Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

An Epidemiological Model for Tuberculosis Considering Environmental Transmission and Reinfection

As tuberculosis (TB) patients do not have lifetime immunity, environmental transmission is one of the key reasons why TB has not been entirely eradicated. In this study, an SVEIRB model of recurrent TB considering environmental transmission was developed to explore the transmission kinetics of recurrent TB in the setting of environmental transmission, exogenous infection, and prophylaxis. A more thorough explanation of the effect of environmental transmission on recurrent TB can be found in the model's underlying regeneration numbers. The global stability of disease-free and local equilibrium points can be discussed by looking at the relevant characteristic equations. The Lyapunov functions and the LaSalle invariance principle are used to show that the local equilibrium point is globally stable, and TB will persist if the basic reproduction number is larger. Conversely, the disease will disappear if the basic reproduction number is less than one. The impact of environmental transmission on the spread of tuberculosis was further demonstrated by numerical simulations, which also demonstrated that vaccination and reducing the presence of the virus in the environment are both efficient approaches to control the disease's spread.

Measuring Dependence in a Set of Asset Returns

An index measuring the degree of dependence in a set of asset returns is defined as the ratio of an equivalent number of independent assets to the number of assets. The equivalence is based on either attaining the same optimized value enhancement or spread reduction. The value enhancement is the difference in value of a value maximizing portfolio and the maximum value delivered by the components. The spread reduction is the percentage reduction attained by a spread minimizing portfolio relative to the smallest spread for the components. Asset values or bid and ask prices of portfolios, are modeled by conservative valuation operators from the theory of two price economies. The dependence indices fall with the number of assets in the portfolio and they are explained by a measure of concentration applied to normalized eigenvalues of the correlation matrix along with the average level of correlation, the level of the (Rudin and Morgan, 2006) portfolio diversification index and the number of assets in the portfolio. A time series of the indices constructed on the basis of the S&P 500 index and the nine sector ETF's reveals a collapse during the financial crisis with no recovery until 2016, with a peak in February 2020 and a COVID crash in March of 2020. Furthermore, factor dependence benefits are richer than those found in equity indices. Dependence benefits across global indices are not as strong as dependence benefits across an equal number of domestic assets, but they rise substantially for longer horizons of up to three years.

Barriers to integrating lean construction and integrated project delivery (IPD) on construction megaprojects towards the global integrated delivery (GID) in multinational organisations: lean IPD&GID transformative initiatives

PurposeThe architecture, engineering and construction (AEC) industry encounter substantial risks and challenges in its evolution towards sustainable development. International businesses, multinational AEC organisations, technical professionals, project and portfolio management organisations face global connectivity challenges between business units, especially during the outbreak of novel coronavirus pandemic, to manage construction megaprojects (CMPs). That raises the need to manage global connectivity as a main strategic goal of global organisations. This paper aims to investigate barriers to integrating lean construction (LC) practices and integrated project delivery (IPD) on CMPs towards the global integrated delivery (GID) transformative initiatives and develop future of work (FOW) global initiatives in contemporary multinational AEC organisations.Design/methodology/approachA two-stage quantitative and qualitative research approach is adopted. The qualitative research methodology consists of a literature review to appraise barriers to integrating LeanIPD&GID on CMPs. Barriers are arranged into six-factor clusters (FCs), with a conceptualisation of LeanIPD&GID, GID strategy placements and FOW global initiatives with multiple validations. This analysis also involved semi-structured interviews and focus group techniques. Stage two consisted of an empirical questionnaire survey that shaped the foundation of analysis and findings of 230 respondents from 23 countries with extensive cosmopolitan experience in the construction of megaprojects. The survey examined a set of 28 barriers to integrating LeanIPD&GID on CMPs resulting from a detailed analysis of extant literature after validation. Descriptive and inferential statistical tests were exploited for data analysis, percentage scoring analysis, principal component analysis (PCA) and eigenvalues were used to elaborate on clustered factors.FindingsThe research conceptualised LeanIPD&GID principles and proposed GID strategy placements for LeanIPD&GID transformative initiatives and FOW global initiatives. It concluded that the most significant barriers to integration of LeanIPD&GID on CMPs are "lack of mandatory building information modelling (BIM) and LC industry standards and regulations by governments”, "lack of involvement and support of governments”, "high costs of BIM software licenses”, "resistance of industry to change from traditional working practices” and "high initial investment in staff training costs of BIM”. PCA revealed the most significant FCs are "education and knowledge-related barriers”, "project objectives-related barriers” and "attitude-related barriers”. Awareness of BIM in the Middle East and North Africa (MENA) region is higher than LC and LC awareness is higher than IPD knowledge. Whilst BIM adoption in the MENA region is higher than LC;the second is still taking its first steps, whilst IPD has little implementation. LeanBIM is slightly integrated, whilst LeanIPD integration is almost not present.Originality/valueThe research findings, conclusion and recommendation and proposed GID strategy placements for LeanIPD&GID transformative initiatives to integrating LeanIPD&GID on CMPs. This will allow project key stakeholders to place emphasis on tackling LeanIPD&GID barriers identified in this research and commence GID strategies. The study has provided effective practical strategies for enhancing the integration of LeanIPD&GID transformative initiatives on CMPs.

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

A fundamental issue in robotics is the precise localization of mobile robots in uncertain environments. Due to changing environmental patterns and lighting, localization under difficult perceptual conditions remains problematic. This paper presents an approach for locating an outdoor mobile robot using deep learning algorithms merge with 3D Light Detection and Ranging LiDAR data and RGB-D image. This approach is divided into three levels. The first is the training level, which involves scanning the localization area with a 3D LiDAR sensor and then converting the data into a 2.5D image based on the Principal Component Analysis. The testing is the second level in the Intensity Hue Saturation process. Then, the RGB and Depth images are combined to create a 2.5D fusion image. These datasets are trained and tested using Convolution Neural Networks. The K-Nearest Neighbor algorithm is used in the third level is the classification. The results show that the proposed approach is better in terms of accuracy of 97.46% and the Mean error distance is 0.6 meters.

Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation.

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed. Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion. The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease. In the model, the three parameters, i. e. protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease. Raising people' s awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. (English) [ FROM AUTHOR] 基于 SEIR 模型, 引入自我防护和隔离两个仓室, 提出一个更加通用的传染病传播模型. 通过对 模型进行定性分析, 计算模型的基本再生数, 通过特征值理论和 Routh - Hurwitz 判据, 分析模型的无病平衡 点和地方病平衡点的局部渐近稳定性. 数值模拟和 COVID - 19 病毒真实数据拟合结果表明, 所提出的 SEIQRP 模型能够有效地描述传染病的动态传播过程. 模型中防护率、潜伏期隔离率和感染者隔离率这三个 参数对疾病的传播起着非常关键的作用. 提高人们加强自我防护意识、重点排查潜伏期患者和对感染者进行 隔离治疗可以有效降低传染病的传播. (Chinese) [ FROM AUTHOR] Copyright of Journal of Northeastern University (Natural Science) is the property of Dongbei Daxue Xuebao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Scalar Measures of Volatility and Dependence for the Multivariate Models with Applications to Asian Financial Markets

The variance–covariance matrix is a multi-dimensional array of numbers, containing information about the individual variabilities and the pairwise linear dependence of a set of variables. However, the matrix itself is difficult to represent in a concise way, particularly in the context of multivariate autoregressive conditional heteroskedastic models. The common practice is to report the plots of k(k−1)/2 time-varying pairwise conditional covariances, where k is the number of markets (or assets) considered;thus, when k=10, there will be 45 graphs. We suggest a scalar measure of overall variabilities (and dependences) by summarizing all the elements in a variance–covariance matrix into a single quantity. The determinant of the covariance matrix Σ, called the generalized variance, can be used as a measure of overall spread of the multivariate distribution. Similarly, the positive square root of the determinant ;R;of the correlation matrix, called the scatter coefficient, will be a measure of linear independence among the random variables, while collective correlation+(1−;R;)1/2 will be an overall measure of linear dependence. In an empirical application to the six Asian market returns, these statistics perform the intended roles successfully. In addition, these are shown to be able to reveal and explain the empirical facts that cannot be uncovered by the traditional methods. In particular, we show that both the contagion and interdependence (among the national equity markets) are present and could be quantitatively measured in contrast to previous studies, which revealed only market interdependence.

Research on Chinese Stock Market during COVID-19—Based on Random Matrix Theory

This paper focuses on the three industries that are greatly impacted by COVID-19, including the consumption industry, the pharmaceutical industry, and the financial industry. The daily returns of 98 stocks in the consumption industry, the pharmaceutical industry, and the financial industry in the 100 trading days from January 2, 2020, to June 3, 2020, are selected. Based on the random matrix theory, it first analyzes whether the stock market conforms to the efficient market hypothesis during the epidemic period, and second it further studies the linkage between the three industries. The results show that (1) the correlation coefficient is approximately a normal distribution, but the mean value is greater than 0, which is greater than that of the more mature markets such as the United States. (2) There are three eigenvalues greater than the prediction value, of which the maximum eigenvalue is about 11.18 times larger than the largest eigenvalue of the RMT. (3) There is a significant positive relationship between the maximum eigenvalue and the correlation coefficient. The specific market performance is that the stock price fluctuations show a high degree of consistency. (4) In the sample interval, the financial industry has a restraining effect on the consumption industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the consumption industry in the short term. The consumption industry has a promoting effect on the financial industry in the short term, and the pharmaceutical industry has a promoting and then restraining effect on the financial industry in the short term. The consumption industry has a promoting and then restraining effect on the pharmaceutical industry in the short term, and the financial industry has a promoting and then restraining effect on the pharmaceutical industry in the short term. (5) In the sample interval, the consumption industry is mainly affected by itself, while the role of the pharmaceutical industry and the financial industry is very small. The pharmaceutical industry is mainly affected by itself and the consumption industry, while the role of the financial industry is very small. The financial industry is mainly affected by itself and the consumption industry, while the role of the pharmaceutical industry is very small. This situation has consequences for individual investors and institutional investors, since some stock returns can be expected, creating opportunities for arbitrage and for abnormal returns, contrary to the assumptions of random walk and information efficiency. The research on the correlation between asset returns will help to accurately price assets and avoid losses caused by price fluctuations during the epidemic.

Correlation and Causation in the Perspective of COVID-19: An Empirical Study on Canonical Correlation Analysis

COVID-19 has an immense effect on the Globe, crossing 53,86,95,729 affected in more than 220 nations, with 63,18,093 individuals deceased. Various countries released COVID-19 protocols to enclose its spread to control the pandemic. This research article illustrates the Effect of COVID-19 on aged people (age>50), diabetes individuals, and individuals with smoking habits concerning the cause of death. An attempt has been made to identify the predominant variables for the cause of death due to COVID-19. IBM SPSS statistical tool enabled by Canonical Correlation Analysis (CCA) is used for simulation. Data were gathered from the Kaggle, an open repository for 2020. Based on the results obtained, predictions regarding the Cause and Effect of COVID-19 are discussed. © 2022 IEEE.

A Triangle Framework among Subgraph Isomorphism, Pharmacophore and Structure-function Relationship

Coronavirus disease 2019 (COVID-19) has gained utmost attention in the current time from academic research and industrial practices because it continues to rage in many countries. Pharmacophore models exploit molecule topological similarity as well as functional compound similarity so that they can be reliable via the application of the concept of bioisosterism. In this work, we analyze the targets for coronavirus protein and the structure of RNA virus variation, thereby complete the safety and pharmacodynamic action evaluation of small-molecule anti-coronavirus oral drugs. Common pharmacophore identifications could be converted into subgraph querying problems, due to chemical structures can also be converted to graphs, which is a knotty problem pressing for a solution. We adopt simplified representation pharmacophore graphs by reducing complete molecular structures to s to detect isomorphic topological patterns and further to improve the substructure retrieval efficiency. Our threefold architecture subgraph isomorphism-based method retrieves query subgraphs over large graphs. First, by means of extracting a sequence of subgraphs to be matched and then comparing the number of vertex and edge between the potential isomorphic subgraphs and the query graph, we lower the computational scaling markedly. Afterwards, the directed vertex and edge matrix recording vertex and edge positional relation, directional relation and distance relation has been created. Then, on the basis of permutation theorem, we calculate the row sum of vertex and edge adjacency matrix of query graph and potential sample. Finally, according to equinumerosity theorem, we check the eigenvalues of the vertex and edge adjacency matrices of the two graphs are equinumerous. The topological distance could be calculated based on the graph isomorphism and the subgraph isomorphism can be implemented after the combination of the subgraph. The proposed quantitative structure-function relationships (QSFR) approach can be effectively applied for pharmacophoric patterns identification. The framework of new drug development for covid-19 has been established based on this triangle. © 2022 ACM.

The analysis of cross-correlation between Istanbul Stock Exchange and major stock markets and indices: An empirical analysis using Random Matrix Theory

This study attempts to investigate the cross-correlation between stocks listed under the XU100 index of Borsa Istanbul with several ratios and indices of the stock markets worldwide by using the Random Matrix Theory approach through a correlation matrix. In addition, Eigenvector Analysis, Network Analysis, Dimension Reduction will be carried out to investigate cross-correlation between markets. It was found that XU100, which is an index that includes 100 stocks highest in volume, has a distinguishing behavior compared to other indices and rates in terms of eigenvalue and related eigenvector structures. Furthermore, mean-value portfolio analysis showed that the empirical correlation matrix underestimates the portfolio risks than the correlation matrix obtained by filtering the noise. Coronavirus pandemic also affected Borsa Istanbul by breaking periodic behavior of volatility and correlation cycle. © 2022 John Wiley & Sons, Ltd.

Does telework work? Gauging challenges of telecommuting to adapt to a “new normal”

The paper aims to contribute to a deeper understanding of organisation management while telecommuting. With exploratory factor analysis (EFA), we define the specific set of telework organising efficiency characteristics. We determined the number of factors with Kaiser Eigenvalues rule as well as Cattel's scree criterion. We conducted the study in Lithuania, the country with a low percentage of teleworkers until organisations have been urged to properly implement their performance to remote means after the COVID-19 quarantine was announced. This paper reveals that the fundamental challenges of teleworking are the feedback issues related to working accomplishment, especially to the task and process overload, and individual self-organisation ability. Moreover, the flexibility of work organisation represents a unique characteristic of telework, and managers should cooperate more effectively with teleworkers to keep them motivated.

Bifurcation from an Epidemic Model in the Presence of Memory Effects

In this paper, we consider a fractional SIS epidemic system with logistic growth demographic and saturated incidence rate for susceptibles. First, we validate our model by proving the global existence, positivity as well as boundedness of solutions. Then, we give necessary and sufficient conditions for the extinction and persistence of the disease from the population. We also study the local asymptotic stability of the unique positive equilibrium point by analyzing the corresponding characteristic equation. We find that combining logistic growth and saturated incidence for susceptibles can lead the system dynamic behavior to exhibit stability switches. By choosing the growth rate and the carrying capacity of the population as the bifurcation parameters, the stability of the positive equilibrium and the existence of Hopf bifurcation are investigated. Finally, numerical simulations are performed to verify the theoretical results, to fit real-time data from 10 June to 25 November of 2020 and also to predict the number of cumulative cases for COVID-19 in Morocco during 2021.

Theoretical Analysis of the SIRVVD Model for Insights into the Target Rate of COVID-19/SARS-CoV-2 Vaccination in Japan

The effectiveness of the first dose of vaccination for COVID-19 is different from that of the second dose;therefore, in several studies, various mathematical models that can represent the states of the first and second vaccination doses have been developed. Using the results of these studies and considering the effects of the first and second vaccination doses, we can simulate the spread of infectious diseases. The susceptible-infected-recovered-vaccination1-vaccination2-death (SIRVVD) model is one of the proposed mathematical models;however, it has not been sufficiently theoretically analyzed. Therefore, we obtained an analytical expression for the number of infected persons by considering the numbers of susceptible and vaccinated persons as parameters. We used the solution to determine the target vaccination rate for decreasing the infection numbers of the COVID-19 Delta variant (B.1.617) in Japan. Furthermore, we investigated the target vaccination rates for cases with strong or weak variants by comparing with the COVID-19 Delta variant (B.1.617). This study contributes to the mathematical development of the SIRVVD model and provides insights into the target vaccination rate for decreasing the number of infections. Author

Reconstruction of Time-varying Graph Signals via Sobolev Smoothness

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals. IEEE

Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition

With the emergence of the global epidemic of COVID-19, face recognition systems have achieved much attention as contactless identity verification methods. However, covering a considerable part of the face by the mask poses severe challenges for conventional face recognition systems. This paper proposes an automated Masked Face Recognition (MFR) system based on the combination of a mask occlusion discarding technique and a deep-learning model. Initially, a pre-processing step is carried out in which the images pass three filters. Then, a Convolutional Neural Network (CNN) model is proposed to extract the features from unoccluded regions of the faces (i.e., eyes and forehead). These feature maps are employed to obtain covariance-based features. Two extra layers, i.e., Bitmap and Eigenvalue, are designed to reduce the dimension and concatenate these covariance feature matrices. The deep covariance features are quantized to codebooks combined based on Bag-of-Features (BoF) paradigm. Finally, a global histogram is created based on these codebooks and utilized for training an SVM classifier. The proposed method is trained and evaluated on Real-World-Masked-Face-Recognition-Dataset (RMFRD) and Simulated-Masked-Face-Recognition-Dataset (SMFRD) achieves an accuracy of 95.07% and 92.32 %, respectively, showing its competitive performance compared to the state-of-the-art. Experimental results prove that our system has high robustness against noisy data and illumination variations. © 2021 IEEE.

Effect of Intervention of Vaccination and Treatment on the Transmission Dynamics of HBV Disease: A Mathematical Model Analysis

Hepatitis B is a globally infectious disease. It is pretty contagious and can be transmitted by blood or bodily fluids, through things like sharing razors and toothbrushes. It has been called the silent killer because it is asymptomatic, one might have the virus but not know until it manifests itself until much later. Since people do not give attention, it will develop into cirrhosis and hepatocellular carcinoma that leads to liver transplantation and death. This nature of HBV disease motivated us to perform this work. Mathematical modeling of HBV transmission is an interesting research area. In this paper, we present characteristics of HBV virus transmission in the form of a mathematical model. We proposed and analyzed a compartmental nonlinear deterministic mathematical model SEACTR for transmission dynamics and control of hepatitis B virus disease. In this model, we used force infection which takes the contact rate of susceptible population and transmission probability into account. We proved that the solution of the considered dynamical system is positive and bounded. The model is studied qualitatively using the stability theory of differential equations and the effective reproductive number which represents the epidemic indicator is obtained from the largest eigenvalue of the next-generation matrix. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined. The sensitivity index shows that the transfer rate from exposed class to acute infective class and transfer rate from exposed class to chronic infective class are the most dominant parameters contributing to the transmission of HBV. On the one hand, the vaccination rate and treatment rate are the parameters that suppress the transmission of the disease the most, and enhancing the vaccination rate for newborns and treatment for chronically infected individuals is very effective to stop the transmission of HBV. The combined efforts of vaccination, effective treatment, and interruption of transmission make elimination of the infection plausible and may eventually lead to the eradication of the virus.

Forecasting the COVID-19 Pandemic in Saudi Arabia Using a Modified Singular Spectrum Analysis Approach: Model Development and Data Analysis.

BACKGROUND: Infectious disease is one of the main issues that threatens human health worldwide. The 2019 outbreak of the new coronavirus SARS-CoV-2, which causes the disease COVID-19, has become a serious global pandemic. Many attempts have been made to forecast the spread of the disease using various methods, including time series models. Among the attempts to model the pandemic, to the best of our knowledge, no studies have used the singular spectrum analysis (SSA) technique to forecast confirmed cases. OBJECTIVE: The primary objective of this paper is to construct a reliable, robust, and interpretable model for describing, decomposing, and forecasting the number of confirmed cases of COVID-19 and predicting the peak of the pandemic in Saudi Arabia. METHODS: A modified singular spectrum analysis (SSA) approach was applied for the analysis of the COVID-19 pandemic in Saudi Arabia. We proposed this approach and developed it in our previous studies regarding the separability and grouping steps in SSA, which play important roles in reconstruction and forecasting. The modified SSA approach mainly enables us to identify the number of interpretable components required for separability, signal extraction, and noise reduction. The approach was examined using different levels of simulated and real data with different structures and signal-to-noise ratios. In this study, we examined the capability of the approach to analyze COVID-19 data. We then used vector SSA to predict new data points and the peak of the pandemic in Saudi Arabia. RESULTS: In the first stage, the confirmed daily cases on the first 42 days (March 02 to April 12, 2020) were used and analyzed to identify the value of the number of required eigenvalues (r) for separability between noise and signal. After obtaining the value of r, which was 2, and extracting the signals, vector SSA was used to predict and determine the pandemic peak. In the second stage, we updated the data and included 81 daily case values. We used the same window length and number of eigenvalues for reconstruction and forecasting of the points 90 days ahead. The results of both forecasting scenarios indicated that the peak would occur around the end of May or June 2020 and that the crisis would end between the end of June and the middle of August 2020, with a total number of infected people of approximately 330,000. CONCLUSIONS: Our results confirm the impressive performance of modified SSA in analyzing COVID-19 data and selecting the value of r for identifying the signal subspace from a noisy time series and then making a reliable prediction of daily confirmed cases using the vector SSA method.

SEIR order parameters and eigenvectors of the three stages of completed COVID-19 epidemics: with an illustration for Thailand January to May 2020.

By end of October 2020, the COVID-19 pandemic has taken a tragic toll of 1150 000 lives and this number is expected to increase. Despite the pandemic is raging in most parts of the world, in a few countries COVID-19 epidemics subsided due to successful implementations of intervention measures. A unifying perspective of the beginnings, middle stages, and endings of such completed COVID-19 epidemics is developed based on the order parameter and eigenvalue concepts of nonlinear physics, in general, and synergetics, in particular. To this end, a standard susceptible-exposed-infected-recovered (SEIR) epidemiological model is used. It is shown that COVID-19 epidemic outbreaks follow a suitably defined SEIR order parameter. Intervention measures switch the eigenvalue of the order parameter from a positive to a negative value, and in doing so, stabilize the COVID-19 disease-free state. The subsiding of COVID-19 epidemics eventually follows the remnant of the order parameter of the infection dynamical system. These considerations are illustrated for the COVID-19 epidemic in Thailand from January to May 2020. The decay of effective contact rates throughout the three epidemic stages is demonstrated. Evidence for the sign-switching of the dominant eigenvalue is given and the order parameter and its stage-3 remnant are identified. The presumed impacts of interventions measures implemented in Thailand are discussed in this context.

COVID-19 interventions in some European countries induced bifurcations stabilizing low death states against high death states: An eigenvalue analysis based on the order parameter concept of synergetics.

Taking a dynamical systems perspective, COVID-19 infections are assumed to spread out in a human population via an instability. Conversely, government interventions to reduce the spread of the disease and the number of fatalities may induce a bifurcation that stabilizes a desirable state with low numbers of COVID-19 cases and associated deaths. The key characteristic feature of an infection dynamical system in this context is the eigenvalue that determines the stability of the states under consideration and is known in synergetics as the order parameter eigenvalue. Using a SEIR-like infection disease model, the relevant order parameter and its eigenvalue are determined. A three stage methodology is proposed to track and estimate the eigenvalue through time. The method is applied to COVID-19 infection data reported from 20 European countries during the period of January 1, 2020 to June 15. It is shown that in 15 out of the 20 countries the eigenvalue switched its sign suggesting that during the reporting period an intervention bifurcation took place that stabilized the desirable low death state. It is shown that the eigenvalue analysis also allows for a ranking of countries by the degree of the stability of the infection-free state. For the investigated countries, Ireland was found to exhibit the most stable infection-free state. Finally, a six point classification scheme is suggested with groups 5 and 6 including countries that failed to stabilize the desirable infection-free low death state. In doing so, tools for assessing the effectiveness of government interventions are provided that are at the heart of bifurcation theory, in general, and synergetics, in particular.