Spreading Model Research Articles

Optimal resource allocation for rapid convergence to stable healthy state in epidemic spreading models

The networked Susceptible-Infected-Susceptible (SIS) model has been widely investigated as a model for the spread of epidemics within networked systems. In networks with SIS dynamics and unstable healthy states, a critical question is how to distribute compensatory curing resources (with constrained total cost) among individuals, ensuring that the network converges to a healthy state as fast as possible. This paper introduces a novel approach to this problem by developing an algorithm for the optimal allocation of compensatory curing resources required by agents in a given network. This solution approach has been made possible by reformulating the original convergence rate optimization problem and an additional constraint guaranteeing a minimum convergence rate as a standard semidefinite programming problem. The applicability of the proposed algorithm to arbitrary undirected topologies and other variations of the SIS model, including the one with a weighted cost function, has been demonstrated. In the case of symmetry preserving curing and infection rates, it has been shown that the problem over a given network with a symmetric topology can be reduced to a smaller network with each orbit acting as a node. Additionally, for networks with one or two orbits, the problem has been addressed analytically, and several examples have been included. Based on two different scenarios inspired by the SARS outbreak in Hong Kong and the COVID-19 outbreak in the USA, it is shown that the algorithm's optimal results outperform the uniform distribution of additional curing resources. The paper also explores how optimal performance metrics change with the given upper limit on the total amount of additional curing resources.

An overview of the challenges and methods for defining the wildlife-disease interface in the context of disease spread modelling, including concepts of infectious diseases epidemiology

ABSTRACT The wildlife-domestic interface is a key concept helping us to understand how diseases affect both wild and domestic animal populations, yet how to define and measure it remains a challenge. One tool which can contribute to our understanding of the wildlife-domestic interface is disease spread modelling. This can provide insight into how diseases spread within and between populations, and guide decision-making for disease control, response and surveillance programs. Specifically, quantitative estimation of contact rates permits such disease spread models to be developed and used confidently. Here we present a case study of the potential spread of rabies (an exotic disease in Australia) within the wild dog-domestic dog (Canis familiaris) ecosystem of northern Australia to illustrate the concept of the wildlife-domestic interface and disease transmission. We summarise a decade of research conducted to understand one such interface, the wild-domestic dog interface at one site in northern Australia, for the purposes of exploring the impact of one specific disease, rabies. Over a period of 10 years, free-roaming domestic dogs and wild dogs in the Northern Peninsula Area (NPA) of Cape York, Queensland were studied. Key findings included identification of a small but important group of domestic dogs which regularly roam in bushland areas; peri-urban wild dog activity, particularly in the dry season, likely driven by the availability of food sources; and the potential for interaction between hunting dogs and wild dogs in remote areas, particularly during the wet season. These observations can be used to inform disease spread models and identify strategies to mitigate the risks of disease transmission. However, the collection and incorporation of data into such models needs more consideration regarding what information is usable (such as contact rates) and the best ways to collect it. The scarcity of such models incorporating the wildlife-domestic interface suggests that integrated epidemiological-ecological studies are needed to fill this gap.

Simulation of Crowd Evacuation under Attack Considering Emotion Spreading

Abstract In recent years, attacks against crowded places such as campuses and theaters have frequently and negatively impacted social security and stability. In such an event, the crowd will be subjected to higher psychological stress, and their emotion will spread to others rapidly. This paper establishes the Attack-Evacuation Evacuation Simulation Model (AEES-SFM) based on the social force model to consider emotion spreading under attack. In this model, (1)Attack-Escape Derive Force was considered for the interaction between attackers and evacuees and (2) emotion spreading among the evacuees was considered to modify the value of psychological forces. To validate the simulation, several experiments were carried out in a university in China. Comparing the simulation and experimental results, it is found that the simulation results are similar to the experimental results when considering emotion spreading. Therefore, the AEES-SFM proved to be effective. By comparing the results of the evacuation simulation without emotion spread, the emotion spread model reduces the evacuation time and the number of casualties by about 30%, which is closer to the real experimental results. And the results are still applicable in the case of a 40-person evacuation. This paper provides theoretical support and practical guidance for campus response to violent attacks.

An epidemic spread model with nonlinear recovery rates on meta-population networks

An epidemic spread model with nonlinear recovery rates on meta-population networks

Spatiotemporal modeling of COVID-19 spread: unveiling socioeconomic disparities and patterns, across social classes in the urban population of Kermanshah, Iran.

Presenting ongoing outbreaks and the potential for their spread to nearby neighborhoods and social classes may offer a deeper understanding, enable a more efficient reaction to outbreaks, and enable a comprehensive understanding of intricate details for strategic response planning. Hence, this study explored the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas among social classes in the Kermanshah metropolis. In this cross-sectional study, the data of 58.951 COVID-19-infected patients were analyzed. In 2020, out of 24.849 infected patients, 10.423 were females, 14,426 were males, and in 2021, 15.714 were females, and 18,388 were males. To categorize social classes (working, middle, and upper), we utilized economic, social, cultural, and physical indicators. Our analysis utilized Arc/GIS 10.6 software along with statistical tests, including standard distance (SD), mean center (MC), standard deviational ellipse (SDE), and Moran's I. The results revealed that the average epicenter of the disease shifted from the city center in 2020-2021 to the eastern part of the city in 2021. The results related to the SD of the disease showed that more than 70% of the patients were concentrated in this area of the city. The SD of COVID-19 in 2020 compared to 2021 also indicated an increased spread throughout the city. Moran's I test and the hotspot test results showed the emergence of a clustered pattern of the disease. In the Kermanshah metropolis, 58,951 COVID-19 cases were recorded, with 55.76% males and 44.24% females. Social class distribution showed 28.86% upper class, 55.95% middle class, and 15.19% working class. A higher disease prevalence among both males and females in the upper class compared to others. Our study designed a spatiotemporal disease spread model, specifically tailored for a densely populated urban area. This model allows for the observation of how COVID-19 propagates both spatially and temporally, offering a deeper understanding of outbreak dynamics in different neighborhoods and social classes of the city.

Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.

Stability Analysis of the Transmission Model of TB-HIV Co-Dynamics with Vaccination and Treatment

This study aims to analyse and determine the stability of the equilibrium point of the spread model consists of TB infection, HIV infection, and TB-HIV co-infection disease. This model considers eight compartments, namely unvaccinated susceptible, vaccinated susceptible, exposed, infected with TB, infected with HIV, infected with TB and HIV, treatment, and recovered by considering vaccination and treatment in the compartments as the strategies to manage spread of the diseases. The stability of non-endemic equilibrium point is carried out by determining the basic reproduction number and eigenvalues. Simulation is conducted to investigate effect of vaccination and treatment. By giving appropriate values of parameters and varying values of vaccination rate we found that increasing value of vaccination rate will reduce and eliminate TB infection, HIV infection, and TB-HIV co-infection disease from the population. The curve solutions of the dynamics of each compartment are given to confirm the analytical results.

Coupling CFD and VR for advanced firefighting training in a virtual ship engine room

Firefighting training using virtual reality (VR) technology has still not been used extensively in the maritime industry. One of the reasons lies in the inability of VR to accurately replicate the true dynamics of a fire. However, this shortfall can be effectively addressed by integrating computational fluid dynamics-based (CFD) models of fire spread into VR training simulations, thereby significantly enhancing the realism and educational value of these scenarios. In this study, a scenario of a fire spread inside the ship engine room has been developed. First, as a CFD analysis, whose results were then transferred to the VR environment. That approach has been tested with users who have noted that CFD-based fire gives a more realistic appearance than the generic modelled solely using the game engine.

Misinformation spreading on activity-driven networks with heterogeneous spreading rates.

The spread of misinformation on social media is inextricably related to each user's forwarding habits. In this paper, given that users have heterogeneous forwarding probabilities to their neighbors with varied relationships when they receive misinformation, we present a novel ignorant-spreader-refractory (ISR) spreading model with heterogeneous spreading rates on activity-driven networks with various types of links that encode these differential relationships. More exactly, in this model, the same type of links has an identical spreading rate, while different types of links have distinct ones. Using a mean-field approach and Monte Carlo simulations, we investigate how the heterogeneity of spreading rates affects the outbreak threshold and final prevalence of misinformation. It is demonstrated that the heterogeneity of spreading rates has no effect on the threshold when the type of link follows a uniform distribution. However, it has a significant impact on the threshold for non-uniform distributions. For example, the heterogeneity of spreading rates increases the threshold for normal distribution while it lowers the threshold for an exponent distribution. In comparison to the situation of a homogeneous spreading rate, whether the heterogeneity of spreading rates improves or decreases the final prevalence of misinformation is also determined by the distributions of the type of links.

Modeling and analysis of COVID-19 spreading based on complex network theory

Complex networks can effectively describe interactions within real-world complex systems. In researches of epidemic spreading, scientists constructed various physical contact networks between individuals on the microscopic scale and the metapopulation networks on the macroscopic scale. These different types of network structures significantly impact the propagation dynamics of epidemic in human society. For instance, population flows in global airline networks influence the speed and arrival time of epidemics across large-scale space. In this paper we review the epidemic spreading models on various network structures, including fully mixed networks, three types of lower-order networks, three types of higher-order networks, metapopulation networks, and multiple strains competitive epidemic spreading models. We also provide an overview of the application of complex network theory in the COVID-19 pandemic, covering topics of prediction, prevention, and control of the epidemic. Finally, we discuss the strengths and limitations of these models and propose perspectives for future research.

The complex landscape of luminal breast cancer.

The breast epithelium, vital for mammary gland function, is influenced by oestrogen through the ER signalling pathway. Luminal breast cancer, characterised by ER expression, comprises the majority of all breast cancers and presents significant clinical challenges due to therapy resistance and recurrence. Despite advancements in understanding luminal disease, improving long-term survival and reducing relapse of breast cancer patients by predicting therapy efficacy and understanding resistance mechanisms remain critical challenges. This review discusses luminal breast cancer biology, focusing on molecular classification of the primary disease, metastatic spread, and experimental models.

Money/Asset Ratio as a Predictor of Inflation

This paper modifies the quantity theory of money to forecast inflation, relating the latter to scale variables such as monetary aggregate M2 and government bonds that measure the money demand for asset transactions. The out-of-sample forecast results show that at least since the early 1990s, the money/asset model that uses the money supply/government debt ratio as a predictor has been significantly improved upon univariate and multivariate models, such as Phillips curve and term spread models, for forecasting U.S. inflation over one- to three-year horizons. In using real-time vintage data, I find that, since 2000Q1, the forecasts derived from the money/asset model have slightly improved upon those from the Greenbook in forecasting quarter-over-quarter CPI inflation at short horizons, from two- to four-quarter. These results imply that the Federal Reserve can use the money supply/government debt ratio to forecast and control the inflation rates, coordinating monetary policy with fiscal policy. Moreover, the money supply/government debt ratio can partly explain the U.S. inflation dynamics from the early 1960s until COVID-19.

Finite determination for embeddings into Banach spaces: New proof, low distortion

The main goal of this paper is to improve the result of M. I. Ostrovskii [Embeddability of locally finite metric spaces into Banach spaces is finitely determined, Proc. Amer. Math. Soc. 140 (2012) 2721–2730.] on the finite determination of bilipschitz and coarse embeddability of locally finite metric spaces into Banach spaces. There are two directions of the improvement: (1) Substantial decrease of distortion (from about [Formula: see text] to 3 + [Formula: see text]) is achieved by replacing the barycentric gluing by the logarithmic spiral gluing. This decrease in the distortion is particularly important when the finite determination is applied to construction of embeddings. (2) Simplification of the proof: a collection of tricks employed in M. I. Ostrovskii [Embeddability of locally finite metric spaces into Banach spaces is finitely determined, Proc. Amer. Math. Soc. 140 (2012) 2721–2730.] is no longer needed; in addition to the logarithmic spiral gluing we use only a version of the Brunel–Sucheston argument used to prove the existence of spreading models.

Choice of landscape discretisation method affects the inferred rate of spread in wildlife disease spread models

Disease modelling at the livestock-wildlife interface is an important topic for which discrete-space models are used for the wildlife component. One prominent example is African Swine Fever, where wild boar play an influential role as reservoirs of disease spillover into domestic pig farms. In this paper, we present a simulation study that demonstrates the impact of seemingly arbitrary choices of landscape discretisation method on the inferred rate of spread within the model. We use an ordinary differential equation model to implement a simplified model of disease transmission between discrete groups of wild boar with spillover into domestic pig farms contained within a homogeneous landscape. We examine a range of scenarios whereby the landscape is discretised into wild boar patches of varying size and shape, and compare the rate of spread between domestic pig farms placed at fixed points on the landscape. Our results demonstrate a non-monotonic relationship between patch size and rate of spread, which is particularly unstable and unpredictable for square and triangular shaped patches. Discretisation of the landscape into hexagons appears to produce a more stable relationship between patch size and rate of spread for the three types of transmission kernel we investigated. Although the rate of disease spread does converge to a stable value, this occurs at patch sizes that are much smaller than would be used in practice for wild boar. We conclude that outputs of disease models containing a wildlife component should not be considered to be robust to arbitrary choices for patch size and placement, but rather as a source of uncertainty to be examined using sensitivity analysis. Furthermore, we strongly recommend the use of hexagons rather than squares or right triangles for landscape discretisation.

Mathematical modeling of pollution of underground aquifers due to mining of minerals

Purpose. The research aims to create a mathematical model of salt contamination spreading through underground aquifers in the event of depressurization of the hydrocarbon production well crater for further assessment of environmental and economic damage from these processes. Methods. To predict the environmental and economic damage from salt contamination, the distribution of concentrations of harmful substances was investigated, taking into account the number of supply sources and their intensity over time, based on situ studies at the Rybalske Oil Field, Okhtyrskyi District of Sumska Oblast in Ukraine, where there were technological failures wells, accompanied by open fountains with the release of large amounts of highly mineralized water and the formation of craters. Mathematical modelling methods were used to process the data from the study of accidental technogenic pollution of underground aquifers. Findings. Based on real data from the study of the processes of potential salt contamination spread in fresh aquifers as a result of accidents at hydrocarbon production facilities, a mathematical model of salt contamination spreading in drinking groundwater in the event of depressurization of an oil field well crater has been developed. Potential economic losses in case of possible groundwater contamination with highly mineralized solution, which can into drinking groundwater aquifers, are substantiated. It has been established that in connection with the occurrence of an emergency situation due to the release of formation water to the surface in the territory of oil and gas fields, the formation of technogenic meromictic reservoirs is possible, which is confirmed by the example of the Rybalske Oil Field. It is proved that the total mineralization of crater water increases linearly with depth of the reservoir occurrence, and a similar dependence is characteristic of the chloride ion content. Originality. For the first time, a multicomponent mathematical model of mineral salt migration processes in underground freshwater aquifers in the case of depressurization of a meromictic reservoir has been developed. Practical implications. The research results obtained using numerical methods make it possible to predict the processes of spreading harmful substances in drinking underground aquifers as a result of emergencies at oil and gas fields, taking into account the number of sources of pollutants penetrating the study area, the heterogeneity of properties of the environment into which the harmful substance enters, and to assess the dynamics of changes in the concentration of these substances and time with further assessment of environmental and economic damage from these processes.

제4차 산업혁명 시대의 중학교 가정 교과의 3D 프린팅 활용 의생활 메이커 교육 교수⋅학습과정안 개발

The purpose of this study was to develop a teaching and learning plan for clothing maker education in middle schools using 3D printers, one of the key tools of the Fourth Industrial Revolution. This plan aims to revitalize clothing maker education by incorporating maker education programs. The research was conducted in four stages: Analysis, Design, Development, and Evaluation. First, in the analysis stage, the content related to clothing education in the 2015 revised secondary school home economics curriculum was analyzed to derive the elements of clothing maker education and home economics. Additionally, an analysis of equipment suitable for maker education was conducted, leading to the selection of 3D printers and heat transfer printers as maker equipment. Second, in the design stage, the composition of the teaching and learning program based on the TPMS (Tinkering, Practical reasoning, Making together, & Sharing and spreading) model and the selection of media were established. Third, in the development stage, a comprehensive plan was implemented to develop a maker education teaching and learning process that emphasized practical problem-solving. Learning activity sheets, PPT materials, group and self-evaluation forms, and program evaluation tools were also developed. Finally, in the evaluation stage, safety rules were added following expert validation of the teaching and learning plans. Class goals were clarified considering the functions of home economics. The evaluation tools were also revised and supplemented to facilitate student-centered assessment.

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

Single-cell virology: On-chip, quantitative characterization of the dynamics of virus spread from one single cell to another.

Virus spread at the single-cell level is largely uncharacterized. We have designed and constructed a microfluidic device in which each nanowell contained a single, infected cell (donor) and a single, uninfected cell (recipient). Using a GFP-expressing poliovirus as our model, we observed both lytic and non-lytic spread. Donor cells supporting lytic spread established infection earlier than those supporting non-lytic spread. However, non-lytic spread established infections in recipient cells substantially faster than lytic spread and yielded higher rates of genome replication. While lytic spread was sensitive to the presence of capsid entry/uncoating inhibitors, non-lytic spread was not. Consistent with emerging models for non-lytic spread of enteroviruses using autophagy, reduction of LC3 levels in cells impaired non-lytic spread and elevated the fraction of virus in donor cells spreading lytically. The ability to distinguish lytic and non-lytic spread unambiguously will enable discovery of viral and host factors and host pathways used for non-lytic spread of enteroviruses and other viruses as well.

Dilute Polymer Droplets Show Generalized Wetting Dynamics via an Average Viscosity.

Despite the prevalence of non-Newtonian fluids in various practical applications, comprehensive dynamic wetting models are lacking. Existing models often oversimplify complex rheological behavior, limiting our ability to predict wetting dynamics. This work introduces and experimentally validates a generalized model for the dynamic wetting of non-Newtonian shear-thinning fluids (dilute polymer solutions) on solid substrates. We experimentally analyzed 12 different shear-thinning fluids using both a power-law model and Carreau-Yasuda model. The data clearly show that the dynamic contact angle can be generalized using an average viscosity to capture rheological changes during droplet spreading. The average viscosity was defined using the fluid's constitutive model over shear rates relevant to the spreading process. Using a small droplet approximation, we propose and validate a semianalytical spreading model to predict the basal radius of non-Newtonian droplets. The model agrees well with the experimental data. Additionally, the average viscosity was used to define a spreading time scale, which is capable of collapsing the spreading of different non-Newtonian fluids onto a master spreading curve. This work offers significant potential for predicting the dynamic shape and spreading of non-Newtonian fluids with complex rheologies in a range of applications and industrial processes.

DEM-based pluvial flood inundation modeling at a metropolitan scale

The global increase in urban flooding presents a substantial challenge that affects communities across the globe. This study introduces a post-flood inundation modeling framework tailored to pluvial floods on a metropolitan scale. We employ a dual drainage modeling approach for enhanced accuracy. The framework comprises two primary components: a Storm Water Management Model (SWMM) for simulating water movement within the storm drain system, and an advanced DEM-based flood inundation module that leverages SWMM results to predict flood inundation areas. Compared to the conventional flood spreading models commonly used in dual drainage modeling, this module incorporates high-resolution DEM cells into the surface flow routing processes, allowing to capture intricate surface terrain complexities. Additionally, the proposed module considers direct rainfall impacts on pluvial flood inundation mapping, resulting in enhanced accuracy. To evaluate the proposed model's accuracy, a two-step validation approach has been implemented. This includes comparing the proposed model results with both the high water marks measured in the aftermath of Hurricane Harvey and the flood depth map generated by a hydrodynamic model. The findings affirm the effectiveness of our proposed framework for post-flood inundation mapping within metropolitan settings. This method provides a dependable approach to urban flood modeling and represents an advancement in addressing the challenges associated with urban flooding.