Nonlinear dynamics modeling and epidemic forecast of COVID-19

To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model named SLIR(susceptible-low-risk-infected-recovered), based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.

The complex evolution of information quality improvement in competitive market

Information is important market resource. High-quality information is beneficial to increase enterprise's reputation and reduce consumer's verification cost. This paper constructs a two-layer dynamic model, in which enterprises simultaneously conduct price and information game. The goal of profit maximization integrates two types of games into one system. The complex evolution of the two-layer system are studied by equilibrium analysis, stability analysis, bifurcation diagram, entropy and Lyapunov exponent. It is found that improving the information quality through regulations will increase involution and reduce stability of the market. Then, the block chain technology is introduced into the model for improving information quality of the market. It is found that increasing enterprises' willingness to adopt block chain can improve the information quality quickly and effectively, and that is verified by entropy value. Therefore, the application and promotion of new technologies are more effective than exogenous regulations for improving information quality in market.

ANALYZING A MACROPRUDENTIAL INSTRUMENT DURING THE COVID-19 PANDEMIC USING BORDER COLLISION BIFURCATION

Bank Indonesia, el banco central de Indonesia, ha realizado ajustes en un instrumento de política macroprudencial llamado índice de intermediación macroprudencial (IIM) para impulsar el crecimiento de los préstamos en el contexto de la recuperación económica nacional debido a la pandemia de COVID-19. En este artículo, se desarrolla un modelo dinámico de préstamo bancario con comportamiento procíclico, y se equipa con el instrumento predecesor del IIM denominado requerimiento de reserva basado en la relación préstamo-depósito (RR-RPD). Examinamos los efectos de los parámetros RR-RPD en la dinámica del préstamo utilizando el análisis de bifurcación de colisión de fronteras para determinar los valores umbral de los parámetros RR-RPD para que se pueda mantener la estabilidad del equilibrio del préstamo. Este modelo se aplica a los datos mensuales de los bancos comerciales de Indonesia antes y durante la pandemia de COVID-19 para evaluar la región de estabilidad de los parámetros del instrumento.Alternate :Bank Indonesia, the central bank of Indonesia, has made adjustment settings in a macroprudential policy instrument called macroprudential intermediation ratio (MIR) to boost loan growth in the context of national economic recovery due to the COVID-19 pandemic. In this paper, a dynamic model of bank loan with procyclicality behavior is developed, and it is equipped with the predecessor of the MIR instrument called loan-to-deposit ratio based reserve requirement (LDR-RR). We examine the effects of LDR-RR parameters on the dynamics of loan using the border collision bifurcation analysis to determine the threshold values of the LDR-RR parameters so that the stability of loan equilibrium can be maintained. This model is applied to monthly data of Indonesian commercial banks before and during the COVID-19 pandemic to assess the stability region of the instrument parameters.

Virtual labs for the study of enzymatic stirred tank bioreactors

Virtual laboratories have successfully proven to be very versatile and intuitive when simulating experiments in science, biotechnology, and engineering. These tools must complement the experiments carried out in real labs or pilot plants. This study describes the creation of a virtual laboratory through the Easy JavaScript Simulation platform. A web‐based simulation of an enzymatic stirred‐tank bioreactor has been built using a dynamic model. This simulation reproduces the behavior of a continuous bioreactor, including the deviations of ideal mixing conditions as by the use of an in tanks‐in‐series model for nonideal flow. This article describes the continuous dynamic model in a stirred tank bioreactor, as well as the operation of a tool capable of carrying out virtual practice with students. Practice scripts have been developed that should be used by students during the practical classes. This interactive tool is powerful and useful to develop many experiments by varying the different input parameters, saving time and resources. In addition, the tool allows following teaching sessions in specific situations such as the health situation derived from the pandemic caused by COVID‐19.

Applications of mathematical modeling and simulation in COVID-19

In this chapter, we describe the use of mathematical and simulation tools applied in various aspects of the coronavirus disease 2019 pandemic through an extensive and careful review of the recently published works. We detailed the existing implementations of models dealing with (i) the spread of the disease, (ii) the prediction of new outbreaks, (iii) the existence of new variants of the virus, (iv) the effects on the at-risk population, (v) the long-term health consequences, (vi) the resource allocation for supportive staffs and clinical beds, (vii) the dynamics of transmission and how to cut the transmission chain, (viii) the impacts of travel restrictions, social distancing and early detection, (ix) the efficacy of prophylactic agents, (x) the effects of optimum interventions, (xi) the impact of existing vaccines, and (xii) the economic effects of the pandemic. © 2023 Elsevier Inc. All rights reserved.

Detecting the research structure and topic trends of social media using static and dynamic probabilistic topic models

PurposeA huge volume of published research articles is available on social media which evolves because of the rapid scientific advances and this paper aims to investigate the research structure of social media.Design/methodology/approachThis study employs an integrated topic modeling and text mining-based approach on 30381 Scopus index titles, abstracts, and keywords published between 2006 and 2021. It combines analytical analysis of top-cited reviews with topic modeling as means of semantic validation. The output sequences of the dynamic model are further analyzed using the statistical techniques that facilitate the extraction of topic clusters, communities, and potential inter-topic research directions.FindingsThis paper brings into vision the research structure of social media in terms of topics, temporal topic evolutions, topic trends, emerging, fading, and consistent topics of this domain. It also traces various shifts in topic themes. The hot research topics are the application of the machine or deep learning towards social media in general, alcohol consumption in different regions and its impact, Social engagement and media platforms. Moreover, the consistent topics in both models include food management in disaster, health study of diverse age groups, and emerging topics include drug violence, analysis of social media news for misinformation, and problems of Internet addiction.Originality/valueThis study extends the existing topic modeling-based studies that analyze the social media literature from a specific disciplinary viewpoint. It focuses on semantic validations of topic-modeling output and correlations among the topics and also provides a two-stage cluster analysis of the topics.

Emphasizing visual tools: using R to understand dynamic models of biology and epidemiology

Epidemiological models have enhanced relevance because of the COVID-19 pandemic. In this note, we emphasize visual tools that can be part of a learning module geared to teaching the SIR epidemiological model, suitable for advanced undergraduates or beginning graduate students in disciplines where the level of prior mathematical knowledge of students may not be very strong. Visual tools – phase portrait, flow field and trajectory and line plots – available in the R software are presented in a step by step manner, moving from the exponential growth model to the logistic growth model and then to the SIR model. Code for numerical simulation of differential equations and estimation of parameters is presented for the SIR model. Suggestions for students to connect the learning from these examples with research papers on COVID-19 are provided.

Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models.

BACKGROUND: There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory. METHODS: Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk. RESULTS: Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81-303.84) and the actual number of the spillovers (0.94-74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value. CONCLUSIONS: Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus.

Prediction and Analysis of Infectious Diseases Based on M-SIR Model

The epidemic of infectious diseases has become a major problem threatening the world public health, and the dynamic models of virus spreading are widely used for epidemic tracking and prediction. The existing dynamic models do not consider the synergistic effects of population migration factors and changes in transmission rates on diseases. Therefore, based on the SIR (Susceptible-Infectious-Recovered) model, the time-dependent M-SIR (Migration-Susceptible-Infectious-Recovered) model was proposed by introducing the population migration (Migration) factor. Meanwhile, introducing the machine learning LightGBM (Light Gradient Boosting Machine) method to track the infection rate and recovery rate, and explored the impact of cross-regional population movement and prevention and control measures on the development of the epidemic. Take the new crown epidemic as an example, firstly, the data of population migration and epidemic spread were statistically analyzed to monitor the relationship between population mobility and epidemic development. Then, the m-sir model is used to predict the infected cases and removed cases in Beijing and Shanghai. Through comparative analysis with the SIR model, the prediction accuracy of the model has been greatly improved. At the same time, the development trend of the epidemic situation in related cities before and after control is explored, which can provide some theoretical support for future epidemic prediction and control decisions. © 2022 IEEE.

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.

NEW PARADIGMS FOR SCHOOL DESIGN INTO THE ECOLOGICAL TRANSITION

In light of the energy and environmental crisis and of the concurrent impact of the sanitary emergency due to the Covid-19, it’s necessary that even the construction industry re-examine, with a new perspective, the building paradigms, to search for new functional solutions meant to be sustainable within a socioeconomic context in fast development. This scenery is changing rapidly and the School, because of its proper function to educate the future generation, has to serve as transitional outpost. This work exhibits the methodological path to define new design paradigms based on the quality and resilience of the spaces, in order to satisfy the new didactical needs, regarding both its use than its social function, even during the emergency period, through an approach based onto a dynamic analysis of spaces and paths, able to reinforce the Civic Center role belonging to the school building in relation to the urban landscape. © 2022, Luciano Editore. All rights reserved.

A Non-autonom SIR Model in Epidemiology

With the outbreak of COVID-19, the modelling of epidemic spread has once again become highly important. This paper introduces an epidemic spreading model with a changing infection rate. This model extends the traditional SIR (Susceptible – Infected – Removed) model. The SIR model is a dynamic model which divides individuals into 3 groups: susceptible, infected, and removed (including recovered and died). Individuals in each group have a constant proportion to change to the next group. This paper assumes the infection rate is dependent on the development cycle of the virus, which can vary in the different periods since being infected, instead of constants. This makes the differential equations a non-autonomous model. This paper works on how to fit the function of the infection rate and solve the equations. This paper uses Burr distribution which has 3 unknown parameters as the function of infection rate, and then discusses about two different methods to get these parameters—the least-squares method and the maximum likelihood estimation. As a numerical experiment of this model, this paper uses the data of COVID-19 in Ireland to make predictions and compare with the traditional SIR model. The non-autonomous model in this paper shows better performance than the traditionary SIR model. This new model might be potential in further epidemic simulation, and it is not hard to be combined with other extensions of the SIR model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dynamic model change analysis of social behavior processes during the COVID19 pandemic

In this paper, an analysis of changes in dynamic process models described by variables that represent social behavior from the point of view of people's mobility and of economic indices in the framework of the COVID19 pandemic is presented. Here, the mobility described by Google and Apple is used as a proxy for the social behavior to correlate it with the dynamic evolution of daily COVID19 infections. In addition, indices related from the global economy are used as a proxy of the socio-economic process, where two of ascending evolution (MSFT Microsoft and NASDAQ, Inc.) and another with smooth evolution (WTI oil gallon price) are analyzed. The evolution of such proxies are related to the daily COVID19 cases. In the latter case, it is difficult to detect a territorial region of influence given the number of origins of influences that the selected indices have, but the impact of the first peak in China and the subsequent evolution in the world can be studied, especially in our country and in the Netherlands. The main findings include that the underlying model for social behavior has changed in different stages, depending on the months of the year and that after mid-2021 an unstable equilibrium is on the track, with the addition of the new possibilities provided by the vaccination process and the rules of social coexistence. It is concluded that it is necessary to analyze which decision should be taken at the social level of public policy and which personal decisions for each individual. © 2021 IEEE.

Study on Forecasting Model of Port Container Throughput in China

The new corona pneumonia (COVID-19) epidemic is still spreading globally. The critical role of ports in the global economy and logistics system are highlighted. More and more attention is paid to port development trend. Therefore, it is very important to establish a model to forecast the development trend of the port container throughput. This paper quantified the factors affecting container throughput such as economy and foreign trade, and predicted the container throughput of ports in China. In this paper, a multi-factor dynamic model is constructed, which considers macroeconomic growth, foreign trade import and export volume, containerization rate, single container weight, empty and heavy container ratio and other factors. With the data of 2020 as the benchmark, it is comprehensively predicted that by 2025. China's port container throughput will reach 320 million TEU. The container throughput growth will continue to decline. The average annual growth will be 4.0% in the 14th Five-Year Plan period. Further, this model can be used to predict the development trend of port container in 2050. At the same time, the development peak of port container throughput in China can also be analyzed. This conclusion can provide a basis for government departments and enterprises to make decisions. © 2022 SPIE

The effect of network structure on the purchase of virtual goods on social networking services

Purpose>This paper aims to identify the effect of social structure variables on the purchase of virtual goods. Using field data, it also tests whether their effects on a social networking service are dynamic.Design/methodology/approach>To achieve the research objectives, the authors have applied the random effects panel Tobit model with actual time-series corporate data to explain a link between network structure factors and actual behavior on social networking services.Findings>The authors have found that various network structure variables such as in-degree, in-closeness centrality, out-closeness centrality and clustering coefficients are significant predictors of virtual item sales;while the constraint is marginally significant, out-degree is not significant. Furthermore, these variables are time-varying, and the dynamic model performs better in a model fit than the static one.Practical implications>The findings will help social networking service (SNS) operators realize the importance of understanding network structure variables and personal motivations or the behavior of consumers.Originality/value>This study provides implications in that it uses various and dynamic network structure variables with panel data.

Advances in air quality research – current and emerging challenges

This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

New insights into the DPSIR model: Revealing the dynamic feedback mechanism and efficiency of ecological civilization construction in China

Managing ecosystems is considered a "wicked problem" without clear solutions due to the limited understanding of complex ecosystems and social dynamics. In this study, a method based on the Driving forces–Pressures–State–Impacts–Responses (DPSIR) framework was developed to reveal the Ecological Civilization Construction (ECC) together with structural equation modeling (SEM), panel data model (PDM), coupling and coordination degree (CCD) model, and data envelopment analysis (DEA). The SEM reveals that component Responses as exogenous variables can better explain the DPSIR framework nexuses than Driving forces, indicating that environmental protection measures taken by Chinese government played a dominant role in ECC. ECC indexes (ECCI) of 30 Chinese provinces were 18–87% higher in 2019 than 2012, and the PDM demonstrates that temperature, precipitation, and GDP can explain about 87.2% of ECCI variation among 30 provinces. About 12–40% increase in CCD within the DPSIR framework were detected in 30 Chinese provinces in 2019 compared to 2012. The DEA suggests that China's ECC's average comprehensive and technical efficiencies were only 0.62 and 0.77 in 2019, respectively. Meanwhile, these results show that ECC remains to be strengthened and coordinated. Implications on ECC were proposed for some provinces. Overall, this study proposes a Response-driven pathway named RDPSI can explain the achievements and limitation factors in China's ECC. Also, our results emphasize the importance of integrating science and technology, policy formulation, and precise implementation to achieve sustainable development. • A new method for studying the dynamic relationship of the SES is developed. • A sustainable development pathway named RDPSI is proposed. • China has formed a SES of positive feedback cycle, but the system is unstable. • There are increasingly significant spatial correlation issues in 30 Chinese provinces. [ FROM AUTHOR] Copyright of Journal of Cleaner Production is the property of Elsevier B.V. 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.)

Uniform Persistence and Global Attractivity in a Delayed Virus Dynamic Model with Apoptosis and Both Virus-to-Cell and Cell-to-Cell Infections

In this paper, we study the global dynamics of a delayed virus dynamics model with apoptosis and both virus-to-cell and cell-to-cell infections. When the basic reproduction number R0>1, we obtain the uniform persistence of the model, and give some explicit expressions of the ultimate upper and lower bounds of any positive solution of the model. In addition, by constructing the appropriate Lyapunov functionals, we obtain some sufficient conditions for the global attractivity of the disease-free equilibrium and the chronic infection equilibrium of the model. Our results extend existing related works.

Estimation of the Basic Reproduction Number for the COVID-19 Pandemic in Minnesota

This paper focuses on the dynamics of the COVID-19 pandemic and estimation of associated real-time variables characterizing disease spread. A nonlinear dynamic model is developed which enhances the traditional SEIR epidemic model to include additional variables of hospitalizations, ICU admissions, and deaths. A 6-month data set containing Minnesota data on infections, hospital-ICU admissions and deaths is used to find least-squares solutions to the parameters of the model. The model is found to fit the measured data accurately. Subsequently, a cascaded observer is developed to find real-time values of the infected population, the infection rate, and the basic reproduction number. The observer is found to yield good real-time estimates that match the least-squares parameters obtained from the complete data set. The importance of the work is that it enables real-time estimation of the basic reproduction number which is a key variable for controlling disease spread. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license

Optimization of Multisource Dynamic Model in TBO

When extracting flight data from airport terminal area, there are matters such as large volume, unclear features, and similar trend in time series. In order to deal with the related issues and to optimize the description, by combining with the TBO (Trajectory-Based Operation), an application proposed by the ICAO (International Civil Aviation Organization) in ASBU (Aviation System Block Upgrade), using multisource dynamic model to establish 4DDW (4D dynamic warping) algorithm, the multisource modeling integrated with evaluation system is proposed to realize the flight path optimization with time series characteristics and accord with the interval concept. The calculation results show that 4DDW can obtain the optimal solution for multiprofile calculation of TBO by comparing the composite trajectory deviation values and time dimension planning using the buffer and threshold values recommended by ICAO in airspace planning and flight procedure design. The results meet the requirements of high accuracy and convergence features of spatial waypoints and can improve the airport operation standards and terminal area capacity.