Spreading Model Research Articles

Hot topics with decaying attention in social networks: Modeling and analysis of message spreading

Hot topics with decaying attention in social networks: Modeling and analysis of message spreading

Well-posedness and stability analysis of an epidemic model with infection age and spatial diffusion

A compartment epidemic model for infectious disease spreading is investigated, where movement of individuals is governed by spatial diffusion. The model includes infection age of the infected individuals and assumes a logistic growth of the susceptibles. Global well-posedness of the equations within the class of nonnegative smooth solutions is shown. Moreover, spectral properties of the linearization around a steady state are derived. This yields the notion of linear stability which is used to determine stability properties of the disease-free and the endemic steady state.

Mathematical Model for Spread and Control of Cocoa Black Pod Disease

Black pod disease is a major threat to cocoa production worldwide. A mathematical model for the spread of cacao black pod disease is presented in this article. The model takes into account several variables that influence the spread of the disease. A set of differential equations that are numerically solved using Runge-Kutta method embedded in MATLAB software are used to simulate the dynamics of disease transmission which form the basis of the model. Utilizing information from the literature and ecological observations from cocoa fields in West Africa, where black pod disease poses a serious threat to the production of cocoa, the model was verified. The model's outcomes highlight the significance of early detection and rapid intervention in mitigating the severity of cocoa black pod disease outbreaks. Moreover, it emphasizes the importance of adopting integrated disease management approaches that consider fungicide administration and removal of infected pods. The usefulness of mathematical modeling as a tool for understanding and managing cocoa black pod disease is illustrated by this study.

Fire From Volcanic Activity: Quantifying the threat from an understudied hazard

Natural hazards can trigger fires that increase the area and severity of impacts beyond that caused by the initiating hazard. For some physical processes, such as earthquake, fires are a well appreciated and studied cascading hazard. However, fire from volcanic activity (FFVA) is a highly dangerous and largely understudied hazard arising from volcanic activity. To demonstrate the potential destructiveness of this hazard, we develop a FFVA ignition probability model underpinned by a widely applicable fault tree, which identifies the mechanisms that can lead to fire ignition from volcanic activity. By assigning values to each node of the fault tree, our model can be used to consider the relative probabilities associated with different fire ignition mechanisms. We couple this ignition probability model with a fire spread model to evaluate hazardous areas and associated impacts caused by FFVA. To demonstrate the applicability of our model, we use an eruption scenario for volcanic ballistic projectiles in the Auckland Volcanic Field (Aotearoa New Zealand). This case study demonstrates the potential for FFVA to compound and greatly increase the impacts caused by other volcanic hazards. We found that fire ignition and spread could increase the damaged area and monetary losses associated with the initiating hazard alone by more than six and five times, respectively. We suggest that more study is needed to better understand, evaluate, and plan for FFVA.

Stochastic control methods for optimization problems in Ornstein-Uhlenbeck spread models

We develop stochastic optimal control methods for spread financial models defined by the Ornstein–Uhlenbeck (OU) processes. To this end, we study the Hamilton - Jacobi - Bellman (HJB) equation using the Feynman - Kac (FK) probability representation. We show an existence and uniqueness theorem for the classical solution of the HJB equation, a quasi-linear partial derivative equation of parabolic type. Then we show a special verification theorem and, as a consequence, construct optimal consumption/investment strategies for power utility functions. Moreover, using fixed point tools we study the numeric approximation for the HJB solution and we establish the convergence rate which, as it turns out in this case, is super geometric, i.e., more rapid than any geometric one. Finally, we illustrate numerically the behavior of the obtained strategies.

Spatial distribution and ecological niche modeling of geographical spread of Anopheles gambiae complex in Nigeria using real time data

The need for evidence-based data, to inform policy decisions on malaria vector control interventions in Nigeria, necessitated the establishment of mosquito surveillance sites in a few States in Nigeria. In order to make evidence-based-decisions, predictive studies using available data becomes imperative. We therefore predict the distribution of the major members of the Anopheles gambiae s.l. in Nigeria. Immature stages of Anopheles were collected from 72 study locations which span throughout the year 2020 resulted in the identification of over 60,000 Anopheline mosquitoes. Of these, 716 breeding sites were identified with the presence of one or more vector species from the An. gambiae complex and were subsequently used for modelling the potential geographical distribution of these important malaria vectors. Maximum Entropy (MaxEnt) distribution modeling was used to predict their potentially suitable vector habitats across Nigeria. A total of 23 environmental variables (19 bioclimatic and four topographic) were used in the model resulting in maps of the potential geographical distribution of three dominant vector species under current climatic conditions. Members of the An. gambiae complex dominated the collections (98%) with Anopheles stephensi, Anopheles coustani, Anopheles funestus, Anopheles moucheti, Anopheles nilli also present. An almost equal distribution of the two efficient vectors of malaria, An. gambiae and Anopheles coluzzii, were observed across the 12 states included in the survey. Anopheles gambiae and Anopheles coluzzii had almost equal, well distributed habitat suitability patterns with the latter having a slight range expansion. However, the central part of Nigeria (Abuja) and some highly elevated areas (Jos) in the savannah appear not suitable for the proliferation of these species. The most suitable habitat for Anopheles arabiensis was mainly in the South-west and North-east. The results of this study provide a baseline allowing decision makers to monitor the distribution of these species and establish a management plan for future national mosquito surveillance and control programs in Nigeria.

Modelling the influences of powder layer depth and particle morphology on powder bed fusion using a coupled DEM-CFD approach

Optimising the quality of metal parts produced by additive manufacturing requires an understanding of how the powder characteristics impact both layer spreading and the subsequent powder melting. Here we present a simulation study which couples models of powder spreading (by the Discrete Element Method) and powder bed fusion by laser beam (by CFD). Previous simulations of these processes have mostly assumed idealised smooth surfaces, spherical particles and a single laser track. Here we seek a more realistic description by spreading over a previously-melted (rough) surface and by melting a number of tracks laid side-by-side to mimic the crosshatched scans typically used during part production. The effects of powder morphology (via a range of particle shapes including spheres, disks, ellipsoids and cuboids) and layer depth on spreading and subsequent melting have been investigated. We find that the fraction of laser energy transferred to the part and the melt pool volume both increase rapidly with the volume of powder deposited, due to the combined effects of multiple laser reflections and the insulating nature of the powder layer. In contrast, particle shape has very little effect on the overall melting behaviour of the powder, with the volume deposited and uniformity of coverage being the key determining factors. These observations suggest that the use of cheaper non-spherical powders is feasible provided that a sufficiently uniform coverage can be achieved at small layer depths.

Epidemic spreading dynamics on two-layer complex networks

Recent studies have shown that many real-world systems can be described by multi-layer complex networks. In this paper, the concept of layers is introduced to construct a traffic-driven SIR epidemic spreading model on “logical-physical” layered network. Based on the peak density of infected nodes and the ultimate density of recovered nodes, we investigate the features of epidemic spreading on layered network. Through numerical simulations, it is shown that traffic flow greatly influences the intensity and scope of epidemic spreading. By comparing the effects of four kinds of two-layer networks generated by ER random network model and BA scale-free network model on epidemic spreading, we found that the homogeneity of logical or physical network structure can promote the spread of epidemic more than heterogeneous networks. This work may be of service to design traffic-driven epidemic prevention and control strategies.

离心圆盘式有机肥撒施机改进设计与仿真分析

Aiming at the poor efficiency in crushing a handful of caking organic fertilizers spraying from the traditional disc spreader, an improved disc spreader equipped with spike-tooth crushing unit was designed with the introduction of its structural composition and working principle. Also simulation experiments were carried out based on the discrete element method according to the solutions made before and after the structural improvement. Firstly, a granular organic fertilizer model was established based on the EDEM and a caking organic fertilizer bonding model was built based on the HMB (Hertz-Mindlin with bonding) contact model. Then two organic fertilizer spreading models corresponding to the solutions were respectively established, based on which simulation experiments were repeated three times on the spreading process. The analysis on the particle velocity vector diagram revealed the interaction relationship between organic fertilizers with the toothed shaft, external wall and disk, verifying the crushing mechanism of crushing unit. By calculating the total number of the bonds generated and broken in the spreading process in both of the spreading models, the average broken rate of bonds in the spreading process was separately 58.87% and 98.05% based on each solution, revealing that the improved solution outperformed the traditional solution in terms of the efficiency in crushing the caking organic fertilizers. This research will be a reference in designing the critical components or improving the overall performance of disc spreader.

The Impact of Government Interventions on COVID-19 Spread and Consumer Spending

We examine the impact of government interventions on the spread of COVID-19 and consumer spending. We do this by first estimating models of COVID-19 spread, consumer spending, and social distancing in the United States during the early stages of the COVID-19 pandemic. Social distancing has a large effect on reducing COVID-19 spread and is responsive to national and local case numbers. Nonmask government interventions reduce COVID-19 spread, whereas the effectiveness of mask mandates is much smaller and statistically insignificant. Mask mandates tend to increase social distancing, as do nonmask governmental restrictions as a whole. Social distancing hurts spending in the absence of a mask mandate but has a negligible effect on spending if there is a mask mandate. Mask mandates have a direct effect of increasing spending in counties with high levels of social distancing while reducing spending in counties with low levels of social distancing. We use these three estimated models to calculate the effect of mask mandates and other governmental interventions on COVID-19 cases, deaths, and consumer spending. Implemented mask mandates decreased COVID-19 cases by a statistically insignificant 774,000 cases, saving 28,000 lives, over a four-month period, but led to $76B–$155B of additional consumer spending. Other nonmask governmental interventions that were implemented reduced the number of COVID-19 cases by 34M, saving 1,230,000 lives, while reducing consumer spending by approximately $470B–$703B over our 4-month period of the study. Thus, these restrictions were cost effective as long as one values each saved life at $387,000–$608,000 or more. This paper was accepted by Matt Shum, marketing. Supplemental Material: The data is available at https://doi.org/10.1287/mnsc.2023.4853 .

Assessment of a reconfiguration of the InterSpread Plus US national FMD model as a potential tool to analyze a foot-and-mouth disease outbreak on a single large cattle feedlot in the United States.

An incursion of foot-and-mouth disease (FMD) into the United States remains a concern of high importance and would have devastating socioeconomic impacts to the livestock and associated industries. This highly transmissible and infectious disease poses continual risk for introduction into the United States (US), due to the legal and illegal global movement of people, animals, and animal products. While stamping out has been shown to effectively control FMD, depopulation of large cattle feedlots (>50,000 head) presents a number of challenges for responders due to the resources required to depopulate and dispose of large numbers of animals in a timely and effective manner. However, evaluating alternative strategies for FMD control on large feedlots requires a detailed within-farm modeling approach, which can account for the unique structure of these operations. To address this, we developed a single feedlot, within-farm spread model using a novel configuration within the InterSpread Plus (ISP) framework. As proof of concept we designed six scenarios: (i) depopulation - the complete depopulation of the feedlot, (ii) burn-through - a managed "burn-through" where the virus is allowed to spread through the feedlot and only movement restriction and biosecurity are implemented, (iii) firebreak-NV - targeted depopulation of infected pens and adjacent pens without vaccination; (iv) firebreak - targeted depopulation of infected pens and adjacent pens with vaccination of remaining pens; (v) harvest-NV - selective harvest of pens where a 100% movement restriction is applied for 28-30 days, then pens are set for selection to be sent to slaughter, while allowing a controlled "burn-through" without vaccination; and (vi) harvest - selective harvest of pens with vaccination. Overall, the burn-through scenario (ii) had the shortest epidemic duration (31d (30, 33)) median (25th, 75th percentiles), while the firebreak scenario (iv) had the longest (47d (38,55)). Additionally, we found that scenarios implementing depopulation delayed the peak day of infection and reduced the total number of pens infected compared to non-depopulation scenarios. This novel configuration of ISP provides proof of concept for further development of this new tool to enhance response planning for an incursion of FMD in the US and provides the capability to investigate response strategies that are designed to address specific outbreak response objectives.

SHI1I2R competitive information spreading model in online and offline two-layer networks in emergencies

Previous research on information propagation has primarily focused on either online social networks or offline social networks. Additionally, there remains a gap in understanding the propagation of competitive information in emergency scenarios. This paper introduces an SHI1I2R information spreading model to investigate the dynamics of competitive information dissemination and interactions in online and offline two-layer networks during emergencies. To enhance the accuracy of information propagation modeling, we depart from the conventional assumption of constant spreading and decay rates and instead propose nonlinear spreading and interest decay rates within the SHI1I2R model. Employing a microscopic Markov chain approach, we conduct theoretical analysis and derive the spreading threshold of information in two-layer networks. Subsequently, we perform numerical simulations to validate the effectiveness of the SHI1I2R model and explore the factors influencing information propagation. Our findings indicate that information spreads more rapidly and extensively in two-layer networks compared to one-layer networks. Furthermore, the final scale of information dissemination and the speed of propagation follow the order: random network > small-world network > scale-free network > regular network.

The potential impact of banana Xanthomonas wilt on food systems in Africa: modeling scenarios of policy response and disease control measures

Banana Xanthomonas wilt (BXW) is one of the most important diseases threatening banana production in Africa south of the Sahara (SSA). In this study, we examine the potential impacts of BXW on banana production, demand, and food security in SSA, if the disease spread across all banana-producing countries in the region. The analysis is based on a multidisciplinary approach that combines a mathematical model of field-level BXW spread over time with a dynamic global partial equilibrium economic model. Since BXW control relies exclusively on management, we analyze three scenarios of BXW spread that are constructed around assumptions about the level of policy response to the disease, and about how this response may affect the adoption of appropriate management practices by farmers to control BXW. Modeling results suggest that if the disease is left uncontrolled, banana production in SSA within 10 years can decrease by as much as 55%, compared to a BXW-free baseline scenario, resulting in economic losses of around 25 billion USD. At the same time, the population at risk of hunger in countries that highly depend on bananas as a staple food is projected to increase by more than 4.6%. Even a limited policy response to BXW can reduce infections and mitigate some of the production, economic, and food security consequences. BXW impacts are almost completely negated when farmers have good knowledge of the disease and fully adopt the appropriate management practices. This result highlights the need for policy frameworks which rely on sustained and coordinated efforts by public and private stakeholders, within and across SSA countries and at different geographical scales. It also aims to raise awareness and promote the adoption of such practices, while also considering local peculiarities and socioeconomic conditions.

A swarming neural network computing approach to solve the Zika virus model

In this work, a swarming computational procedure is presented for the numerical treatment of the dynamical model of the susceptible, exposed, infected, and recovered (SEIR) classes that portrayed the spreading of Zika virus. The artificial neural network procedures (ANNPs) have been applied to solve the SEIR mathematical model for spreading of the Zika virus together with the hybridization efficiency of global swarming and local search schemes. The global particle swarm optimization (PSO) and local search active-set algorithm (ASA) have been proposed to solve the model. An error based objective function is presented for the SEIR differential model and then optimized by the hybrid computing efficiency of PSO-ASA. Five neurons, fifteen variables of each class and ten numbers of trials have been used to solve the SEIR mathematical model for spreading of the Zika virus. The correctness of the proposed computing ANNPs-PSO-ASA is observed by using the comparison of the obtained and reference solutions along with the performances of the absolute error, ranges around 10−06 to 10−08. The reliability of the designed computing ANNPs-PSO-ASA technique is observed by using the statistical operator performances on single/multiple trials for the SEIR system for spreading of the Zika virus dynamics.

Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread

BackgroundBayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important.MethodsIn this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons.ResultsA particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall.DiscussionThe models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall ‘best’ model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality.ConclusionsFrom a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance.In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.

Studying the Dynamics of the Rumor Spread Model with Fractional Piecewise Derivative

Sensitively altered news, commonly referred to as rumors, can lead an individual, organization, or nation astray, potentially resulting in harm, even to the extent of causing violence among large groups of people. In this digital age, news can be easily twisted and rapidly spread through the internet and social media. It becomes challenging for consumers to discern whether the information they encounter online has been manipulated. Unfortunately, the rise of internet forgeries has facilitated the dissemination of false or distorted information by unscrupulous individuals, particularly on sensitive matters, to serve their own interests. Once a rumor is generated and made public on the internet, it quickly spreads through sharing and discussions by anonymous individuals, sometimes intentionally, without thorough fact-checking. In this manuscript, we investigate the dynamical model of rumor propagation in a social network using the classical Caputo piecewise derivative. We examine the existence and uniqueness of a solution for the aforementioned problem and analyze the equilibrium, stability, boundedness, and positivity of the model. To obtain the numerical simulation of the piecewise derivative, we employ various fractional orders, and the approximate solution of the considered model is found using the fractional piecewise numerical iterative approach of the Newton polynomial. This approach allows us to gain valuable insights into the dynamics of rumor propagation and its effects within a social network.

Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model

Objectives. COVID-19 has a number of specific characteristics that distinguish it from past pandemics. In addition to the high infection rate, the high spread rate is due to the increased mobility of contemporary populations. The aim of the present work is to construct a mathematical model for the spread of the pandemic and identify patterns under the assumption that Moscow comprises the main source of viral infection in Russia. For this purpose, a twoparameter kinetic model describing the spatial spread of the epidemic is developed. The parameters are determined using theoretical constructions alongside statistical vehicle movement and population density data from various countries, additionally taking into account the development of the first wave on the examples of Russia, Italy and Chile with verification of values obtained from subsequent epidemic waves. This paper studies the development of epidemic events in Russia, starting from the third and including the most recent fifth and sixth waves. Our twoparameter model is based on a kinetic equation. The investigated possibility of predicting the spatial spread of the virus according to the time lag of reaching the peak of infections in Russia as a whole as compared to Moscow is connected with geographical features: in Russia, as in some other countries, the main source of infection can be identified. Moscow represents such a source in Russia due to serving as the largest transport hub in the country.Methods. Mathematical modeling and data analysis methods are used.Results. A predicted time lag between peaks of daily infections in Russia and Moscow is confirmed. Identified invariant parameters for COVID-19 epidemic waves can be used to predict the spread of the disease. The checks were carried out for the wave sequence for which predictions were made about the development of infection for Russia and when the recession following peak would occur. These forecasts for all waves were confirmed from the third to the last sixth waves to confirm the found pattern, which can be important for predicting future events.Conclusions. The confirmed forecasts for the timing and rate of the recession can be used to make good predictions about the fifth and sixth waves of infection of the Omicron variant of the COVID-19 virus. Earlier predictions were confirmed by the statistical data.

Identifying sources of human interictal discharges with travelling wave and white matter propagation.

Interictal epileptiform discharges have been shown to propagate from focal epileptogenic sources as travelling waves or through more rapid white matter conduction. We hypothesize that both modes of propagation are necessary to explain interictal discharge timing delays. We propose a method that, for the first time, incorporates both propagation modes to identify unique potential sources of interictal activity. We retrospectively analysed 38 focal epilepsy patients who underwent intracranial EEG recordings and diffusion-weighted imaging for epilepsy surgery evaluation. Interictal discharges were detected and localized to the most likely source based on relative delays in time of arrival across electrodes, incorporating travelling waves and white matter propagation. We assessed the influence of white matter propagation on distance of spread, timing and clinical interpretation of interictal activity. To evaluate accuracy, we compared our source localization results to earliest spiking regions to predict seizure outcomes. White matter propagation helps to explain the timing delays observed in interictal discharge sequences, underlying rapid and distant propagation. Sources identified based on differences in time of receipt of interictal discharges are often distinct from the leading electrode location. Receipt of activity propagating rapidly via white matter can occur earlier than more local activity propagating via slower cortical travelling waves. In our cohort, our source localization approach was more accurate in predicting seizure outcomes than the leading electrode location. Inclusion of white matter in addition to travelling wave propagation in our model of discharge spread did not improve overall accuracy but allowed for identification of unique and at times distant potential sources of activity, particularly in patients with persistent postoperative seizures. Since distant white matter propagation can occur more rapidly than local travelling wave propagation, combined modes of propagation within an interictal discharge sequence can decouple the commonly assumed relationship between spike timing and distance from the source. Our findings thus highlight the clinical importance of recognizing the presence of dual modes of propagation during interictal discharges, as this may be a cause of clinical mislocalization.

Attention-Based Wildland Fire Spread Modeling Using Fire-Tracking Satellite Observations

Modeling the spread of wildland fires is essential for assessing and managing fire risks. However, this task remains challenging due to the partially stochastic nature of fire behavior and the limited availability of observational data with high spatial and temporal resolutions. Herein, we propose an attention-based deep learning modeling approach that can be used to learn the complex behaviors of wildfires across different fire-prone regions. We integrate optimized spatial and channel attention modules with a convolutional neural network (CNN) modeling architecture and train the attention-based fire spread models using a recently derived fire-tracking satellite observational dataset in conjunction with corresponding fuel, terrain, and weather conditions. The evaluation results and their comparison with benchmark models, such as a deeper and more complex autoencoder model and the semi-empirical FARSITE fire behavior model, demonstrate the effectiveness of the attention-based models. These new data-driven fire spread models exhibit promising modeling performances in both the next-step prediction (i.e., predicting fire progression from one timestep earlier) and recursive prediction (i.e., recursively predicting final fire perimeters from initial ignition points) of observed large wildfires in California, and they provide a foundation for further practical applications including short-term active fire spread prediction and long-term fire risk assessment.

SENPR disinformation spreading model to discuss the control of the spread of disinformation in emergencies

The spread of disinformation in online social networks (OSNs) poses a significant threat to maintaining normal social order. It is important to understand when and how intervention measures can control the spread of disinformation. We propose a novel information spreading model called susceptible, exposed, negative-infected, positive-infected, recovered population (SENPR) to describe the disinformation dynamics in OSNs under the intervention strategy. The model in this study considers that the susceptible population in the network is transformed into an exposed population through the influence of two information-dissemination methods that affect other susceptible people in the network. Subsequently, the stability of the model was analyzed to verify the validity of the obtained theoretical results. Finally, the influence of different parameters on the spread of disinformation is described, and corresponding management strategies to suppress the spread of false information in emergency situations are discussed. The results show that word-of-mouth is the main mode of communication in the “development” and “burst” stages of information spreading. The earlier the intervention is implemented, the sooner the number of people affected by word-of-mouth begins to decline and the sooner the spreading of disinformation reaches the saturation and decay stages.