Developing a Capability Maturity Model for Knowledge Exchange Dynamics Among Stakeholders and Universities: Evidence From Morocco Higher Education Institution

With the advent COVID-19 pandemic, it has been proved that we live in a VUCA world. However, humanity was able to sustain the pandemic through knowledge sharing with their peers. This proves that the organization needs to engage effectively with its stakeholders to maintain itself in the VUCA world. Higher Education Institutions (HEIs) are no exception in such a scenario. However, there are various enablers and inhibitors in knowledge exchange dynamics (KED) in a university setting. So based on this, the study develops an inhibitor-based model for implementing KED in university projects. The inhibitors are identified through a systematic literature review and validated by experts, such as academicians and their stakeholders. Prioritizing these inhibitors allowed the expert to concentrate on the most critical inhibitors using the Orders of Magnitude-Analytic Hierarchy Process. Using the priority weights obtained from the model, a capability maturity model is developed to assess a university's capability and maturity level for a successful KED. From the capability maturity model results, one can understand the specific inhibitors that act as a hindrance to KED and set agenda for improvement for HEIs. IEEE

Deconstruction of COVID-19 transmission network and its epidemic analysis in a suburban village of Shijiazhuang in China

In January 2021, the COVID-19 outbreak in Xiaoguozhuang Village of Shijiazhuang, the first COVID-19 public health emergency in the rural areas of China. Based on the individual trajectory data in 14 days of 941 confirmed cases, taking the transmission network structural analysis and the epidemic transmission dynamics analysis as the methods, the COVID-19 transmission network from the three aspects is deconstructed: epidemic points formation, types of outputs, and regional expansion evolution. Compared with the COVID-19 transmission network of Beijing Xinfadi Market and Dalian Kaiyang Seafood Company, the conclusions are as follows: (1) The numbers of epidemic points and types are large. In the approximate exposure time, new epidemic points will be formed simultaneously with the central city under the background of rapid urbanization. Still, high community activity leads to the formation of co-exposure to epidemic points;short distance "pendulum moves" leading to more extensive individual trajectory density, and finally resulting in the risk of temporary exposure of epidemic points. (2) It has the significant individual-individual contact infection characteristic and output chain relationship characteristic. The secondary outputs of the rural areas are due to the multigenerational family transmission, which is not seen in the urban cities. (3) Compared with the regional expansion of urban cities, the rural areas are manifested by a longer transmission period, caused by the long occult time of outbreaks and the relatively high relative risk of symptomatic confirmed cases in the rural areas. Finally, three suggestions are put forward, enlarging the management space from the terminal areas to adjacent areas around airports, and then implementing delay management on the overflow personnel based on time shift due to carrying the virus from potential epidemic points and buffering isolation area according to the range of risk changes. The deconstruction network of public health emergencies is a beneficial exploration and will provide a basis for improving the resilience of public health networks in rural areas. © 2022, Science Press. All rights reserved.

Comparison of the transmission of COVID -19 between Hebei and Heilongjiang in early 2021.

Objective To compare the diversity of transmission of COVID -19 in Hebei and Heilongjiang Province in early 2021, and to provide theoretical support for the formulation of prevention and control strategies for COVID -19. Methods A dynamical model with staged control strategies was constructed based on the number of existing asymptomatic cases, the number of existing confirmed cases and the cumulative number of removed cases in Hebei and Heilongjiang at the beginning of 2021. Parameters of the model were estimated by the nonlinear least square method. Sensitivity analysis was used to explore the impact of key parameters on the peak number and peak time of existing confirmed cases in the two regions. We respectively analyzed the influence of the change for the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection on the number of existing asymptomatic cases, the number of existing confirmed cases and the number of cumulative cases in the two regions. Results The model fitting results of the two regions were good. Compared the results of Hebei with those of Heilongjiang, there was a larger proportion of asymptomatic infected persons. When the number of initial contacts, the probability of initial contacts, the relative infectivity correction factor of the latent and the composition ratio of the symptomatic infection separately decreased by 10%, the average decrease for the peak number of existing asymptomatic and existing confirmed cases, and the cumulative removed cases in Heilongjiang were more than those in Hebei. Conclusions In early 2021, the transmissions of COVID -19 in Hebei and Heilongjiang were significantly different. In particular, the impact of control measures on the development of the epidemic is different in different areas.Copyright © 2023, Publication Centre of Anhui Medical University. All rights reserved.

Intensity and lag-time of non-pharmaceutical interventions on COVID-19 dynamics in German hospitals.

Introduction: Evaluating the potential effects of non-pharmaceutical interventions on COVID-19 dynamics is challenging and controversially discussed in the literature. The reasons are manifold, and some of them are as follows. First, interventions are strongly correlated, making a specific contribution difficult to disentangle; second, time trends (including SARS-CoV-2 variants, vaccination coverage and seasonality) influence the potential effects; third, interventions influence the different populations and dynamics with a time delay. Methods: In this article, we apply a distributed lag linear model on COVID-19 data from Germany from January 2020 to June 2022 to study intensity and lag time effects on the number of hospital patients and the number of prevalent intensive care patients diagnosed with polymerase chain reaction tests. We further discuss how the findings depend on the complexity of accounting for the seasonal trends. Results and discussion: Our findings show that the first reducing effect of non-pharmaceutical interventions on the number of prevalent intensive care patients before vaccination can be expected not before a time lag of 5 days; the main effect is after a time lag of 10-15 days. In general, we denote that the number of hospital and prevalent intensive care patients decrease with an increase in the overall non-pharmaceutical interventions intensity with a time lag of 9 and 10 days. Finally, we emphasize a clear interpretation of the findings noting that a causal conclusion is challenging due to the lack of a suitable experimental study design.

COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis.

The epidemiology of COVID-19 presented major shifts during the pandemic period. Factors such as the most common symptoms and severity of infection, the circulation of different variants, the preparedness of health services, and control efforts based on pharmaceutical and non-pharmaceutical interventions played important roles in the disease incidence. The constant evolution and changes require the continuous mapping and assessing of epidemiological features based on time-series forecasting. Nonetheless, it is necessary to identify the events, patterns, and actions that were potential factors that affected daily COVID-19 cases. In this work, we analyzed several databases, including information on social mobility, epidemiological reports, and mass population testing, to identify patterns of reported cases and events that may indicate changes in COVID-19 behavior in the city of Araraquara, Brazil. In our analysis, we used a mathematical approach with the fast Fourier transform (FFT) to map possible events and machine learning model approaches such as Seasonal Auto-regressive Integrated Moving Average (ARIMA) and neural networks (NNs) for data interpretation and temporal prospecting. Our results showed a root-mean-square error (RMSE) of about 5 (more precisely, a 4.55 error over 71 cases for 20 March 2021 and a 5.57 error over 106 cases for 3 June 2021). These results demonstrated that FFT is a useful tool for supporting the development of the best prevention and control measures for COVID-19.

Comparison between Deterministic and Stochastic Model for Interaction (COVID-19) with Host Cells in Humans

In this paper, the deterministic and the stochastic models are proposed to study the interaction of the Coronavirus (COVID-19) with host cells inside the human body. In the deterministic model, the value of the basic reproduction number R0 determines the persistence or extinction of the COVID-19. If R0 < 1, one infected cell will transmit the virus to less than one cell, as a result, the person carrying the Coronavirus will get rid of the disease .If R0 > 1, the infected cell will be able to infect all cells that contain ACE receptors. The stochastic model proves that if α1 & α2 are sufficiently large then α1 & α2 maybe give us ultimate disease extinction although R0 > 1, and this facts also proved by computer simulation. © 2022 University of Baghdad. All rights reserved.

Teaching undergraduate physical chemistry lab with kinetic analysis of COVID-19 in the United States.

A physical chemistry lab for undergraduate students described in this report is about applying kinetic models to analyze the spread of COVID-19 in the United States and obtain the reproduction numbers. The susceptible-infectious-recovery (SIR) model and the SIR-vaccinated (SIRV) model are explained to the students and are used to analyze the COVID-19 spread data from U.S. Centers for Disease Control and Prevention (CDC). The basic reproduction number R 0 and the real-time reproduction number R t of COVID-19 are extracted by fitting the data with the models, which explains the spreading kinetics and provides a prediction of the spreading trend in a given state. The procedure outlined here shows the differences between the SIR model and the SIRV model. The SIRV model considers the effect of vaccination which helps explain the later stages of the ongoing pandemic. The predictive power of the models is also shown giving the students some certainty in the predictions they made for the following months.

Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review.

Applied epidemiological models have played a critical role in understanding the transmission and control of disease outbreaks. Their utility and accuracy in decision-making on appropriate responses during public health emergencies is however a factor of their calibration to local data, evidence informing model assumptions, speed of obtaining and communicating their results, ease of understanding and willingness by policymakers to use their insights. We conducted a systematic review of infectious disease models focused on SARS-CoV-2 in Africa to determine: a) spatial and temporal patterns of SARS-CoV-2 modelling in Africa, b) use of local data to calibrate the models and local expertise in modelling activities, and c) key modelling questions and policy insights. We searched PubMed, Embase, Web of Science and MedRxiv databases following the PRISMA guidelines to obtain all SARS-CoV-2 dynamic modelling papers for one or multiple African countries. We extracted data on countries studied, authors and their affiliations, modelling questions addressed, type of models used, use of local data to calibrate the models, and model insights for guiding policy decisions. A total of 74 papers met the inclusion criteria, with nearly two-thirds of these coming from 6% (3) of the African countries. Initial papers were published 2 months after the first cases were reported in Africa, with most papers published after the first wave. More than half of all papers (53, 78%) and (48, 65%) had a first and last author affiliated to an African institution respectively, and only 12% (9) used local data for model calibration. A total of 60% (46) of the papers modelled assessment of control interventions. The transmission rate parameter was found to drive the most uncertainty in the sensitivity analysis for majority of the models. The use of dynamic models to draw policy insights was crucial and therefore there is need to increase modelling capacity in the continent.

Impact of the COVID-19 Pandemic on Medical Devices Market and Medical Services Market of the Eurasian Economic Union Member States (EAEU)

The spread of coronavirus infection has become a trigger mechanism for global economic shocks for almost all industries, especially for the medical sector. The health care system, pharmaceutical industry enterprises, the market of medical services, and manufacturers of medical devices are known to have experienced the greatest burden. Despite all the contradictions, such as control and restrictive measures, disagreements on models of combating coronavirus, restrictions related to the border closure, transport links, etc., the EAEU countries (Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia) have shown an example of timely response and counteraction to the spread of COVID-19 pandemic. At the same time, Russia acted as a guarantor and basis for the economic well-being of the entire Eurasian economic entity. Based on all abovementioned, there is a necessity to consider detailed current changes in the markets of medical devices and medical services in the Eurasian Economic Union countries and, in particular, in the Russian Federation, what is considered to be the object of the research. The latest data on the market of medical devices and services, legislative acts, theoretical and methodological developments in the field of evolutionary and institutional economics were used in the research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Construction and stochastic scale-free modelling of empirical, global, index-case SARS-CoV-2 transmission network

First reported in Wuhan, the novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome 2 (SARS-CoV-2) has astonished health-care systems across the globe due to its rapid and simultaneous spread to the neighbouring and distantly located countries. We constructed the first, global, spatio-temporal, index-case transmission network of SARS-CoV-2 or C19-TraNet consisting of 185 nodes and 196 edges, by manually curating their travel history information that allowed us to map multiple virus invasion routes, both short- as well as long-range, into different geographical locations. To model the growing C19-TraNet, a novel stochastic scale-free (SSF) algorithm is proposed that accounts for stochastic addition of both nodes as well as edges at each time step. C19-TraNet is characterized by a fourth-order polynomial growth of average connectivity having two growth phases, namely, a Chinese and a European wave separated by a stagnation phase that delayed overall growth by 51 days, compared to 1000 corresponding SSF models. Its community structure reveals a heterogeneous grouping of countries, from different WHO regions, suggesting easy invasion of SARS-CoV-2 to susceptible populations through short- as well as long-range transmission. Border control measures initially diminished Chinese wave, however, lack of coordinated actions, multiple transmission routes transported SARS-CoV-2 to remaining countries.

Multivariate data driven prediction of COVID-19 dynamics: Towards new results with temperature, humidity and air quality data.

Since the start of the COVID-19 pandemic many studies investigated the correlation between climate variables such as air quality, humidity and temperature and the lethality of COVID-19 around the world. In this work we investigate the use of climate variables, as additional features to train a data-driven multivariate forecast model to predict the short-term expected number of COVID-19 deaths in Brazilian states and major cities. The main idea is that by adding these climate features as inputs to the training of data-driven models, the predictive performance improves when compared to equivalent single input models. We use a Stacked LSTM as the network architecture for both the multivariate and univariate model. We compare both approaches by training forecast models for the COVID-19 deaths time series of the city of São Paulo. In addition, we present a previous analysis based on grouping K-means on AQI curves. The results produced will allow achieving the application of transfer learning, once a locality is eventually added to the task, regressing out using a model based on the cluster of similarities in the AQI curve. The experiments show that the best multivariate model is more skilled than the best standard data-driven univariate model that we could find, using as evaluation metrics the average fitting error, average forecast error, and the profile of the accumulated deaths for the forecast. These results show that by adding more useful features as input to a multivariate approach could further improve the quality of the prediction models.

Modeling the effects of the contaminated environments on COVID-19 transmission in India.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus that caused an outbreak of typical pneumonia first in Wuhan and then globally. Although researchers focus on the human-to-human transmission of this virus but not much research is done on the dynamics of the virus in the environment and the role humans play by releasing the virus into the environment. In this paper, a novel nonlinear mathematical model of the COVID-19 epidemic is proposed and analyzed under the effects of the environmental virus on the transmission patterns. The model consists of seven population compartments with the inclusion of contaminated environments means there is a chance to get infected by the virus in the environment. We also calculated the threshold quantity R0 to know the disease status and provide conditions that guarantee the local and global asymptotic stability of the equilibria using Volterra-type Lyapunov functions, LaSalle's invariance principle, and the Routh-Hurwitz criterion. Furthermore, the sensitivity analysis is performed for the proposed model that determines the relative importance of the disease transmission parameters. Numerical experiments are performed to illustrate the effectiveness of the obtained theoretical results.

Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis.

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S. and the seven largest European countries to forecast possible pandemic dynamics by investigating the effects of infection vulnerability stratification and measures on preventing the spread of infection. We assume that (i) the number of cases would be underestimated at the beginning of a new virus pandemic due to the lack of effective diagnostic methods and (ii) people more susceptible to infection are more likely to become infected; whereas during the later stages, the chances of infection among others will be reduced, thereby potentially leading to pandemic cessation. Based on infection vulnerability stratification, we demonstrate effects brought by the fraction of infected persons in the population at the start of pandemic deceleration on the cumulative fraction of the infected population. We interestingly show that moderate and long-lasting preventive measures are more effective than more rigid measures, which tend to be eventually loosened or abandoned due to economic losses, delay the peak of infection and fail to reduce the total number of cases. Our calculations relate the pandemic's second wave to high seasonal fluctuations and a low vulnerability stratification coefficient. Our characterisation of basic reproduction dynamics indicates that second wave of the pandemic is likely to first occur in Germany, Spain, France, and Italy, and a second wave is also possible in the U.K. and the U.S. Our findings show that even if the total elimination of the virus is impossible, the total number of infected people can be reduced during the deceleration stage.

Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm.

COVID-19 pandemic is spreading around the world becoming thus a serious concern for health, economic and social systems worldwide. In such situation, predicting as accurately as possible the future dynamics of the virus is a challenging problem for scientists and decision-makers. In this paper, four phenomenological epidemic models as well as Suspected-Infected-Recovered (SIR) model are investigated for predicting the cumulative number of infected cases in Saudi Arabia in addition to the probable end-date of the outbreak. The prediction problem is formulated as an optimization framework and solved using a Particle Swarm Optimization (PSO) algorithm. The Generalized Richards Model (GRM) has been found to be the best one in achieving two objectives: first, fitting the collected data (covering 223 days between March 2nd and October 10, 2020) with the lowest mean absolute percentage error (MAPE = 3.2889%), the highest coefficient of determination (R2 = 0.9953) and the lowest root mean squared error (RMSE = 8827); and second, predicting a probable end date found to be around the end of December 2020 with a projected number of 378,299 at the end of the outbreak. The obtained results may help the decision-makers to take suitable decisions related to the pandemic mitigation and containment and provide clear understanding of the virus dynamics in Saudi Arabia.

Human mobility and COVID-19 initial dynamics.

Countries in Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked mobile network operators to share on a voluntarily basis anonymised and aggregate mobile data to improve the quality of modelling and forecasting for the pandemic at EU level. In fact, mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level, respectively. Using this information together with anonymised and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14-20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating the best data-driven approaches for coming out of confinement and mostly in building future scenarios in case of new outbreaks.

SEIR model for COVID-19 dynamics incorporating the environment and social distancing.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number [Formula: see text]. The model equations are solved numerically using fourth and fifth order Runge-Kutta methods. RESULTS: We found an [Formula: see text] of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus.