Spreading Model Research Articles

Risk analysis and resilience assessment of China's oil imports after the Ukraine Crisis：A network-based dynamics model

Global oil trade integration while creating a platform for risk spread. This study established a two-stage model for risk spread and recovery, simulating the cascading impact of oil supply shortage risks. On this basis, an assessment of the resilience of China's oil imports is conducted. The results indicate that, during the risk spread phase, countries causing a significant negative impact on China's oil imports may not necessarily be China's primary import sources. The proportion of indirect losses in Chinese oil imports due to Canadian oil supply shortages accounted for more than 70 % of the total losses. The United States, Singapore, Australia, the United Kingdom, and Brazil repeatedly act as intermediaries in risk spread. For the recovery phase, swiftly restoration of China's initial imports needs to establish new trade relationships with other non-traditional partners through competition. The top three countries in terms of accumulated new trade relationships are Korea (21), Turkmenistan (13), and Mexico (4). Regarding resilience, when the risk originates from Saudi Arabia, China demonstrates the weakest risk resistance, followed by Russia and Iran. The results not only serve as an early warning for China on regional oil supply risks but also provide policy directions for securing oil stable supply.

Variance of the Infection Number of Heterogeneous Malware Spread in Network

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.

A Bayesian spatio-temporal model of COVID-19 spread in England

Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005–1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024–1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129–1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009–1.0036], percentage of adults aged 45–64 years old [RR = 1.0031, 95% CI 1.0024–1.0039], and particulate matter (PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {PM}_{2.5}$$\\end{document}) concentrations [RR = 1.0126, 95% CI 1.0083–1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

On how structures convey non-diffusive turbulence spreading

We report on comprehensive experimental studies of turbulence spreading in edge plasmas. These studies demonstrate the relation of turbulence spreading and entrainment to intermittent convective density fluctuation events or bursts (i.e. blobs and holes). The non-diffusive character of turbulence spreading is thus elucidated. The turbulence spreading velocity (or mean jet velocity) manifests a linear correlation with the skewness of density fluctuations, and increases with the auto-correlation time of density fluctuations. Turbulence spreading by positive density fluctuations is outward, while spreading by negative density fluctuations is inward. The degree of symmetry breaking between outward propagating blobs and inward propagating holes increases with the amplitude of density fluctuations. Thus, blob-hole asymmetry emerges as crucial to turbulence spreading. These results highlight the important role of intermittent convective events in conveying the spreading of turbulence, and constitute a fundamental challenge to existing diffusive models of spreading.

Integrating Real-Time Meteorological Conditions into a Novel Fire Spread Model for Grasslands

Accurate comprehension of grassland fires is imperative for maintaining ecological stability. In this study, we propose a novel fire model that incorporates real-time meteorological conditions. Our methodology integrates key meteorological factors including relative humidity, temperature, degree of solidification of combustible materials, and wind speed. These factors are embedded into a comprehensive function that determines both the downwind and upwind spreading speeds of the fire. Additionally, the model accommodates fire spread in the absence of wind by incorporating the direction perpendicular to the wind, with wind speed set to zero. By precisely determining wind speed, the model enables real-time calculation of fire spread speeds in all directions. Under stable wind conditions, the fire spread area typically adopts an elliptical shape. Leveraging ellipse properties, we define the aspect ratio as a function related to wind speed. Consequently, with knowledge of the fire duration, the model accurately estimates the area of fire spread. Our findings demonstrate the effectiveness of this model in predicting and evaluating fires in the Hulunbuir Grassland. The model offers an innovative method for quantifying grassland fires, contributing significantly to the understanding and management of grassland ecosystems.

Modelling Plasmid-Mediated Horizontal Gene Transfer in Biofilms

In this study, we present a mathematical model for plasmid spread in a growing biofilm, formulated as a nonlocal system of partial differential equations in a 1-D free boundary domain. Plasmids are mobile genetic elements able to transfer to different phylotypes, posing a global health problem when they carry antibiotic resistance factors. We model gene transfer regulation influenced by nearby potential receptors to account for recipient-sensing. We also introduce a promotion function to account for trace metal effects on conjugation, based on literature data. The model qualitatively matches experimental results, showing that contaminants like toxic metals and antibiotics promote plasmid persistence by favoring plasmid carriers and stimulating conjugation. Even at higher contaminant concentrations inhibiting conjugation, plasmid spread persists by strongly inhibiting plasmid-free cells. The model also replicates higher plasmid density in biofilm’s most active regions.

Optimal Control of Transmission Dynamics of Meningitis Disease with Vaccination, Campaign, and Treatment Factors

Meningitis disease is an inflammation of the membranes that protect the brain and spinal cord. This disease can be fatal to life-threatening because symptoms can appear suddenly. Meningitis is also common in infants and young children and can cause complications if not treated promptly. To prevent infection of susceptible compartment and to speed recovery of infected compartments, vaccination, campaign, and treatment are needed to prevent the spread of the disease or at least reduce the number of carrier and infected individuals. Therefore, we propose a model of meningitis disease spread consisting of five compartments, namely susceptible, carrier, infected with and without symptoms, and recovered. Vaccination, campaign and treatment are included in the model mechanism as controls. This study aims to observe the effectiveness of vaccination, campaign, and treatment in inhibiting the spread of the disease and accelerating the recovery of infected individuals. The Pontryagin minimum principle is followed to derive the optimal control problem. Numerical simulation using Runge-Kutta of order four scheme with the forward-backward sweep approach is applied to visualize the curves of state and control variables. From the numerical simulations, it is found that vaccination, campaign, and treatment are reasonable to apply in order to bridle meningitis disease from the population.

Comparing Accuracy of Wildfire Spread Prediction Models under Different Data Deficiency Conditions

Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect prediction accuracy, thereby providing rational suggestions for emergency decision-making. In this paper, we tested how different conditions of missing data affect the prediction accuracy of existing wildfire spread models and quantified the corresponding errors. The final experimental results suggest that it is necessary to judge the potential impact of data gaps based on the geographical conditions of the study area appropriately, as there is no significant pattern of behavior yet identified. This study aims to simulate the impact of data scarcity on the accuracy of wildfire spread prediction models in real scenarios, thereby enabling researchers to better understand the priority of different environmental variables for the model and identify the acceptable degree of missing data and the indispensable data attributes. It offers new insights for developing spread prediction models applicable to real-world scenarios and rational assessment of the effectiveness of model outcomes.

Mathematical modeling and optimal control of multi-strain COVID-19 spread in discrete time

This research article presents a mathematical model that tracks and monitors the spread of COVID-19 strains in a discrete time frame. The study incorporates two control strategies to reduce the transmission of these strains: vaccination and providing appropriate treatment and medication for each strain separately. Optimal controls were established using Pontryagin's maximum principle in discrete time, and the optimality system was solved using an iterative method. To validate the effectiveness of the theoretical findings, numerical simulations were conducted to demonstrate the impact of the implemented strategies in limiting the spread of COVID-19 mutant strains.

A new semi-local centrality for identifying influential nodes based on local average shortest path with extended neighborhood

Quantifying the importance of nodes in complex networks is known as the problem of identifying influential nodes and is considered a critical aspect in interacting with these networks. This problem has many applications such as controlling rumors, sickness spreading, and viral marketing, where its importance has been understood by the research society in the last decade. This paper proposes a new semi-local centrality to identify influential nodes in complex networks based on the theory of Local Average Shortest Path with extended Neighborhood concept (LASPN). LASPN focuses on a distributed technique to extract the subgraph associated with each node and apply the average shortest path theory to it. We use the extended neighborhood concept to find the nearest neighbors of each node with low complexity, where this can lead to high efficiency in dealing with large-scale networks. In addition to applying relative changes in the average shortest path, the proposed metric considers the importance of the node itself as well as its nearest neighbors in ranking the nodes. Evaluation of the proposed centrality metric has been done through numerical simulations on several real-world networks. The results based on Kendall's τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au$$\\end{document} coefficient under the SIR infection spreading model show that LASPN improves the performance by 2.7% compared to the best available equivalent method.

Turbulence spreading by resonant wave-wave interactions: A fractional kinetics approach.

This paper is concerned with the processes of spatial propagation and penetration of turbulence from the regions where it is locally excited into initially laminar regions. The phenomenon has come to be known as "turbulence spreading" and witnessed a renewed attention in the literature recently. Here, we propose a comprehensive theory of turbulence spreading based on fractional kinetics. We argue that the use of fractional-derivative equationspermits a general approach focusing on fundamentals of the spreading process regardless of a specific turbulence model and/or specific instability type. The starting point is the Hamiltonian of resonant wave-wave interactions, from which a family of scaling laws for the asymptotic spreading is derived. Both three- and four-wave interactions are considered. The results span from a subdiffusive spreading in the parameter range of weak chaos to avalanche propagation in regimes with population inversion. Attention is paid to how nonergodicity introduces weak mixing, memory and intermittency into spreading dynamics, and how the properties of non-Markovianity and nonlocality emerge from the presence of islands of regular dynamics in phase space. Also we resolve an existing question concerning turbulence spillover into gap regions, where the instability growth is locally suppressed, and show that the spillover occurs through exponential (Anderson-like) localization in case of four-wave interactions and through an algebraic (weak) localization in case of triad interactions. In the latter case an inverse-cubic behavior of the spillover function is found. Wherever relevant, we contrast our findings against the available observational and numerical evidence, and we also commit ourselves to establish connections with the models of turbulence spreading proposed previously.

Australian Fire Danger Rating System: implementing fire behaviour calculations to forecast fire danger in a research prototype†

Background The Australian Fire Danger Rating System (AFDRS) was implemented operationally throughout Australia in September 2022, providing calculation of fire danger forecasts based on peer-reviewed fire behaviour models. The system is modular and allows for ongoing incorporation of new scientific research and improved datasets. Aims Prior to operational implementation of the AFDRS, a Research Prototype (AFDRSRP), described here, was built to test the input data and systems and evaluate the performance and potential outputs. Methods Fire spread models were selected and aligned with fuel types in a process that captured bioregional variation in fuel characteristics. National spatial datasets were created to identify fuel types and fire history in alignment with existing spatial weather forecast layers. Key results The AFDRSRP demonstrated improvements over the McArthur Forest and Grass Fire Danger systems due to its use of improved fire behaviour models, as well as more accurately reflecting the variation in fuels. Conclusions The system design was robust and allowed for the incorporation of updates to the models and datasets prior to implementation of the AFDRS.

Forest fire spreading: A nonlinear stochastic model continuous in space and time

AbstractForest fire spreading is a complex phenomenon characterized by a stochastic behavior. Nowadays, the enormous quantity of georeferenced data and the availability of powerful techniques for their analysis can provide a very careful picture of forest fires opening the way to more realistic models. We propose a stochastic spreading model continuous in space and time that has the potentiality to use such data in their full power. The state of the forest fire is described by the subprobability densities of the green trees and of the trees on fire that can be estimated thanks to data coming from satellites and earth detectors. The fire dynamics is encoded into a kernel function that can take into account wind conditions, land slope, spotting phenomena, and so on, bringing to a system of integrodifferential equations whose solutions provide the evolution in time of the subprobability densities. That makes the model complementary to models based on cellular automata that furnish single instantiations of the stochastic phenomenon. Moreover, stochastic models based on cellular automata can be derived from the present model by space and time discretization. Existence and uniqueness of the solutions is proved by using Banach's fixed‐point theorem. The asymptotic behavior of the model is analyzed as well. By specifying a particular structure for the kernel, we obtain numerical simulations of the fire spreading under different conditions. For example, in the case of a forest fire evolving toward a river, the simulations show that the probability density of the trees on fire is different from zero beyond the river due to the spotting phenomenon. The kernel could be slightly modified to include firefighters interventions and weather changes.

SIHQR model with time delay for worm spread analysis in IIoT-enabled PLC network

Industrial Internet of Things (IIoT) is a product of deep integration between Internet of Things (IoT) and industrial control networks. As an important component of IIoT, Programmable Logic Controller (PLC) has become a springboard for many hackers to disrupt industrial networks by spreading viruses. It is known that worm can cause enormous loss. Therefore, revealing the rules of worm spread in PLC networks and exploring methods to inhibit worm spread are increasingly important. To this end, we propose a new worm spread model named SIHQR with time delay. Additionally, we establish a game model between susceptible and infected nodes in order to express the infection rate by several game parameters. Through analysis, we discuss the conditions for the emergence of three scenarios: disease-free equilibrium, endemic equilibrium and Hopf bifurcation. We study the stability at the equilibrium points and obtain an expression for a threshold value which determines whether the system exhibits endemic equilibrium or Hopf bifurcation. In the experimental section, we analyze the impact of initial values of node states and the magnitude of game parameters on worm spread in the three aforementioned scenarios. Finally, we propose a method to mitigate or even eliminate the Hopf bifurcation. The experimental results show that the proposed model has welldone performance.

Building Models in Pairs for Cross‐Verification Using SDL and DEVS

AbstractThe objective of the paper is to present a methodology that can be used to translate a model from one formalism to another allowing model reuse and cross‐verification. With the use of formal languages, the model specifications can be expressed in a rigorous and univocal way, and the model can be validated against the specifications or conceptual model. Expressing the same model in different formal languages opens the conceptual model validation to a varied number of specialists. Also, in the context of modeling a new system, where there is no data to perform validation against a real system, having pairs of models can be used to perform cross‐model verification to ensure that the model specifications or conceptual models are correctly implemented by comparing the results produced by both models. Specifically, a method is presented to translate a model conceptualized on specification and description language (SDL) to discrete event system specification (DEVS). The transformation mechanism between SDL and DEVS formalisms is described. The methodology is exemplified with a disease spread model for COVID‐19, and it is shown how the results obtained by the two models can be used for cross‐verification.

A Fireline Displacement Model to Predict Fire Spread

Most current surface fire simulators rely upon Rothermel’s model, which considers the local properties of fuel, topography, and meteorology to estimate the rate of spread, and utilises the concept of elliptical growth to predict the evolution of the fire perimeter throughout time. However, the effects of convective processes near the fireline, which modify fire spread conditions along the fire perimeter, are not considered in this model. An innovative fire prediction simulator based on the concept of fireline element displacement, which is composed of translation, rotation, and extension, rather than a point-by-point displacement, is proposed in this article. Based on the laws of convective heat fluxes across and along the fireline and on laboratory experiments, models to estimate the angular rotation velocity and the extension of the fireline during its displacement are proposed. These models are applied to a set of laboratory experiments of point ignition fires on slopes of 30° and 40° and, given the fact that the rate of spread of the head, back, and flank fire are known, the evolution of the fire perimeter can be predicted. The fire spread model can be applied to other situations of varying boundary conditions provided that the parameters required by the model are known.

Research on information spreading of SISIOIN model in social networks

This paper focuses on the internal law of online public opinion spreading under the conditions of different types of spreaders by constructing the model of public opinion spreading by subtypes of spreaders. The new [Formula: see text] model is constructed by abstracting factors such as netizens’ interests, emotions and views on hot events in the Internet into propensity indices. The simulation results show that the spread of public opinion after categorizing netizens is more in line with the spread of public opinion in reality. Moreover, netizens tend to show different spreading behaviors in different public opinion cases, and the degree of change in netizens’ propensity varies greatly. All the data used in the simulation experiments are real data and analyzed with real events. The research results are helpful for what kind of control strategies are adopted after the occurrence of hotspot events.

Partial differential equation models for invasive species spread in the presence of spatial heterogeneity.

Models of invasive species spread often assume that landscapes are spatially homogeneous; thus simplifying analysis but potentially reducing accuracy. We extend a recently developed partial differential equation model for invasive conifer spread to account for spatial heterogeneity in parameter values and introduce a method to obtain key outputs (e.g. spread rates) from computational simulations. Simulations produce patterns of spatial spread which appear qualitatively similar to observed patterns in grassland ecosystems invaded by exotic conifers, validating our spatially explicit strategy. We find that incorporating spatial variation in different parameters does not significantly affect the evolution of invasions (which are characterised by a long quiescent period followed by rapid evolution towards to a constant rate of invasion) but that distributional assumptions can have a significant impact on the spread rate of invasions. Our work demonstrates that spatial variation in site-suitability or other parameters can have a significant impact on invasions and must be considered when designing models of invasive species spread.

Banach spaces with the Lebesgue property of Riemann integrability

A Banach space is said to have the Lebesgue property if every Riemann-integrable function f:[0,1]→X is Lebesgue almost everywhere continuous. We give a characterization of the Lebesgue property in terms of a new sequential asymptotic structure that is strictly between the notions of spreading and asymptotic models. We also reproduce an apparently lost theorem of Pelczynski and da Rocha Filho that a subspace X⊂L1[0,1] has the Lebesgue property if every spreading model of X is equivalent to the unit vector basis of ℓ1.

Agent-based perspectives on epidemiological models: analysis of interviews with upper high-school students

In disease spread modeling, a prevalent approach employs differential equations to depict the dynamics of susceptible, infectious, and recovered populations over time. Nonetheless, alternative avenues exist through agent-based epidemiological models, drawing inspiration from interaction models from the physics of complex systems. This study delves into the formulation of such models by upper high-school students who attended a teaching-learning module on computational simulations. The paper focuses on their development of agent-based virus spread models, exploring their ability to forge analogies with previously encountered models of complex systems - namely, predator-prey, opinion dynamics, and cooperative behaviour models. Through the qualitative analysis of individual interviews, our findings reveal that effective strategies of analogy’s construction embed a comprehensive exploration of the underlying interaction mechanisms governing the evolution of the system under study. Conversely, in instances where the mechanistic dimension remains unexplored or vague, the depth and quality of the model elaborated is lower and the potential of comparing models to construct a more robust analogy remains unexploited.