-stratified susceptible-infectious-quarantined-hospitalized-removed-susceptible (SIQHRS) model based on the new guidelines of COVID-19 diagnosis and treatment (the ninth edition) was constructed to simulate the transmission dynamics of Omicron within 365 days. At baseline, we assumed no interventions other than 60% booster vaccination in individuals aged <=60 years and 80% in individuals aged >60 years, quarantine and hospitalization. Oral antiviral medications for COVID-19 (e.g. BRII-196/BRII-198) and non-pharmaceutical interventions (NPIs) such as social distancing and antigen self-testing were considered in subsequent scenarios. Sensitivity analyses were conducted to reflect different levels of interventions. A total of 0.73 billion cumulative quarantines (95% CI 0.53-0.83), 33.59 million hospitalizations (22.41-39.31), and 0.62 million deaths (0.40-0.75) are expected in 365 days. The case fatality rate with pneumonia symptoms (moderate, severe and critical illness) is expected to be 1.83% (1.68-1.99%) and the infected fatality rate 0.38‰ (0.33-0.42‰). The highest existing hospitalization and ICU occupations are 3.11 (0.30-3.85) and 20.33 (2.01-25.20) times of capacity, respectively. Sensitivity analysis showed that interventions can be adjusted to meet certain conditions to reduce the total number of infections and deaths. In conclusion, after sufficient respiratory and ICU beds are prepared and the relaxed NPIs are in place, the SARS-CoV-2 Omicron variant would not seriously impact the health system. This article is protected by copyright. All rights reserved.

Spatio-temporal model to investigate COVID-19 spread accounting for the mobility amongst municipalities.

The rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections.

Effective Analysis and Investigation in COVID-19 Prevention Policies Based on Population Dynamics Modeling and Simulation

The paper proposes a population dynamics model to simulate the COVID-19 pandemic and analyze the effectiveness of prevention policies in the early stage. The model is designed to aid the decision-making process of policy-making in the early stage. The model is formulated based on the SEIR model to simulate the spread of COVID19 from human to human. By implementing the data in the U.S., the model is first fitted to the data first. Then, the model simulates the number of infected people with the change of time under different levels of social distancing and mask-wearing. © 2022 IEEE.

Mathematical Models of Epidemic Dynamics to Simulate the Distribution of COVID-19

Mathematical modelling of the COVID-19 epidemic is based on system dynamics and SIR models, which are not considered adequate. To overcome the shortcomings of modelling, a non-classical discipline, epidemic dynamics, is proposed. The epidemic should be viewed as an open, self-replicating dynamic system in epidemic dynamics. Epidemic dynamics models are based on a dynamic system model with an extended network of inverse relationship. This non-classical approach allows the tools of non-linear and non-equilibrium dynamics to be used and models of epidemic dynamics to be represented in the form of non-linear and non-stationary differential equations. The solutions of the equations are special COVID-19 distribution functions – functions of the flows and accumulation levels of the infected and the dead. The COVID-19 distribution functions show high accuracy in approximating the statistics, demonstrating the excellent adequacy of these functions in principle. The application of COVID-19 distribution functions makes it possible to quantitatively describe the basic concepts of an epidemic to carry out comparative parametric analysis of the distribution of diseases and predict the development of an epidemic. © 2022 Copyright for this paper by its authors.

Modeling and Quantifying the Role of User Active Search in COVID-19 Multi-Information Analysis and Prediction in the Chinese Sina-microblog

The study of information spreading is important and necessary, especially during the Coronavirus, when plenty of opinions aroused in the Chinese Sina-microblog. On the basis of previous studies, we propose a comprehensive susceptible-reading-forwarding-immune (SRFI) model with considering user active search. We establish differential equations introducing average active reading rate to describe the multi-information propagation process. By using typical event about COVID-19 during the outbreak of public opinion to carry out the numerical fitting experiment to estimate model parameters, fit real data, and analyze the calculated information transmission indexes, we verify the validity of the model. We analyze the sensitivity of multiple parameters to multi-information transmission index based on reading and forwarding and the effect of average active reading rate to show the influence of the new parameter on multi-information transmission. In addition, to compare the predictive ability of the previous model with our new model, we use the early prediction method. Result shows that our new model can forecast the process of multi-information transmission faster and more exactly. The conclusions above indicate that the role of user active search is not negligible and the I-SRFI model can help us design effective communication strategies for rapid implementation of public health interventions. © 2022 IEEE.

Dynamic Modeling of Professional Mobility in the Context of Labor Market Precarization

The pandemic has become a catalyst for the inevitable process of digitalization of communications, significantly changing the organization and technology of professional activity around the world. There has been a radical change in labor market trends due to the digital transformation of the economy, changes in the unemployment rate, the transition of professional groups to the remote work format due to the external need for isolation to minimize the spread of Covid-19. The uncertainty of the labor market development conditions, as well as changes in the unemployment rate among the young subset of employable population, cause the emergence of various forms of unstable employment. The presence of high social risks, the need to develop mechanisms to increase the level of protection of the population, ensuring the growth of youth welfare and the formation of an energy saving policy aimed, inter alia, at the innovative development of the labor economy, determine the relevance of developing a set of models for predicting precarization of the labor market. To describe the dynamics of the labor market development, we constructed a model for assessing the risks for young professionals from key sectors of the economy who enter the precariat during the pandemic. Based on the provisions of the theory of positional games and behavioral economics, we have developed a multifactorial dynamic model for predicting professional mobility and precarization of the labor market, considering the fulfillment of Nash equilibrium conditions. The model allows you to track and predict the professional mobility of qualified youth depending on the intensity of the labor market, wage levels and the degree of digital maturity of companies in the region. Copyright © 2022 The Authors.

What is the role of aerosol transmission in SARS-Cov-2 Omicron spread in Shanghai?

The Omicron transmission has infected nearly 600,000 people in Shanghai from March 26 to May 31, 2022. Combined with different control measures taken by the government in different periods, a dynamic model was constructed to investigate the impact of medical resources, shelter hospitals and aerosol transmission generated by clustered nucleic acid testing on the spread of Omicron. The parameters of the model were estimated by least square method and MCMC method, and the accuracy of the model was verified by the cumulative number of asymptomatic infected persons and confirmed cases in Shanghai from March 26 to May 31, 2022. The result of numerical simulation demonstrated that the aerosol transmission figured prominently in the transmission of Omicron in Shanghai from March 28 to April 30. Without aerosol transmission, the number of asymptomatic subjects and symptomatic cases would be reduced to 130,000 and 11,730 by May 31, respectively. Without the expansion of shelter hospitals in the second phase, the final size of asymptomatic subjects and symptomatic cases might reach 23.2 million and 4.88 million by May 31, respectively. Our results also revealed that expanded vaccination played a vital role in controlling the spread of Omicron. However, even if the vaccination rate were 100%, the transmission of Omicron should not be completely blocked. Therefore, other control measures should be taken to curb the spread of Omicron, such as widespread antiviral therapies, enhanced testing and strict tracking quarantine measures. This perspective could be utilized as a reference for the transmission and prevention of Omicron in other large cities with a population of 10 million like Shanghai.

Estimating the Effectiveness of Shielding during Pregnancy against SARS-CoV-2 in New York City during the First Year of the COVID-19 Pandemic.

Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population.

E-commerce supply chain network design using on-demand warehousing system under uncertainty

During the COVID-19 pandemic, e-commerce retailers have had trouble satisfying the growing demand because of limited warehouse capacity constraints. Fortunately, an on-demand warehousing system has emerged as a new alternative to mitigate warehouse capacity issues. In recent years, several studies have focused on the supply chain problem considering on-demand warehousing. However, there is no study that deals simultaneously with inherent uncertainties and the property of commitment, which is the main advantage of on-demand warehousing. To fill these research gaps, this paper presents an e-commerce supply chain network design problem considering an on-demand warehousing and decisions for commitment periods. We propose the two-stage stochastic programming model that captures the inherent uncertainties to formulate the presented problem. We solve the proposed model utilizing sample average approximation combined with the Benders decomposition algorithm. Of particular note, we develop a method to generate effective initial cuts for improving the convergence speed of the Benders decomposition algorithm. Computational results show that the developed method could find an effective feasible solution within a reasonable computational time for problems of practical size. Furthermore, we show the significant cost-saving effects, based on experiment results, that occur when an on-demand warehousing system is used for designing supply chain networks.

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study.

BACKGROUND: Timely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients. METHODS: We retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 'wave' in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values. RESULTS: We included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80-0.84]) compared to the National early warning score (0.72 [0.69-0.74]) and the Modified early warning score (0.67 [0.65-0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [- 0.09 to 0.14], slope = 0.79 [0.73-0.86]). CONCLUSIONS: This study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.

Spatiotemporal pattern recognition and dynamical analysis of COVID-19 in Shanghai, China.

Shanghai suffered a large outbreak of Omicron mutant of COVID-19 at the beginning of March 2022. To figure out the spatiotemporal patterns of the epidemic, a retrospective statistical investigation, coupled with a dynamic model, is implemented in this study. The hotspots of SARS-CoV-2 transmissions are identified, and strong aggregative effects in the decay stage are found. Besides, the visualization of disease diffusion is provided to show how COVID-19 disease invades all districts of Shanghai in the early stage. Furthermore, the calculations from the dynamic model manifest the effect of detections to suppress the epidemic dissemination. These results reveal the strategies to improve the spatial control of disease.

Virtual Training System for Unmanned Aerial Vehicle Control Teaching–Learning Processes

The present work is focused on the development of a Virtual Environment as a test system for new advanced control algorithms for an Unmanned Aerial Vehicles. The virtualized environment allows us to visualize the behavior of the UAV by including the mathematical model of it. The mathematical structure of the kinematic and dynamic models is represented in a matrix form in order to be used in different control algorithms proposals. For the dynamic model, the constants are obtained experimentally, using a DJI Matrice 600 Pro UAV. All of this is conducted with the purpose of using the virtualized environment in educational processes in which, due to the excessive cost of the materials, it is not possible to acquire physical equipment;moreover, is it desired to avoid damaging them. Finally, the stability and robustness of the proposed controllers are determined to ensure analytically the compliance with the control criteria and its correct operation.