Narrow Results

In this paper, two definitions about traffic congestion are proposed. We describe the traffic congestion spreading with the SEIR model of a complex small-world network. In addition, the relationships among the congestion factor, the number of average infection, the average recover rate and the infection rate are given by simulations in general traffic congestion conditions.

With the rapid development of the Internet, social media networks have become the primary platform for people to express their views. In addition, network public opinion has a considerable influence on society. Thus, considering the significant impact of online public opinion on society, it is necessary to study and analyze the propagation process for public opinion. In this study, we propose the Media and Interpersonal Relationship-SEIR (MI-SEIR) model based on the SEIR model. Our proposed model considers the impact of media transmission and interpersonal relationships on opinion propagation. Our MI-SEIR model divides the propagation nodes into three categories: support, neutral and opposition. There is a discussion mechanism between these nodes that represents the node’s viewpoint value evolution rule based on the node firmness, influence of nodes, quality of media coverage and parameters of infection. The state transition of nodes is decided based on the change of opinion value. Our simulation experimental results show that this model is more representative of the real propagation of online public opinion and is thus of practical significance for research and opinion analysis applications.

Inspired by infectious disease dynamics and modern psychology, this paper aims at constructing a multi-dimensional function to get the model of information dissemination on social networks under epidemic-related panic base on the characteristics of individual differences and global characteristics, like emotional cumulative effect, herd effect, time-sensitive decline effect, cognitive level, intimacy, personal influence, etc. The results show that the psychological effect has a significant effect on the increase of the spread of panic news; When netizens are in an emotional atmosphere, their emotional self-regulation ability is limited; when the infection rate is relatively low, the characteristics of individual differences play a leading role in affecting the spreading process. When the infection rate is high enough, the herd effect and emotional cumulative effect play a major role in promoting information dissemination; In a society with a higher rate of emotional contact, it is easier to form a kind of collective wisdom, which can help the collective quickly identify rumors. Moreover, in this kind of society, the role of opinion leaders is limited, and timely refutation of rumors can significantly reduce the spread of panic news.

In order to propose a more realistic epidemic dynamics model and effective medical resource allocation strategy, this paper constructs an improved SEIR model combined with a dynamical medical resource allocation model and individual behavior sharing medical resources. Simultaneously, a genetic algorithm to solve the medical resource allocation model is proposed to obtain the optimum resource allocation strategy. In this SEIR model, there is an important critical value of the stored medical resources, when the number of stored medical resources is more than the critical value, the inhibition of epidemic can be continuously enhanced until it reaches a minimum threshold, and then stabilizes near a minimum value, but when the resource surplus is below the critical value, the inhibitory effect on epidemic will weaken. The results demonstrate that the number of patients in the proposed method decreased more than 40% compared with the conventional control experiment. Moreover, the algorithm can automatically make decisions according to individual behavior in sharing preferences and the epidemic development trend.

This paper takes COVID-19-related online rumors as the research object, and explores the law of spreading public opinion in social networks. The paper also conducts empirical research on the relationship between rumor spreading, user characteristics and subject interest differences, and analyzes the common influence of individual factors and social environment. In the process of public opinion dissemination, measures that can effectively regulate the dissemination of public opinion are proposed. Based on the susceptible-exposed-infected-recovered (SEIR) model, this paper analyzes the influence of individual differentiation characteristics, friend factors, and time-dependent decline on user status changes. The study found that the user’s environment can affect the spread and popularity of public opinion information, and prolong the survival time of public Controlling the propagation threshold and exit threshold of the platform helps to control the large-scale dissemination of online public opinion. The extinction of public opinion is affected by the decline of time and heat rather than certain probability.

It is crucial to capture the actual infection scale of communicable diseases. However, the official case numbers cannot equal to actual infection scale in society, because a large number of asymptomatic individuals are not recognized. To deal with this challenge, this paper takes COVID-19 as the object, and develops an improved SEIR dynamics model to estimate its actual infection scale. Generalized to the circumstances in this work, we improve the classical SEIR model by considering three implicit factors: self-recovered asymptomatic individuals, recovered individuals, and deceased individuals. The dynamics process inside the improved model is expressed using mathematical formulas, and the parameter estimation scheme is given accordingly. To evaluate the estimation effect of the proposal, we employ pandemic data from 10 representative countries to build experimental scenario. The results obtained through model fitting demonstrate that the estimated actual infection scale is approximately 10–30 times higher than the reported average “newly confirmed cases”. Furthermore, our findings reveal a noteworthy negative correlation between the transmission coefficient and vaccination rate, confirming the beneficial role of vaccination in mitigating COVID-19 spreading.

In this study, an SEIR epidemic model is proposed for treatment of infectives considering the development of acquired immunity in recovered individuals. We employed two different types of treatment functions. Stability analysis for disease-free as well as endemic equilibria is performed. It is observed that the existence of unique endemic equilibrium depends on the basic reproductive number R₀ as well as on treatment rate. Numerical simulations are performed on the proposed models to support and analyze theoretical findings.

In the new paradigm of health-centric governance, policymakers are in constant need of appropriate metrics to determine suitable policies in a non-arbitrary fashion. To this end, in this paper, a compartmentalized model for the transmission of COVID-19 is developed, with a socially distanced compartment added to the model. The modification allows for administrators to quantify the extent to which voluntary social distancing norms are followed, and address restrictions accordingly. Modifications are also made to incorporate inter-region migration, and suitable metrics are proposed to quantify the impact of migration on the rise of cases. The healthcare capacity is modeled and a method is developed to study the consequences of the saturation of the healthcare system. The model and related measures are used to study the nature of the transmission and spread of COVID-19 in India, and appropriate insights are drawn.

In the last 60 years, great progress has been made in controlling and preventing tuberculosis in China. However, the number of tuberculosis cases has increased dramatically in the last 25 years, mainly due to the lack of effective control measures of immigrating populations with tuberculosis. In order to explore the effective control and prevention measures we propose a deterministic model to study the transmission dynamics of tuberculosis in Guangdong province of China in this paper. The model consists of susceptible, exposed and infectious recovered subpopulations of immigrating populations from other provinces and the local population of Guangdong. We obtain the effective reproduction number. Based on the analysis, we also establish an optimal immune programming model, and get the optimal proportion of vaccine coverage with control of the effective reproduction number. Simulation is used to determine the validation and reliability. Our study demonstrates that the immigrating population from different provinces needs to be vaccinated according to the incidence rate of TB in their original provinces, and it is an effective way to prevent the outbreak of tuberculosis in Guangdong.

Today, the entire world is witnessing an enormous upsurge in coronavirus pandemic (COVID-19 pandemic). Confronting such acute infectious disease, which has taken multiple victims around the world, requires all specialists in all fields to devote their efforts to seek effective treatment or even control its disseminate. In the light of this aspect, this work proposes two new fractional-order versions for one of the recently extended forms of the SEIR model. These two versions, which are established in view of two fractional-order differential operators, namely, the Caputo and the Caputo–Fabrizio operators, are numerically solved based on the Generalized Euler Method (GEM) that considers Caputo sense, and the Adams–Bashforth Method (ABM) that considers Caputo–Fabrizio sense. Several numerical results reveal the impact of the fractional-order values on the two established disease models, and the continuation of the COVID-19 pandemic outbreak to this moment. In the meantime, some novel results related to the stability analysis and the basic reproductive number are addressed for the proposed fractional-order Caputo COVID-19 model. For declining the total of individuals infected by such pandemic, a new compartment is added to the proposed model, namely the disease prevention compartment that includes the use of face masks, gloves and sterilizers. In view of such modification, it is turned out that the performed addition to the fractional-order Caputo COVID-19 model yields a significant improvement in reducing the risk of COVID-19 spreading.

Coronavirus disease 2019 (COVID-19) is a worldwide infectious disease and very contagious. A mathematical model using ordinary differential equations was introduced to study the dynamics of COVID-19. Local and global stability analysis was proved, which depends on the basic reproduction number ${{ℛ}}_0$. The value of parameters was estimated using the COVID-19 outbreak in China. Based on the index sensitivity results, an optimal control problem was formulated to represent the implementation of a movement control order. Finally, it is numerically shown that the spread of COVID-19 can be smothered viably by implementing an optimal control strategy.

Tuberculosis (TB) is a highly contagious disease caused by Mycobacterium tuberculosis, which has historically resulted in numerous fatalities. The analysis of TB is conducted through mathematical models featuring differential equations that describe the dynamics of epidemic spread, as well as its control and prevention. This paper presents the SEIR $+$ D model of TB. According to this model, the total population is divided into five compartments: susceptible, exposed, infected, recovered, and deceased. This paper evaluates the rates (such as transmission rate, incubation rate, etc.) at which individuals transition from one compartment to another under various scenarios.

Since the first outbreak of Middle East Respiratory Syndrome (MERS), Korea has a quite rapid MERS spread compared to other countries. Possible causes for such a sudden increase include the undiagnosed initial patient and lapses in infection control practices. To characterize MERS infection and transmission, this paper applies the period-based discrete SEIR model. Infected people of SEIR model shows a good fit to observed patients and MERS will become extinct around 113 days since the first outbreak. Through an effective quarantine plan, if we can reduce exposable people by 20%, it is estimated that the maximum number of infectious people may decrease by about 69% and MERS fade-out period will be shortened by about 30%. Simulations on assumed model support that Korean government’s two policies to control MERS infection rate are effective in lessening its spread. Simulation on reproduction ratio scenarios in SEIR model indicates that success in early infection control practices is critical for shortening the period of disease fade-out. Even there are some restrictions and assumptions on SEIR model simulation, our simulation results are to be helpful in developing strategies to prevent the infectious diseases like MERS.

In this study, we developed a SEIR model, including social interactions and individual human mobility in everyday activities. For this purpose, daily mobility of people was considered by using the molecular dynamic method and the virus spreading was modeled employing the ordinary SEIR scheme. Utilizing this model, the variation of population size, density, and health strategy as well as the effect of busy places such as malls, were considered. The results show that our flexible model is able to consider the effects of different parameters such as distance between peoples, local population density and health strategy in the outbreak.

In this paper, we study a system of differential equations that models the population dynamics of SEIR vector transmission of dengue fever. The model studied breeding value based on the number of reported cases of dengue fever in Selangor because the state had the highest case in Malaysia. The model explains that maximum level of human infection rate of dengue fever achieved in a very short period. It is also revealed that there existed suitability result between theoretical and empirical calculation using the model. The result of SEIR model will hopefully provide an insight into the spread of dengue fever in Selangor Malaysia and basic form for modeling this area.