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Journal articles on the topic 'Networked Epidemic Model'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Liu, Zuhan, and Canrong Tian. "A weighted networked SIRS epidemic model." Journal of Differential Equations 269, no. 12 (2020): 10995–1019. http://dx.doi.org/10.1016/j.jde.2020.07.038.

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2

Tian, Canrong, Qunying Zhang, and Lai Zhang. "Global stability in a networked SIR epidemic model." Applied Mathematics Letters 107 (September 2020): 106444. http://dx.doi.org/10.1016/j.aml.2020.106444.

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ÁLVAREZ, E., J. DONADO-CAMPOS, and F. MORILLA. "New coronavirus outbreak. Lessons learned from the severe acute respiratory syndrome epidemic." Epidemiology and Infection 143, no. 13 (2015): 2882–93. http://dx.doi.org/10.1017/s095026881400377x.

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SUMMARYSystem dynamics approach offers great potential for addressing how intervention policies can affect the spread of emerging infectious diseases in complex and highly networked systems. Here, we develop a model that explains the severe acute respiratory syndrome coronavirus (SARS-CoV) epidemic that occurred in Hong Kong in 2003. The dynamic model developed with system dynamics methodology included 23 variables (five states, four flows, eight auxiliary variables, six parameters), five differential equations and 12 algebraic equations. The parameters were optimized following an iterative pr

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Шеншин, Александр Игоревич, Евгения Андреевна Шварцкопф, and Константин Александрович Разинкин. "MATHEMATICAL PROVISION OF TWO-STAGE MODEL OF EPIDEMIC PROCESSES OF NETWORKED AUTOMATED STRUCTURES." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 3(-) (October 19, 2021): 431–52. http://dx.doi.org/10.36622/vstu.2021.24.3.010.

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В последние годы отмечается стремительный рост количества атак на информационные системы и ресурсы с использованием вредоносного кода и контента. Наряду с этим, происходит непрерывное совершенствование функциональных возможностей вирусов, позволяющих скрывать своё присутствие в системе. К сожалению, существующий арсенал моделей эпидемических процессов не позволяет эффективно учитывать период скрытого распространения инфекции с последующим реагированием систем защиты при практическом моделировании сетевых эпидемий. В представленном исследовании проведён анализ существующего методического обеспе

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Na, Jihye, Youngeun Nam, Susik Yoon, Hwanjun Song, Byung Suk Lee, and Jae-Gil Lee. "Mobility Networked Time-Series Forecasting Benchmark Datasets." Proceedings of the International AAAI Conference on Web and Social Media 19 (June 7, 2025): 2539–49. https://doi.org/10.1609/icwsm.v19i1.35955.

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Human mobility is crucial for urban planning (e.g., public transportation) and epidemic response strategies. However, existing research often neglects integrating comprehensive perspectives on spatial dynamics, temporal trends, and other contextual views due to the limitations of existing mobility datasets. To bridge this gap, we introduce MOBINS (MOBIlity Networked time Series), a novel dataset collection designed for networked time-series forecasting of dynamic human movements. MOBINS features diverse and explainable datasets that capture various mobility patterns across different transporta

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Xue, Dong, Naichao Liu, Xinyi Chen, and Fangzhou Liu. "A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic." Entropy 26, no. 8 (2024): 654. http://dx.doi.org/10.3390/e26080654.

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This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements

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Liu, Fangzhou, Shaoxuan CUI, Xianwei Li, and Martin Buss. "On the Stability of the Endemic Equilibrium of A Discrete-Time Networked Epidemic Model." IFAC-PapersOnLine 53, no. 2 (2020): 2576–81. http://dx.doi.org/10.1016/j.ifacol.2020.12.304.

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Anderson, Brian D. O., and Mengbin Ye. "Equilibria Analysis of a Networked Bivirus Epidemic Model Using Poincaré–Hopf and Manifold Theory." SIAM Journal on Applied Dynamical Systems 22, no. 4 (2023): 2856–89. http://dx.doi.org/10.1137/22m1529981.

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Liu, Fangzhou, Zengjie Zhang, and Martin Buss. "Optimal filtering and control of network information epidemics." at - Automatisierungstechnik 69, no. 2 (2021): 122–30. http://dx.doi.org/10.1515/auto-2020-0096.

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Abstract In this article, we propose an optimal control scheme for information epidemics with stochastic uncertainties aiming at maximizing information diffusion and minimizing the control consumption. The information epidemic dynamics is represented by a network Susceptible-Infected-Susceptible (SIS) model contaminated by both process and observation noises to describe a perturbed disease-like information diffusion process. To reconstruct the contaminated system states, we design an optimal filter which ensures minimized estimation errors in a quadratic sense. The state estimation is then uti

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10

Bellocchio, Francesco, Paola Carioni, Caterina Lonati, et al. "Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network." International Journal of Environmental Research and Public Health 18, no. 18 (2021): 9739. http://dx.doi.org/10.3390/ijerph18189739.

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Accurate predictions of COVID-19 epidemic dynamics may enable timely organizational interventions in high-risk regions. We exploited the interconnection of the Fresenius Medical Care (FMC) European dialysis clinic network to develop a sentinel surveillance system for outbreak prediction. We developed an artificial intelligence-based model considering the information related to all clinics belonging to the European Nephrocare Network. The prediction tool provides risk scores of the occurrence of a COVID-19 outbreak in each dialysis center within a 2-week forecasting horizon. The model input var

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Zhou, Shumin, Yunxian Dai, and Hongyan Wang. "STABILITY AND HOPF BIFURCATION ANALYSIS OF A NETWORKED SIR EPIDEMIC MODEL WITH TWO DELAYS AND DELAY DEPENDENT PARAMETERS." Journal of Applied Analysis & Computation 15, no. 4 (2025): 1996–2026. https://doi.org/10.11948/20240382.

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12

Chwat, Olivia. "Social Solidarity during the Pandemic: The “Visible Hand” and Networked Social Movements." Kultura i Społeczeństwo 65, no. 1 (2021): 87–104. http://dx.doi.org/10.35757/kis.2021.65.1.3.

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The author poses the following questions: (1) What forms are social movements adopting today, particularly in response to the epidemic crisis? (2) Are we observing the practice of grassroots solidarity reaching beyond the charitable model of support? She seeks answers taking the Facebook group Visible Hand [Widzialna Ręka] as an example; it was established shortly after lockdown had been announced in the first quarter of 2020, as a form of social organisation aiming to provide mutual aid during the difficult time of the pandemic. She asserts that communities organising themselves in a manner s

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Siettos, Constantinos I., Cleo Anastassopoulou, Lucia Russo, Christos Grigoras, and Eleftherios Mylonakis. "Forecasting and control policy assessment for the Ebola virus disease (EVD) epidemic in Sierra Leone using small-world networked model simulations." BMJ Open 6, no. 1 (2016): e008649. http://dx.doi.org/10.1136/bmjopen-2015-008649.

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14

Beketova, Gulzhanat, and Ainur Manapova. "Adaptive mathematical modeling for predicting and analyzing malware." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1698. https://doi.org/10.11591/ijeecs.v38.i3.pp1698-1707.

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In this paper, we propose and investigate an improved mathematical model of malware propagation in network structures based on a modification of the well-known raw-immune-response susceptible-infected-recovered (SIR) model. For detailed numerical analysis, our study introduces the fourth-order Runge-Kutta method, which provides higher accuracy in determining fundamental parameters such as infection, recovery and immunity loss coefficients of network nodes. The obtained simulation results demonstrate that the peak of the epidemic occurs when 34.7% of all nodes are infected, with a peak after 32

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Poncela-Casasnovas, Julia, Bonnie Spring, Daniel McClary, et al. "Social embeddedness in an online weight management programme is linked to greater weight loss." Journal of The Royal Society Interface 12, no. 104 (2015): 20140686. http://dx.doi.org/10.1098/rsif.2014.0686.

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The obesity epidemic is heightening chronic disease risk globally. Online weight management (OWM) communities could potentially promote weight loss among large numbers of people at low cost. Because little is known about the impact of these online communities, we examined the relationship between individual and social network variables, and weight loss in a large, international OWM programme. We studied the online activity and weight change of 22 419 members of an OWM system during a six-month period, focusing especially on the 2033 members with at least one friend within the community. Using

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Augustine, Asakizi Nji, Asi Quigle Atud, Mbouombouo Mama, and Ajeagah Gideon Aghaindum. "Utilizing the Susceptible Infectious Susceptible (sis) Model to Evaluate the Risk for Waterborne Parasites to Spread through Household Water Sources in Bamenda, Cameroon." Advances in Research 25, no. 6 (2024): 454–60. https://doi.org/10.9734/air/2024/v25i61217.

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This research presents a distributed continuous-time epidemic model, called a SUSCEPTIBLE INFECTIOUS SUSCEPTIBLE- SIS model to denote a waterborne disease spreading over a network of different domestic waters sources such as tap water, well water and spring water in the understanding of infectious pathogens in the Municipality of Bamenda. It involves multiple groups of individuals sharing a water source which comprises the population in the communities or quarter of Upstation mile 1, Nkwen, Mankon and Nsongwa in and around this urban area. A sufficient condition is obtained for the healthy sta

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Sun, Chu, Qing Xia, and Xiaoren Mei. "Evaluation of Product Innovation Practice of Chinese Internet Companies Based on DANP Model." Wireless Communications and Mobile Computing 2022 (March 9, 2022): 1–15. http://dx.doi.org/10.1155/2022/5744875.

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Purpose. Internet companies have played an important supporting role in China’s economic growth and social resource allocation in the advent of an exogenous shock: the coronavirus disease (COVID-19) pandemic. This is owing to their digital, networked, and platform-based characteristics amidst an environment of intensified competition and stagnation of innovation activities due to the pandemic shock. Thus, based on the social network theory and resource-based theory, this study combines the DANP model with corporate innovation to build a product innovation performance evaluation framework for C

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18

Liu, Gehui, Yuqi Chen, Haichen Chen, Jiehao Dai, Wenjie Wang, and Senbin Yu. "The Identification of Influential Nodes Based on Neighborhood Information in Asymmetric Networks." Symmetry 16, no. 2 (2024): 193. http://dx.doi.org/10.3390/sym16020193.

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Identifying influential nodes, with pivotal roles in practical domains like epidemic management, social information dissemination optimization, and transportation network security enhancement, is a critical research focus in complex network analysis. Researchers have long strived for rapid and precise identification approaches for these influential nodes that are significantly shaping network structures and functions. The recently developed SPON (sum of proportion of neighbors) method integrates information from the three-hop neighborhood of each node, proving more efficient and accurate in id

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Cross, Cristina, Alysse Edwards, Dayna Mercadante, and Jorge Rebaza. "Dynamics of a networked connectivity model of epidemics." Discrete and Continuous Dynamical Systems - Series B 21, no. 10 (2016): 3379–90. http://dx.doi.org/10.3934/dcdsb.2016102.

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20

Nowzari, Cameron, Victor M. Preciado, and George J. Pappas. "Optimal Resource Allocation for Control of Networked Epidemic Models." IEEE Transactions on Control of Network Systems 4, no. 2 (2017): 159–69. http://dx.doi.org/10.1109/tcns.2015.2482221.

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21

Martín, Gonzalo, maria-cristina marinescu, Singh David E, and Jesus Carretero. "Leveraging social networks for understanding the evolution of epidemics." BMC Systems Biology 5, Supplement 3 (2011): S14. https://doi.org/10.1186/1752-0509-5-S3-S14.

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Background To understand how infectious agents disseminate throughout a population it is essential to capture the social model in a realistic manner. This paper presents a novel approach to modeling the propagation of the influenza virus throughout a realistic interconnection network based on actual individual interactions which we extract from online social networks. The advantage is that these networks can be extracted from existing sources which faithfully record interactions between people in their natural environment. We additionally allow modeling the characteristics of each individual a

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22

Hwang, Wonjun, Yoora Kim, and Kyunghan Lee. "Augmenting Epidemic Models with Graph Neural Networks." ACM SIGMETRICS Performance Evaluation Review 50, no. 4 (2023): 11–13. http://dx.doi.org/10.1145/3595244.3595249.

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Conventional epidemic models are limited in their ability to capture the dynamics of real world epidemics in a sense that they either place restrictions on the models such as their topology and contact process for mathematical tractability or focus only on the average global behavior, which lacks details for further analysis. We propose a novel modeling approach that augments the conventional epidemic models using Graph Neural Networks to improve their expressive power while preserving useful mathematical structures. Simulation results show that our proposed model can predict spread times in b

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23

Osipov, Vasiliy, Sergey Kuleshov, Alexandra Zaytseva, and Alexey Aksenov. "Approach for the COVID-19 Epidemic Source Localization in Russia Based on Mathematical Modeling." Informatics and Automation 20, no. 5 (2021): 1065–89. http://dx.doi.org/10.15622/20.5.3.

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The paper presents the results of statistical data from open sources on the development of the COVID-19 epidemic processing and a study сarried out to determine the place and time of its beginning in Russia. An overview of the existing models of the processes of the epidemic development and methods for solving direct and inverse problems of its analysis is given. A model for the development of the COVID-19 epidemic via a transport network of nine Russian cities is proposed: Moscow, St. Petersburg, Nizhny Novgorod, Rostov-on-Don, Krasnodar, Yekaterinburg, Novosibirsk, Khabarovsk and Vladivostok

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Qu, Zongxi, Beidou Zhang, and Hongpeng Wang. "A Multivariate Deep Learning Model with Coupled Human Intervention Factors for COVID-19 Forecasting." Systems 11, no. 4 (2023): 201. http://dx.doi.org/10.3390/systems11040201.

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Artificial intelligence (AI) technology plays a crucial role in infectious disease outbreak prediction and control. Many human interventions can influence the spread of epidemics, including government responses, quarantine, and economic support. However, most previous AI-based models have failed to consider human interventions when predicting the trend of infectious diseases. This study selected four human intervention factors that may affect COVID-19 transmission, examined their relationship to epidemic cases, and developed a multivariate long short-term memory network model (M-LSTM) incorpor

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Li, Bing, and Qi Liu. "Optimal Scheduling of Emergency Materials Based on Gray Prediction Model under Uncertain Demand." Electronics 12, no. 20 (2023): 4337. http://dx.doi.org/10.3390/electronics12204337.

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In the context of long-term infectious disease epidemics, guaranteeing the dispatch of materials is important to emergency management. The epidemic situation is constantly changing; it is necessary to build a reasonable mechanism to dispatch emergency resources and materials to meet demand. First, to evaluate the unpredictability of demand during an epidemic, gray prediction is inserted into the proposed model, named the Multi-catalog Schedule Considering Costs and Requirements Under Uncertainty, to meet the material scheduling target. The model uses the gray prediction method based on pre-epi

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Chumachenko, Dmytro, Ievgen Meniailov, Andrii Hrimov, Vladislav Lopatka, Olha Moroz, and Olena Tolstoluzka. "Simulation and forecasting of the influenza epidemic process using seasonal autoregressive integrated moving average model." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (November 29, 2021): 22–35. http://dx.doi.org/10.32620/reks.2021.4.02.

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Today's global COVID-19 pandemic has affected the spread of influenza. COVID-19 and influenza are respiratory infections and have several similar symptoms. They are, however, caused by various viruses; there are also some differences in the categories of people most at risk of severe forms of these diseases. The strategies for their treatment are also different. Mathematical modeling is an effective tool for controlling the epidemic process of influenza in specified territories. The results of modeling and forecasts obtained with the help of simulation models make it possible to develop timely

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Zakharov, Victor, and Yulia Balykina. "Balance Model of COVID-19 Epidemic Based on Percentage Growth Rate." Informatics and Automation 20, no. 5 (2021): 1034–64. http://dx.doi.org/10.15622/20.5.2.

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The paper examines the possibility of using an alternative approach to predicting statistical indicators of a new COVID-19 virus type epidemic. A systematic review of models for predicting epidemics of new infections in foreign and Russian literature is presented. The accuracy of the SIR model for the spring 2020 wave of COVID-19 epidemic forecast in Russia is analyzed. As an alternative to modeling the epidemic spread using the SIR model, a new CIR discrete stochastic model is proposed based on the balance of the epidemic indicators at the current and past time points. The new model describes

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Pei-Hsuan Hsieh, Pei-Hsuan Hsieh, and Chun-Hua Lin Pei-Hsuan Hsieh. "A Social Network Analysis of COVID-19 Transmission Models in Taiwan: Two Epidemic Waves in 2020-2021." 網際網路技術學刊 23, no. 5 (2022): 1009–18. http://dx.doi.org/10.53106/160792642022092305009.

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<p>The COVID-19 pandemic has made a profound global impact. As it rages on around the globe, social network researchers have been involved in exploring key factors of rapid infection and transmission. For Taiwan, it is thus worthy of exploring the differences between the transmission models of the two epidemic waves in 2020-2021 for any insight that may have been overlooked. In this study, the social network analysis is adopted for revealing any unforeseen threats of infection. In the first wave, 652 confirmed cases were reported from January 21, 2020, to November 30, 2020. In the second

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Hu, Xiaofeng. "Study on the Risk of Transmission of COVID-19 Based on Population Migration." Wireless Communications and Mobile Computing 2022 (June 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/1646626.

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Since the emergence of COVID-19, migration of people has transferred the virus to new locations, causing the epidemic to expand, and local governments have put in place control measures to prevent the virus from spreading further. As of January 24, 2020, we calculated the population immigration from Wuhan to the rest of mainland China using migration statistics from the Gaode Map. In addition, we utilized machine learning methods to simulate the curve of the COVID-19 epidemic in different regions and over different time periods. Furthermore, we used machine learning methods to simulate the COV

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András, Bóta, Hajdu László, Brys Zoltán, and Miklós Ferenz Krész. "Válogatás a hálózatalapú járványterjedési modellek eredményeibôl." eLitMed 32, no. 11/12 (2022): 515–20. https://doi.org/10.33616/lam.32.0515.

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 Graphs, or networks in a mathematical sense, provide a flexible modelling tool available for other scientific fields, including among others medicine. In this publication, we give a brief overview of infec - tious disease related network modelling. While there are known tools in the literature of this scientific field, the process of infection spreading can be modelled perfectly with networks too. The potential of the topic is proven by the diversity of applied models, the versatility of applications and the large number of scientific publications in this field. Due to the size of the fi

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Wang, Xu, Bo Song, Wei Ni, et al. "Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks." Security and Communication Networks 2019 (January 16, 2019): 1–9. http://dx.doi.org/10.1155/2019/1657164.

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Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and link

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Ma, Junyi, Xuanliang Wang, Yasha Wang, Jiangtao Wang, Xu Chu, and Junfeng Zhao. "Enhancing Online Epidemic Supervising System by Compartmental and GRU Fusion Model." Mobile Information Systems 2022 (August 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/3303854.

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The global pandemic, COVID-19, is an acute respiratory infectious disease caused by the 2019 novel coronavirus. Building the online epidemic supervising system to provide COVID-19 dynamic prediction and analysis has attracted the attention of the industry and applications community. In previous studies, the compartmental models and deep neural networks (DNNs) played important roles in predicting and analyzing the dynamics of the pandemic. Nevertheless, the compartmental model has limited ability to fit historical data and thus leads to unsatisfactory prediction accuracy due to the difficulty i

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Zhang, Changlun, NingNan, Zhanyong Jin, and Jian Zhang. "An epidemic spreading model based on community structure in dual social networks." International journal of Microbiology and Mycology (IJMM) 5, no. 3 (2017): 1–10. https://doi.org/10.5281/zenodo.8373620.

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The spread of disease through a physical contact network and the diffusion of information awareness about the disease on a communication network are two intimately related dynamical processes. To catch the influence of community structure on the epidemic spreading, we propose an epidemic spreading model based on the network community structure in dual networks. The model used the dual network to describe the interplay between the two types of spreading dynamics, each occurring on its own layer. As for the diffusion of awareness, we study the impact of the community structure on the changes of

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Ebtehal, Akeel Hamed, Abd Alhade Sultan Batool, and Kadhm Obeas Zainab. "A NEW RANKING TECHNIQUE TO ENHANCE THE INFECTION SIZE IN COMPLEX NETWORKS." LC International Journal of STEM 4, no. 3 (2023): 137–48. https://doi.org/10.5281/zenodo.10431177.

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Detecting the spreaders/sources in complex networks is an essential manner to understand the dynamics of the information spreading process. Consider the k-Shell centrality metric, which is taken into account the structural position of a node within the network, a more effective metric in picking the node which has more ability on spreading the infection compared to other centrality metrics such the degree, betweenness, and closeness.  However, the K-Shell method suffers from some boundaries, it gives the same K-Shell index to a lot of the nodes, and it uses only one indicator to rank the

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Loola Bokonda, Patrick, Moussa Sidibe, Nissrine Souissi, and Khadija Ouazzani-Touhami. "Machine Learning Model for Predicting Epidemics." Computers 12, no. 3 (2023): 54. http://dx.doi.org/10.3390/computers12030054.

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COVID-19 has raised the issue of fighting epidemics. We were able to realize that in this fight, countering the spread of the disease was the main goal and we propose to contribute to it. To achieve this, we propose an enriched model of Random Forest (RF) that we called RF EP (EP for Epidemiological Prediction). RF is based on the Forest RI algorithm, proposed by Leo Breiman. Our model (RF EP) is based on a modified version of Forest RI that we called Forest EP. Operations added on Forest RI to obtain Forest EP are as follows: the selection of significant variables, the standardization of data

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LYSENKO, Sergii, Vitalina Sakhniuk, and Oleg BONDARUK. "A METHOD FOR SYNTHESIZING HARDWARE AND SOFTWARE TOOLS TO ENSURE THE STABILITY OF A CORPORATE COMPUTER NETWORK." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (2023): 344–50. http://dx.doi.org/10.31891/2307-5732-2023-319-1-344-350.

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The paper represents a method for ensuring the resilience of a corporate computer network under the influence of various types of threats. This article will provide an overview of the aspects of resilience and existing approaches to ensuring resilient routing. This article is the result of many studies and experiments, and evaluating the final result, it can be noted that this method can successfully reflect the possible importance of a node when it comes to epidemic dynamics for various network models to ensure network resilience. A possible way to solve the problem was to use the theory of l

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Ghosh, Asit K., J. Chattopadhyay, and P. K. Tapaswi. "An SIRS epidemic model on a dispersive population." Korean Journal of Computational & Applied Mathematics 7, no. 3 (2000): 693–708. http://dx.doi.org/10.1007/bf03012279.

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Du, Yi-Hong, and Shi-Hua Liu. "Epidemic Model of Algorithm-Enhanced Dedicated Virus through Networks." Security and Communication Networks 2018 (June 7, 2018): 1–7. http://dx.doi.org/10.1155/2018/4691203.

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Wi-Fi networks almost cover all active areas around us and, especially in some densely populated regions, Wi-Fi signals are strongly overlapped. The broad and overlapped coverage brings much convenience at the cost of great security risks. Conventionally, a worm virus can infect a router and then attack other routers within its signal coverage. Nowadays, artificial intelligence enables us to solve problems efficiently from available data via computer algorithm. In this paper, we endow the virus with some abilities and present a dedicated worm virus which can pick susceptible routers with kerne

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Yan, Dingyu, Feng Liu, Yaqin Zhang, and Kun Jia. "Dynamical model for individual defence against cyber epidemic attacks." IET Information Security 13, no. 6 (2019): 541–51. http://dx.doi.org/10.1049/iet-ifs.2018.5147.

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Wang, Weiguo, Chen Chu, Jinzhuo Liu, and Tairan Li. "An Epidemic Model of Information Dissemination in Mobile Social Networks." International Journal of u- and e-Service, Science and Technology 8, no. 1 (2015): 221–30. http://dx.doi.org/10.14257/ijunesst.2015.8.1.20.

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Mutasem, Khalil Alsmadi. "Modified SEIR and machine learning prediction of the trend of the epidemic of COVID-19 in Jordan under lockdowns impact." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5455–66. https://doi.org/10.11591/ijece.v12i5.pp5455-5466.

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Susceptible exposed infectious recovered (SEIR) is a fitting model for coronavirus disease (COVID-19) spread prediction. Hence, to examine the effect of different levels of social distancing on the spreading of the disease, a variable was introduced in the SEIR equations system used in this work. We also used an artificial intelligence approach using a machine learning (ML) method known as deep neural network. This modified SEIR model was applied on the available initial spread data until June 25th, 2021 for the Hashemite Kingdom of Jordan. Without lockdown in Jordan, the analysis demonstrates

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VITTORINI, PIERPAOLO, ANTONELLA VILLANI, and FERDINANDO DI ORIO. "AN INDIVIDUAL-BASED NETWORKED MODEL WITH PROBABILISTIC RELOCATION OF PEOPLE AND VECTORS AMONG LOCATIONS FOR SIMULATING THE SPREAD OF INFECTIOUS DISEASES." Journal of Biological Systems 18, no. 04 (2010): 847–66. http://dx.doi.org/10.1142/s0218339010003548.

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Eubank et al. propose to study the spread of infectious disease in large urban environments using dynamic bipartite graph modeling the contact pattern, and computer simulations to estimate the evolution of epidemics. Eubank's approach requires a detailed knowledge of individuals, daily routine. In our work we would generalize the model by introducing a stochastic relocation of people and vectors among locations, thanks to distribution functions. Computer simulations are used to produce the infection and death processes. Finally, the paper presents two case studies. The first case study emphasi

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Anagnostopoulos, Christos, Stathes Hadjiefthymiades, and Evangelos Zervas. "An analytical model for multi-epidemic information dissemination." Journal of Parallel and Distributed Computing 71, no. 1 (2011): 87–104. http://dx.doi.org/10.1016/j.jpdc.2010.08.010.

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Bin Zhao, Bin Zhao, Jia-Ming Sun Bin Zhao, Dian-Kui Gao Jia-Ming Sun, and Li-Zhi Xu Dian-Kui Gao. "Research on Online and Offline Mixed Education Mode in Post Epidemic Era Based on Fuzzy Neural Network-Taking Introduction of Petrochemical Equipment Management as an Example." 電腦學刊 33, no. 2 (2022): 095–103. http://dx.doi.org/10.53106/199115992022043302008.

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<p>In the post epidemic era, the online offline mixed teaching mode is in the development period, and college teachers and students are gradually familiar with this teaching mode. Taking the course introduction to petrochemical equipment management as an example, this paper deeply analyzes the development path of online offline mixed teaching mode. First of all, this paper explores the bottleneck of online teaching in the epidemic era, deeply analyzes the relationship between online teaching and offline teaching, and puts forward the possibility of integration of offline teaching and onl

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Bin Zhao, Bin Zhao, Jia-Ming Sun Bin Zhao, Dian-Kui Gao Jia-Ming Sun, and Li-Zhi Xu Dian-Kui Gao. "Research on Online and Offline Mixed Education Mode in Post Epidemic Era Based on Fuzzy Neural Network-Taking Introduction of Petrochemical Equipment Management as an Example." 電腦學刊 33, no. 2 (2022): 095–103. http://dx.doi.org/10.53106/199115992022043302008.

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<p>In the post epidemic era, the online offline mixed teaching mode is in the development period, and college teachers and students are gradually familiar with this teaching mode. Taking the course introduction to petrochemical equipment management as an example, this paper deeply analyzes the development path of online offline mixed teaching mode. First of all, this paper explores the bottleneck of online teaching in the epidemic era, deeply analyzes the relationship between online teaching and offline teaching, and puts forward the possibility of integration of offline teaching and onl

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Prasse, Bastian, and Piet Van Mieghem. "Network Reconstruction and Prediction of Epidemic Outbreaks for General Group-Based Compartmental Epidemic Models." IEEE Transactions on Network Science and Engineering 7, no. 4 (2020): 2755–64. http://dx.doi.org/10.1109/tnse.2020.2987771.

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Liu, Qun, Daqing Jiang, Tasawar Hayat, and Ahmed Alsaedi. "Dynamical behavior of a stochastic epidemic model for cholera." Journal of the Franklin Institute 356, no. 13 (2019): 7486–514. http://dx.doi.org/10.1016/j.jfranklin.2018.11.056.

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Levin, Simon A., Kirk Moloney, Linda Buttel, and Carlos Castillo-Chavez. "Dynamical models of ecosystems and epidemics." Future Generation Computer Systems 5, no. 2-3 (1989): 265–74. http://dx.doi.org/10.1016/0167-739x(89)90046-0.

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Song, Yongmei, and Xuelian Jiao. "A Real-Time Tourism Route Recommendation System Based on Multitime Scale Constraints." Mobile Information Systems 2023 (April 26, 2023): 1–10. http://dx.doi.org/10.1155/2023/4586047.

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In order to increase the capability of real-time intelligent recommendation of tourists’ information on cross-regional city-level tourist routes with epidemic normalization, a real-time intelligent recommendation algorithm for cross-regional city-level tourist routes with epidemic normalization based on multi-time scale constraints is proposed. Under the training of limited samples, the tourist correlation model of the epidemic normalization of cross-regional city-level tourist routes is created. In addition, two kernel functions i.e. the mixed and the global are assembled to excerpt the corre

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Qazza, Ahmad, and Rania Saadeh. "On the Analytical Solution of Fractional SIR Epidemic Model." Applied Computational Intelligence and Soft Computing 2023 (February 2, 2023): 1–16. http://dx.doi.org/10.1155/2023/6973734.

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This article presents the solution of the fractional SIR epidemic model using the Laplace residual power series method. We introduce the fractional SIR model in the sense of Caputo’s derivative; it is presented by three fractional differential equations, in which the third one depends on the first coupled equations. The Laplace residual power series method (LRPSM) is implemented in this research to solve the proposed model, in which we present the solution in a form of convergent series expansion that converges rapidly to the exact one. We analyze the results and compare the obtained approxima

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