Search Results (22)

A reaction–diffusion susceptible–infectious–susceptible disease model with advection, vital dynamics (birth–death effects), and a standard incidence infection mechanism is carefully analyzed. Two distinct diffusion coefficients for the susceptible and infected populations are considered. The Lie symmetries and closed-form solutions for the RDA–SIS disease model are established. The derived solution allows to study dynamics of disease transmission. Our simulation clearly illustrates the evolution dynamics of the model by using the values of parameters from academic sources. A sensitivity analysis is performed, offering valuable perspectives that could inform future disease management policies. Full article

The modeling and analysis of epidemic processes in networks have attracted much attention over the past few decades. A major underlying assumption is that the recovery process and infection process are homogeneous, allowing the associated theoretical studies to be conducted in a convenient manner. However, the recovery and infection processes usually exhibit heterogeneous rates in the real world, which makes it difficult to characterize the general relations between the dynamics and the underlying network structure. In this work, we focus on the susceptible–infected–susceptible (SIS) epidemic process with heterogeneous recovery rates in a finite-size complete graph. Specifically, we study the metastable-state statistical properties of SIS epidemic dynamics with two different nodal recovery rates in complete graphs. We propose approximate solutions to the metastable-state expectation and the variance in the number of infected nodes within the framework of the mean-field approximation method. We also derive several upper and lower bounds of the steady-state probability that a node is in the infected state. We verify the proposed approximate solutions of the mean and variance via simulations. This work provides insights into the fluctuations in the statistical properties of epidemic processes with complex dynamical behaviors in networks. Full article

This work investigates a class of susceptible–infected–susceptible (SIS) epidemic model with reaction–diffusion–advection (RDA) by utilizing the Lie group methods. The Lie symmetries are computed for the three widely used incidence functions: standard incidence, mass action incidence, and saturated incidence. The Lie algebra for the SIS-RDA epidemic model is four-dimensional for the standard incidence function, three-dimensional for mass action incidence, and two-dimensional for saturated incidence. The reductions and closed-form solutions for the SIS-RDA epidemic model for the standard incidence infection mechanism are established. The transmission dynamics of an infectious disease utilizing closed-form solutions is presented. To illustrate the paths of susceptible and infected populations, we consider the Cauchy problem. Moreover, a sensitivity analysis is conducted to provide insights into potential policy recommendations for disease control. Full article

The Susceptible–Infected–Susceptible (SIS) model in complex networks is one of the critical models employed in the modeling of virus spread. The study of the heterogeneous SIS model with a non-homogeneous nodal infection rate in finite-size networks has attracted little attention. Investigating the statistical properties of heterogeneous SIS epidemic dynamics in finite networks is thus intriguing. In this paper, we focus on the measure of variability in the number of infected nodes for the heterogeneous SIS epidemic dynamics in finite-size bipartite graphs and star graphs. Specifically, we investigate the metastable-state variance of the number of infected nodes for the SIS epidemic process in finite-size bipartite graphs and star graphs with heterogeneous nodal infection rates. We employ an extended individual-based mean-field approximation to analyze the heterogeneous SIS epidemic process in finite-size bipartite networks and star graphs. We derive the approximation solutions of the variance of the infected number. We verify the proposed theory by simulations. The proposed theory has the potential to help us better understand the fluctuations of SIS models like epidemic dynamics with a non-homogeneous infection rate. Full article

Progressive respiratory airway destruction due to unresolved inflammation induced by periodic infectious exacerbation episodes is a hallmark of cystic fibrosis (CF) lung pathology. To clear bacteria, neutrophils release high amounts of reactive oxygen species (ROS), which inflict collateral damage to the neighboring epithelial cells causing oxidative stress. A former genome-wide small interfering RNA (siRNA) screening in CF submucosal gland cells, instrumental for mucociliary clearance, proposed tumor necrosis factor receptor superfamily member 1B (TNFRSF1B; TNFR2) as a potential hit involved in oxidative stress susceptibility. Here, we demonstrate the relevance of TNFRSF1B transcript knock-down for epithelial cell protection under strong oxidative stress conditions. Moreover, a blockade of TNFR signaling through its ligand lymphotoxin-α (LTA), overexpressed in airway epithelial cells under oxidative stress conditions, using the anti-tumor necrosis factor (TNF) biologic etanercept significantly increased the viability of these cells from a toxic oxidizing agent. Furthermore, bioinformatic analyses considering our previous RNA interference (RNAi) screening output highlight the relevance of TNFRSF1B and of other genes within the TNF pathway leading to epithelial cell death. Thus, the inhibition of the LTα3-TNFR2 axis could represent a useful therapeutic strategy to protect the respiratory airway epithelial lining from the oxidative stress challenge because of recurrent infection/inflammation cycles faced by CF patients. Full article

Pseudorabies is an acute and febrile infectious disease caused by pseudorabies virus (PRV), a member of the family Herpesviridae. Currently, PRV is predominantly endemoepidemic and has caused significant economic losses among domestic pigs. Other animals have been proven to be susceptible to PRV, with a mortality rate of 100%. In addition, 30 human cases of PRV infection have been reported in China since 2017, and all patients have shown severe neurological symptoms and eventually died or developed various neurological sequelae. In these cases, broad-spectrum anti-herpesvirus drugs and integrated treatments were mostly applied. However, the inhibitory effect of the commonly used anti-herpesvirus drugs (e.g., acyclovir, etc.) against PRV were evaluated and found to be limited in this study. It is therefore urgent and important to develop drugs that are clinically effective against PRV infection. Here, we constructed a high-throughput method for screening antiviral drugs based on fluorescence-tagged PRV strains and multi-modal microplate readers that detect fluorescence intensity to account for virus proliferation. A total of 2104 small molecule drugs approved by the U.S. Food and Drug Administration (FDA) were studied and validated by applying this screening model, and 104 drugs providing more than 75% inhibition of fluorescence intensity were selected. Furthermore, 10 drugs that could significantly inhibit PRV proliferation in vitro were strictly identified based on their cytopathic effects, virus titer, and viral gene expression, etc. Based on the determined 50% cytotoxic concentration (CC₅₀) and 50% inhibitory concentration (IC₅₀), the selectivity index (SI) was calculated to be 26.3–3937.2 for these 10 drugs, indicating excellent drugability. The antiviral effects of the 10 drugs were then assessed in a mouse model. It was found that 10 mg/kg brincidofovir administered continuously for 5 days provided 100% protection in mice challenged with lethal doses of the human-origin PRV strain hSD-1/2019. Brincidofovir significantly attenuated symptoms and pathological changes in infected mice. Additionally, time-of-addition experiments confirmed that brincidofovir inhibited the proliferation of PRV mainly by interfering with the viral replication stage. Therefore, this study confirms that brincidofovir can significantly inhibit PRV both in vitro and in vivo and is expected to be an effective drug candidate for the clinical treatment of PRV infections. Full article

In the present article, we research the existence of the positive periodic solutions for a mathematical model that describes the propagation dynamics of a pathogen living within a vector population over a plant population. We propose a generalized compartment model of the susceptible–infected–susceptible (SIS) type. This model is derived primarily based on four assumptions: (i) the plant population is subdivided into healthy plants, which are susceptible to virus infection, and infected plants; (ii) the vector population is categorized into non-infectious and infectious vectors; (iii) the dynamics of pathogen propagation follow the standard susceptible–infected–susceptible pattern; and (iv) the rates of pathogen propagation are time-dependent functions. The main contribution of this paper is the introduction of a sufficient condition for the existence of positive periodic solutions in the model. The proof of our main results relies on a priori estimates of system solutions and the application of coincidence degree theory. Additionally, we present some numerical examples that demonstrate the periodic behavior of the system. Full article

This paper introduces the mathematical formalization of two probabilistic procedures for susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) infectious diseases epidemic models, over Erdös-Rényi contact networks. In our approach, we consider the epidemic threshold, for both models, defined by the inverse of the spectral radius of the associated adjacency matrices, which expresses the network topology. The epidemic threshold dynamics are analyzed, depending on the global dynamics of the network structure. The main contribution of this work is the relationship established between the epidemic threshold and the topological entropy of the Erdös-Rényi contact networks. In addition, a relationship between the basic reproduction number and the topological entropy is also stated. The trigger of the infectious state is studied, where the probability value of the stability of the infected state after the first instant, depending on the degree of the node in the seed set, is proven. Some numerical studies are included and illustrate the implementation of the probabilistic procedures introduced, complementing the discussion on the choice of the seed set. Full article

A deterministic model for the transmission dynamics of SIRS-type malaria in hosts and SI in mosquito populations is proposed. The host population is differentiated between naive, primary, and secondary susceptible individuals. Primary and secondary infected individuals (and also recovered) are differentiated from each other according to their degree of infectiousness. The impact of changing the relative susceptibilities of primary and secondary (with respect to naive) susceptible individuals on the dynamics is investigated. Also, the impact of changing the relative infectiousness of secondary infected, primary, and secondary recovered individuals (with respect to primary infected) on the transmission dynamics of malaria is studied. Full article

In this paper, we discuss the pattern dynamics of an SI epidemic model caused by spatial dependency, which is represented by self- and cross-diffusion terms. Cross-diffusion of the susceptible represents a tendency of the susceptible to stay away from the infected. Meanwhile, cross-diffusion of the infected represents their movement to the location with a high density of the susceptible. This study focuses on the presence of the effects of cross-diffusion terms on the Turing instability. This study applies Turing analysis to yield the Turing space and Turing patterns corresponding to the model by involving the infection rate as the bifurcation parameter. The results show that the presence of cross-diffusion terms narrows the Turing space depending on the magnitude of the cross-diffusion coefficients itself. Dynamical behaviors of the model are then investigated through a series of numerical simulations that successfully perform five types of patterns, i.e., spots, spots–stripes, stripes, stripes–holes, and holes. Those patterns give a description of the spread of an infectious disease. The holes denote an outbreak situation in a region, whereas the non-outbreak situation is emphasized by the spots pattern. Further, the decreasing of the ratio of recruitment and death rates indicates that the increasing of the infection rate triggers an outbreak. The present study confirms that cross-diffusion terms have a significant role in infectious disease transmission, spatially. Full article

The structure of a model has an important impact on information dissemination. Many information models of hypernetworks have been proposed in recent years, in which nodes and hyperedges represent the individuals and the relationships between the individuals, respectively. However, these models select old nodes based on preference attachment and ignore the effect of aggregation. In real life, friends of friends are more likely to form friendships with each other, and a social network should be a hypernetwork with an aggregation phenomenon. Therefore, a social hypernetwork evolution model with adjustable clustering coefficients is proposed. Subsequently, we use the SIS (susceptible–infectious–susceptible) model to describe the information propagation process in the aggregation-phenomenon hypernetwork. In addition, we establish the relationship between the density of informed nodes and the structural parameters of the hypernetwork in a steady state using the mean field theory. Notably, modifications to the clustering coefficients do not impact the hyperdegree distribution; however, an increase in the clustering coefficients results in a reduced speed of information dissemination. It is further observed that the model can degenerate to a BA (Barabási–Albert) hypernetwork by setting the clustering coefficient to zero. Thus, the aggregation-phenomenon hypernetwork is an extension of the BA hypernetwork with stronger applicability. Full article

Innate immune pathways are tightly regulated to balance an appropriate response to infectious agents and tolerable levels of inflammation. Dysregulation of innate immune pathways can lead to severe autoinflammatory disorders or susceptibility to infections. Here, we aimed to identify kinases in common cellular pathways that regulate innate immune pathways by combining small-scale kinase inhibitor screening with quantitative proteomics. We found that inhibitors of kinases ATM, ATR, AMPK, and PLK1 reduced the induction of interferon-stimulated gene expression in response to innate immune pathway activation by poly(I:C) transfection. However, siRNA depletion of these kinases did not validate findings with kinase inhibitors, suggesting that off-target effects may explain their activities. We mapped the effects of kinase inhibitors to various stages in innate immune pathways. Determining the mechanisms by which kinase inhibitors antagonize these pathways may illuminate novel mechanisms of innate immune pathway control. Full article

Both the suddenness and seriousness of COVID-19 have caused a variety of public opinions on social media, which becomes the focus of social attention. This paper aims to analyze the strategies regarding the prevention and guidance of public opinion spread under COVID-19 in social networks from the perspective of the emotional characteristics of user texts. Firstly, a model is established to mine text-based emotional tendency based on the Susceptible-Infectious-Susceptible (SIS) model. In addition, a mathematical and simulation analysis of the model is presented. Finally, an empirical study based on the data of microblog contents regarding COVID-19 public opinion in the Sina Weibo platform from January to March 2020 is conducted to analyze the factors that boost and hinder COVID-19 public opinion. The results show that when positive emotion is higher than 0.8, the spread of negative public opinion can be blocked. When the negative emotion and neutral emotion are both below 0.2, the spread of COVID-19 public opinion would be weakened. To accurately guide public opinion on COVID-19, the government authorities should establish a public opinion risk evaluation and an early warning mechanism. Platforms should strengthen public opinion supervision and users should improve their media literacy. The media organizations should insist on positive reporting, improve social cohesion, and guide the trend of public opinion. Full article

Prion disorders, or transmissible spongiform encephalophaties (TSE), are fatal neurodegenerative diseases affecting mammals. Prion-infectious particles comprise of misfolded pathological prion proteins (PrP^TSE). Different TSEs are associated with distinct PrP^TSE folds called prion strains. The high resistance of prions to conventional sterilization increases the risk of prion transmission in medical, veterinary and food industry practices. Recently, we have demonstrated the ability of disulfonated hydroxyaluminum phthalocyanine to photodynamically inactivate mouse RML prions by generated singlet oxygen. Herein, we studied the efficiency of three phthalocyanine derivatives in photodynamic treatment of seven mouse adapted prion strains originating from sheep, human, and cow species. We report the different susceptibilities of the strains to photodynamic oxidative elimination of PrP^TSE epitopes: RML, A139, Fu-1 > mBSE, mvCJD > ME7, 22L. The efficiency of the phthalocyanine derivatives in the epitope elimination also differed (AlPcOH(SO₃)₂ > ZnPc(SO₃)_1-3 > SiPc(OH)₂(SO₃)_1-3) and was not correlated to the yields of generated singlet oxygen. Our data suggest that the structural properties of both the phthalocyanine and the PrP^TSE strain may affect the effectiveness of the photodynamic prion inactivation. Our finding provides a new option for the discrimination of prion strains and highlights the necessity of utilizing range of prion strains when validating the photodynamic prion decontamination procedures. Full article

In order to quantify projections of disease burden and to prioritise disease control strategies in the animal population, good mathematical modelling of infectious disease dynamics is required. This article investigates the suitability of discrete-time Markov chain (DTMC) as one such model for forecasting disease burden in the Norwegian pig population after the incursion of influenza A(H1N1)pdm09 virus (H1N1pdm09) in Norwegian pigs in 2009. By the year-end, Norway’s active surveillance further detected 20 positive herds from 54 random pig herds, giving an estimated initial population prevalence of 37% (95% CI 25–52). Since then, Norway’s yearly surveillance of pig herd prevalence has given this study 11 years of data from 2009 to 2020 to work with. Longitudinally, the pig herd prevalence for H1N1pdm09 rose sharply to >40% in three years and then fluctuated narrowly between 48% and 49% for 6 years before declining. This initial longitudinal pattern in herd prevalence from 2009 to 2016 inspired this study to test the steady-state discrete-time Markov chain model in forecasting disease prevalence. With the pig herd as the unit of analysis, the parameters for DTMC came from the initial two years of surveillance data after the outbreak, namely vector prevalence, first herd incidence and recovery rates. The latter two probabilities formed the fixed probability transition matrix for use in a discrete-time Markov chain (DTMC) that is quite similar to another compartmental model, the susceptible–infected–susceptible (SIS) model. These DTMC of predicted prevalence (DTMCP) showed good congruence (Pearson correlation = 0.88) with the subsequently observed herd prevalence for seven years from 2010 to 2016. While the DTMCP converged to the stationary (endemic) state of 48% in 2012, after three time steps, the observed prevalence declined instead from 48% after 2016 to 25% in 2018 before rising to 29% in 2020. A sudden plunge in H1N1pdm09 prevalence amongst Norwegians during the 2016/2017 human flu season may have had a knock-on effect in reducing the force of infection in pig herds in Norway. This paper endeavours to present the discrete-time Markov chain (DTMC) as a feasible but limited tool in forecasting the sequence of a predicted infectious disease’s prevalence after it’s incursion as an exotic disease. Full article