Статті в журналах: "Networked Epidemic Model"

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

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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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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 обеспечения в области сетевой эпидемиологии и предложено описание (включая научно-методическое обоснование) дискретной двухэтапной модели эпидемического процесса, призванной разрешить указанное противоречие, а также - разработана методика построения этой модели, включающая соответствующие аналитические выражения для параметров моделирования. In recent years there is a rapid increase of number of attacks on information systems and resources using malicious code and content. Along with this, continuous improvement of self-presence hiding functionality of malware are taking place. Unfortunately, existing arsenal of epidemic process models does not provide an ability to effectively take into account a latent spread period of infection and following reaction of protection systems in cases of practical modeling of network epidemics. In presented research was carried out an analysis of the existing methodological works in the field of network epidemiology and was proposed a description (including scientific-methodological justification) of a discrete two-stage epidemic process model, which is designed to resolve said contradiction, and a methodology for constructing this model was developed, including the corresponding analytical expressions for the modeling parameters.

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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 (January 16, 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 process of simulation to fit the real data from the epidemics. Univariate and multivariate sensitivity analyses were performed to determine the reliability of the model. In addition, we discuss how further testing using this model can inform community interventions to reduce the risk in current and future outbreaks, such as the recently Middle East respiratory syndrome coronavirus (MERS-CoV) epidemic.

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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 (October 12, 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 (January 30, 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 utilized to develop the optimal controller, for which the optimality of the closed-loop system is guaranteed by a separation principle. The designed optimal filter and controller, together with the separation principle, form a complete solution for the optimal control of network information epidemics with stochastic perturbations. Such optimal-filtering-based control strategy is also generalizable to a wider range of networked nonlinear systems. In the numerical experiments on real network data, the effectiveness of the proposed optimal control is validated and confirmed.

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Bellocchio, Francesco, Paola Carioni, Caterina Lonati, Mario Garbelli, Francisco Martínez-Martínez, Stefano Stuard, and Luca Neri. "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 (September 16, 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 variables include information related to the epidemic status and trends in clinical practice patterns of the target clinic, regional epidemic metrics, and the distance-weighted risk estimates of adjacent dialysis units. On the validation dates, there were 30 (5.09%), 39 (6.52%), and 218 (36.03%) clinics with two or more patients with COVID-19 infection during the 2-week prediction window. The performance of the model was suitable in all testing windows: AUC = 0.77, 0.80, and 0.81, respectively. The occurrence of new cases in a clinic propagates distance-weighted risk estimates to proximal dialysis units. Our machine learning sentinel surveillance system may allow for a prompt risk assessment and timely response to COVID-19 surges throughout networked European clinics.

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Chwat, Olivia. "Social Solidarity during the Pandemic: The “Visible Hand” and Networked Social Movements." Kultura i Społeczeństwo 65, no. 1 (March 22, 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 similar to Visible Hand are an example of how external crises highlight problems existing within societies and contribute to their destabilisation. While deliberating over whether the initiative in question is one of ad-hoc episodes of non-organised collective activity, a discussion-and-contact forum, or perhaps a contemporary social movement, she reaches for Manuel Castells’ concept of networked social movements—and asserts that Visible Hand may be acknowledged as a social movement. In closing her paper, she considers the connections between moral bond and solidarity.

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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 (January 2016): e008649. http://dx.doi.org/10.1136/bmjopen-2015-008649.

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Poncela-Casasnovas, Julia, Bonnie Spring, Daniel McClary, Arlen C. Moller, Rufaro Mukogo, Christine A. Pellegrini, Michael J. Coons, Miriam Davidson, Satyam Mukherjee, and Luis A. Nunes Amaral. "Social embeddedness in an online weight management programme is linked to greater weight loss." Journal of The Royal Society Interface 12, no. 104 (March 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 Heckman's sample-selection procedure to account for potential selection bias and data censoring, we found that initial body mass index, adherence to self-monitoring and social networking were significantly correlated with weight loss. Remarkably, greater embeddedness in the network was the variable with the highest statistical significance in our model for weight loss. Average per cent weight loss at six months increased in a graded manner from 4.1% for non-networked members, to 5.2% for those with a few (two to nine) friends, to 6.8% for those connected to the giant component of the network, to 8.3% for those with high social embeddedness. Social networking within an OWM community, and particularly when highly embedded, may offer a potent, scalable way to curb the obesity epidemic and other disorders that could benefit from behavioural changes.

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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 Chinese Internet companies. From a value network perspective, this study considers the different performance aspects of network embeddedness, knowledge management, environmental triggering, and organizational effectiveness. Methodology. The performance of Internet enterprise product innovation may be considered a complex multicriteria decision-making problem. This study used the decision-making trial and evaluation laboratory-based analytic network algorithms to analyze the complex influencing relationship among the factors and calculate their weights. Preference ranking organization method for enrichment evaluation was used for selecting the final solution of multiobjective optimization. Three representative and influential Internet companies in China were selected for empirical analysis and practical evaluation, to find the gap between product innovation performance and expectations and present development suggestions. Findings. The study results reveal that relational embeddedness, knowledge sharing degree, cognitive embeddedness, structural embeddedness, knowledge absorptive capacity, and corporate strategic orientation are important to Chinese Internet enterprise product innovation performance. Practical Implications. This Internet enterprise product innovation performance evaluation framework can be used by Internet business operators to assess product innovation performance and identify areas of improvement. This study provides new product innovation ideas for Chinese Internet companies during an epidemic situation. Originality/Value. This study contributes to the present body of knowledge by using the DANP model to conduct empirical analysis, considering value network-related performance aspects to evaluate the product innovation performance of Internet enterprise in China.

13

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 (February 6, 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 identifying influential nodes than traditional methods. However, SPON overlooks the heterogeneity of neighbor information, derived from the asymmetry properties of natural networks, leading to its lower accuracy in identifying essential nodes. To sustain the efficiency of the SPON method pertaining to the local method, as opposed to global approaches, we propose an improved local approach, called the SSPN (sum of the structural proportion of neighbors), adapted from the SPON method. The SSPN method classifies neighbors based on the h-index values of nodes, emphasizing the diversity of asymmetric neighbor structure information by considering the local clustering coefficient and addressing the accuracy limitations of the SPON method. To test the performance of the SSPN, we conducted simulation experiments on six real networks using the Susceptible–Infected–Removed (SIR) model. Our method demonstrates superior monotonicity, ranking accuracy, and robustness compared to seven benchmarks. These findings are valuable for developing effective methods to discover and safeguard influential nodes within complex networked systems.

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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 (November 2016): 3379–90. http://dx.doi.org/10.3934/dcdsb.2016102.

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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 (June 2017): 159–69. http://dx.doi.org/10.1109/tcns.2015.2482221.

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16

Hwang, Wonjun, Yoora Kim, and Kyunghan Lee. "Augmenting Epidemic Models with Graph Neural Networks." ACM SIGMETRICS Performance Evaluation Review 50, no. 4 (April 26, 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 both node-level and network-wide perspectives with high accuracy having median relative errors below 15% for a wide range of scenarios.

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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 (April 17, 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) incorporating human intervention factors. Firstly, we analyzed the correlations and lagged effects between four human factors and epidemic cases in three representative countries, and found that these four factors typically delayed the epidemic case data by approximately 15 days. On this basis, a multivariate epidemic prediction model (M-LSTM) was developed. The model prediction results show that coupling human intervention factors generally improves model performance, but adding certain intervention factors also results in lower performance. Overall, a multivariate deep learning model with coupled variable correlation and lag outperformed other comparative models, and thus validated its effectiveness in predicting infectious diseases.

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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 (August 13, 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. The cities are selected both by geographic location and by the number of population. The model consists of twenty seven differential equations. An algorithm for reverse analysis of the epidemic model has been developed. The initial data for solving the problem were the data on the population, the intensity of process transitions from one state to another, as well as data on the infection rate of the population at given time moments. The paper also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by type of model (basic SEIR model, SIRD model, adaptive behavioral model, modified SEIR models), and by country (in Poland, France, Spain, Greece and others) and an overview of the applications that can be solved using epidemic spread modeling. Additional environmental parameters that affect the modeling of the spread of epidemics and can be taken into account to improve the accuracy of the results are considered. Based on the results of the modeling, the most likely source cities of the epidemic beginning in Russia, as well as the moment of its beginning, have been identified. The reliability of the estimates obtained is largely determined by the reliability of the statistics used on the development of COVID-19 and the available data on transportation network, which are in the public domain.

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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 (October 19, 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-epidemic data to forecast the possible material demand when the disease appears. With the help of the forecast results, the model is able to achieve cross-regional material scheduling. The key objective of material scheduling is, of course, to reach a balance between the cost and the material support rate. In order to fulfil this important requirement, a multi-objective function, which aims to minimize costs and maximize the material support rate, is constructed. Then, an ant colony algorithm, suitable for time and region problems, is employed to provide a solution to the constructed function. Finally, the validity of the model is verified via a case study. The results show that the model can coordinate and deploy a variety of materials from multiple sources according to changes in an epidemic situation and provide reliable support in decisions regarding the dynamic dispatch of emergency materials during an epidemic period.

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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 justified anti-epidemic measures to reduce the dynamics of the incidence of influenza. The study aims to develop a seasonal autoregressive integrated moving average (SARIMA) model for influenza epidemic process simulation and to investigate the experimental results of the simulation. The work is targeted at the influenza epidemic process and its dynamic in the territory of Ukraine. The subjects of the research are methods and models of epidemic process simulation, which include machine learning methods, in particular the SARIMA model. To achieve the aim of the research, we have used methods of forecasting and have built the influenza epidemic process SARIMA model. Because of experiments with the developed model, the predictive dynamics of the epidemic process of influenza for 10 weeks were obtained. Such a forecast can be used by persons making decisions on the implementation of anti-epidemic and deterrent measures if the forecast exceeds the epidemic thresholds of morbidity. Conclusions. The paper describes experimental research on the application of the SARIMA model to the epidemic process of influenza simulation. Models have been verified by influenza morbidity in the Kharkiv region (Ukraine) in epidemic seasons for the time ranges as follows: 2017-18, 2018-19, 2019-20, and 2020-21. Data were provided by the Kharkiv Regional Centers for Disease Control and Prevention of the Ministry of Health of Ukraine. The forecasting results show a downward trend in the dynamics of the epidemic process of influenza in the Kharkiv region. It is due to the introduction of anti-epidemic measures aimed at combating COVID-19. Activities such as wearing masks, social distancing, and lockdown also contribute to reducing seasonal influenza epidemics.

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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 (September 2022): 1009–18. http://dx.doi.org/10.53106/160792642022092305009.

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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 wave, 880 confirmed cases were reported from May 03, 2021, to May 17, 2021. The infection source attribute, i.e., local vs. imported, made the transmission models to be structured differently between the first and the second wave. In the first wave, it was found that a community outbreak could easily happen when one node got infected without knowing when and where the transmission occurred. In contrast, in the second wave, it was found that the gender attribute was more effective than the age attribute in quickly identifying the differences in the transmission models among all the confirmed cases.

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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 (August 13, 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 the dynamics of the total number of cases (C), the total number of recoveries and deaths (R), and the number of active cases (I). The system parameters are the percentage increase in the C(t) value and the characteristic of the dynamic balance of the epidemiological process, first introduced in this paper. The principle of the dynamic balance of epidemiological process assumes that any process has the property of similarity between the value of the total number of cases in the past and the value of the total number of recoveries and deaths at present. To calculate the values of the dynamic balance characteristic, an integer linear programming problem is used. In general, the dynamic characteristic of the epidemiological process is not constant. An epidemiological process the dynamic characteristic of which is not constant is called non-stationary. To construct mid-term forecasts of indicators of the epidemiological process at intervals of stationarity of the epidemiological process, a special algorithm has been developed. The question of using this algorithm on the intervals of stationarity and non-stationarity is being examined. Examples of the CIR model application for making forecasts of the considered indicators for the epidemic in Russia in May-June 2020 are given.

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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 COVID-19 epidemic curve in various regions and over various time periods. Based on the Wuhan exodus, we built a migration transmission risk model. From January 24 to February 19, 2020, we predicted the location, severity, and timing of epidemics in various parts of mainland China. We showed how discrepancies in model predictions might be utilized to measure transmission load in different parts of the country. Higher transmission risk indices suggest more community transmission in the region. According to the study, states with lower transmission risk indices but fewer cases than expected may have taken highly effective public health measures.

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Wang, Xu, Bo Song, Wei Ni, Ren Ping Liu, Y. Jay Guo, Xinxin Niu, and Kangfeng Zheng. "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 links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.

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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 in parameter estimation. For DNNs, the lack of interpretability makes it difficult to explain the prediction results; thus, it cannot provide an in-depth understanding of the transmission mechanism of the pandemic. We propose a fusion model to leverage the merits of both models and resolve their shortcomings. The fusion model extracts epidemic-related knowledge from the state-of-the-art SEIDR compartmental model to guide the training of the GRU model, which can preserve the interpretability and achieve a good performance in predicting epidemic dynamics. This model can help to enhance the online epidemic supervising system by providing more accurate prediction results and deeper analysis. Our extensive experiments across multiple epidemic datasets from six European countries demonstrate that our model outperforms existing state-of-the-art baselines in predicting the active confirmed cases. More importantly, by analyzing the effective reproductive number, our method can reveal the risk of the second wave of the epidemic in Europe and justify the importance of social distancing to control the outbreak of the epidemic.

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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 (February 28, 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, the reduction in dimensions, and finally the selection of new variables that best synthesize information the algorithm needs. This study uses a data set designed for classification studies to predict whether a patient is suffering from COVID-19 based on the following 11 variables: Country, Age, Fever, Bodypain, Runny_nose, Difficult_in_breathing, Nasal_congestion, Sore_throat, Gender, Severity, and Contact_with_covid_patient. We compared default RF to five other machine learning models: GNB, LR, SVM, KNN, and DT. RF proved to be the best classifier of all with the following metrics: Accuracy (94.9%), Precision (94.0%), Recall (96.6%), and F1 Score (95.2%). Our model, RF EP, produced the following metrics: Accuracy (94.9%), Precision (93.1%), Recall (97.7%), and F1 Score (95.3%). The performance gain by RF EP on the Recall metric compared to default RF allowed us to propose a new model with a better score than default RF in the limitation of the virus propagation on the dataset used in this study.

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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 (April 27, 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 linear stationary systems and the phenomenon of propagation in networks as the basis of the method. Complex interdependencies between their elements characterize various systems. The method of synthesizing hardware and software means of ensuring the stability of a corporate computer network consists of such steps as representing networks as a linear stationary system, modelling the stability of a computer network in the context of epidemics by using virtual network expansion, studying the stability of a computer network in the context of uncertain data transmission and virtual network expansion, processing input data received from the modelled computer network, etc. To solve the problem, the method involves the theory of linear stationary systems and the use of the NiR metric, which can successfully reflect the possible importance of a node when it comes to the dynamics of an epidemic for various network models to ensure network resilience. The method is tested by simulations, the results of which show a high correlation with the actual propagation dynamics modeled by SI and SIR processes. NiR also shows a small variance, which means it is reliable for different computer network topologies. The method also involves finding the most critical nodes in a computer network, for which a cascading failure model was used, which models overloaded nodes as non-functional.

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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 (September 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 kernel density estimation (KDE) algorithm as the attacking tasks automatically. This virus can also attack lower-encryption-level routers first and acquire fast-growing numbers of infected routers on the initial stage. We simulate an epidemic behavior in the collected spatial coordinate of routers in a typical area in Beijing City, where 56.0% routers are infected in 18 hours. This dramatical defeat benefits from the correct infection seed selection and a low-encryption-level priority. This work provides a framework for a computer-algorithm-enhanced virus exploration and gives some insights on offence and defence to both hackers and computer users.

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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 (November 1, 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 (January 31, 2015): 221–30. http://dx.doi.org/10.14257/ijunesst.2015.8.1.20.

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32

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

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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 (December 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 emphasizes the effect of using probabilistic relocation in a particular social network, while the second discusses how vector-borne diseases could be taken into account.

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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 (April 2022): 095–103. http://dx.doi.org/10.53106/199115992022043302008.

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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 online teaching. Based on the above analysis results, this paper summarizes the opportunities of Blended Teaching in the post epidemic era. This paper puts forward the practice path of the online and offline mixed teaching mode of “Introduction to petrochemical equipment management”. Finally, the online and offline mixed teaching quality evaluation model of “Introduction to petrochemical equipment management” is constructed, the mixed teaching quality evaluation index system is determined, and the evaluation model based on improved firefly algorithm optimization fuzzy neural network is constructed, and the example analysis is carried out, and the online and offline mixed teaching quality of “Introduction to petrochemical equipment management” is determined as good, It provides a favorable theoretical basis for the reform strategy of mixed teaching mode, and also verifies the effectiveness of the evaluation model.

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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 (April 2022): 095–103. http://dx.doi.org/10.53106/199115992022043302008.

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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 online teaching. Based on the above analysis results, this paper summarizes the opportunities of Blended Teaching in the post epidemic era. This paper puts forward the practice path of the online and offline mixed teaching mode of “Introduction to petrochemical equipment management”. Finally, the online and offline mixed teaching quality evaluation model of “Introduction to petrochemical equipment management” is constructed, the mixed teaching quality evaluation index system is determined, and the evaluation model based on improved firefly algorithm optimization fuzzy neural network is constructed, and the example analysis is carried out, and the online and offline mixed teaching quality of “Introduction to petrochemical equipment management” is determined as good, It provides a favorable theoretical basis for the reform strategy of mixed teaching mode, and also verifies the effectiveness of the evaluation model.

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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 (October 1, 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 (September 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 (September 1989): 265–74. http://dx.doi.org/10.1016/0167-739x(89)90046-0.

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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 approximate solutions to those obtained from other methods. Figures and tables are illustrated to show the efficiency of the LRPSM in handling the proposed SIR model.

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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 correspondence features of the epidemic normalization cross-regional city-level tourist route recommendation information. As a result, the well-known particle swarm optimization (PSO) procedure and algorithm with multitime scale constraints are adopted to carry out the adaptive learning of the epidemic normalization cross-regional city-level tourist route recommendation, and the convergence control of the recommended method is comprehended through mining the geographic information data sets of cities. This paper analyzes the universality and ergodicity of tourists' personal interest preferences and social characteristics in urban tourism and combines a gradient algorithm to carry out particle swarm evolution and self-adaptive optimization for the recommendation of cross-regional city-level tourist routes with a normalized epidemic situation, so as to realize the group real-time intelligent recommendation of tourists’ information on cross-regional city-level tourist routes with the normalized epidemic situation. The model outcomes indicate that the exactitude and precision of cross-regional city-level tourism route information recommendation with this algorithm are decent, and the convergence of the swarm intelligence optimization (SIO) problem is robust, which can circumvent dipping into the local optimal solution in the process of real-time intelligent recommendation of tourism routes and improve the intelligence and global stability of cross-regional city-level tourism route recommendation with epidemic normalization.

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Krivtsov, Serhii, Ievgen Meniailov, Kseniia Bazilevych, and Dmytro Chumachenko. "Predictive model of COVID-19 epidemic process based on neural network." Radioelectronic and Computer Systems, no. 4 (November 29, 2022): 7–18. http://dx.doi.org/10.32620/reks.2022.4.01.

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The COVID-19 pandemic, which has been going on for almost three years, has shown that public health systems are not ready for such a challenge. Measures taken by governments in the healthcare sector in the context of a sharp increase in the pressure on it include containment of the transmission and spread of the virus, providing sufficient space for medical care, ensuring the availability of testing facilities and medical care, and mobilizing and retraining medical personnel. The pandemic has changed government and business processes, digitalizing the economy and healthcare. Global challenges have stimulated data-driven medicine research. Forecasting the epidemic process of infectious processes would make it possible to assess the scale of the impending pandemic to plan the necessary control measures. The study builds a model of the COVID-19 epidemic process to predict its dynamics based on neural networks. The target of the research is the infectious diseases epidemic process in the example of COVID-19. The research subjects are the methods and models of epidemic process simulation based on neural networks. As a result of this research, a simulation model of COVID-19 epidemic process based on a neural network was built. The model showed high accuracy: from 93.11% to 93.96% for Germany, from 95.53% to 95.54% for Japan, from 97.49% to 98.43% for South Korea, from 93.34% up to 94.18% for Ukraine, depending on the forecasting period. The assessment of absolute errors confirms that the model can be used in healthcare practice to develop control measures to contain the COVID-19 pandemic. The respective contribution of this research is two-fold. Firstly, the development of models based on the neural network approach will allow estimate the accuracy of such methods applied to the simulation of the COVID-19 epidemic process. Secondly, an investigation of the experimental study with a developed model applied to data from four countries will contribute to empirical evaluation of the effectiveness of its application not only to COVID-19 but also to other infectious diseases simulations. Conclusions. The research’s significance lies in the fact that automated decision support systems for epidemiologists and other public health workers can improve the efficiency of making anti-epidemic decisions. This study is especially relevant in the context of the escalation of the Russian war in Ukraine when the healthcare system's resources are limited.

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Huang, Xun C., and Minaya Villasana. "An extension of the Kermack–McKendrick model for AIDS epidemic." Journal of the Franklin Institute 342, no. 4 (July 2005): 341–51. http://dx.doi.org/10.1016/j.jfranklin.2004.11.008.

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Mohammadi, Alireza, Ievgen Meniailov, Kseniia Bazilevych, Sergey Yakovlev, and Dmytro Chumachenko. "Comparative study of linear regression and SIR models of COVID-19 propagation in Ukraine before vaccination." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 3 (October 5, 2021): 5–18. http://dx.doi.org/10.32620/reks.2021.3.01.

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The global COVID-19 pandemic began in December 2019 and spread rapidly around the world. Worldwide, more than 230 million people fell ill, 4.75 million cases were fatal. In addition to the threat to health, the pandemic resulted in social problems, an economic crisis and the transition of an ordinary life to a "new reality". Mathematical modeling is an effective tool for controlling the epidemic process of COVID-19 in specified territories. Modeling makes it possible to predict the future dynamics of the epidemic process and to identify the factors that affect the increase in incidence in the greatest way. The simulation results enable public health professionals to take effective evidence-based responses to contain the epidemic. The study aims to develop machine learning and compartment models of COVID-19 epidemic process and to investigate experimental results of simulation. The object of research is COVID-19 epidemic process and its dynamics in territory of Ukraine. The research subjects are methods and models of epidemic process simulation, which include machine learning methods and compartment models. To achieve this aim of the research, we have used machine learning forecasting methods and have built COVID-19 epidemic process linear regression model and COVID-19 epidemic process compartment model. Because of experiments with the developed models, the predictive dynamics of the epidemic process of COVID-19 for 30 days were obtained for confirmed cases, recovered and death. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 1.15, 0.037 and 1.39 percent deviant, respectively, with a linear regression model. For ‘Confirmed’, ‘Recovered’ and ‘Death’ cases mean errors have almost 3.29, 1.08, and 0.71 percent deviant, respectively, for the SIR model. Conclusions. At this stage in the development of the epidemic process of COVID-19, it is more expedient to use a linear model to predict the incidence rate, which has shown higher accuracy and efficiency, the reason for that lies on the fact that the used linear regression model for this research was implemented on merely 30 days (from fifteen days before 2nd of March) and not the whole dataset of COVID-19. Also, it is expected that if we try to forecast in longer time ranges, the linear regression model will lose precision. Alternatively, since SIR model is more comprised in including more factors, the model is expected to perform better in fore-casting longer time ranges.

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Xiang, Nan, Xiao Tang, Huiling Liu, and Xiaoxia Ma. "SELHR: A Novel Epidemic-Based Model for Information Propagation in Complex Networks." Mobile Information Systems 2022 (October 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/5016274.

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The study of information spreading based on the complex network theory and topological structure has become an important issue in complex networks. Plenty of infectious disease models are widely used for information diffusion research in complex networks. Based on these state-of-the-art models, a new epidemic dynamic model with dynamic evolution equations is proposed and performed on the homogeneous and heterogeneous networks, respectively, in this paper. Meanwhile, we divide the propagation states into two states: L and H (low propagation ability groups and high propagation ability groups) and consider the transformation of these two states in our model. Then, the equilibria and stability of the model are analyzed for both homogeneous and heterogeneous networks to verify the validity of the proposed model. Finally, simulation results illustrate that the proposed model and information propagation dynamic evolution equations are reasonable and effective. Experiments with effect factors also reveal the interaction mechanism and the diffusion process of the proposed model in complex networks.

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Xu, Zhongpu, Yu Wang, Naiqi Wu, and Xinchu Fu. "Propagation Dynamics of a Periodic Epidemic Model on Weighted Interconnected Networks." IEEE Transactions on Network Science and Engineering 7, no. 3 (July 1, 2020): 1545–56. http://dx.doi.org/10.1109/tnse.2019.2939074.

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Prasse, Bastian, and Piet Van Mieghem. "The Viral State Dynamics of the Discrete-Time NIMFA Epidemic Model." IEEE Transactions on Network Science and Engineering 7, no. 3 (July 1, 2020): 1667–74. http://dx.doi.org/10.1109/tnse.2019.2946592.

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Angali, Adel, Musa Mojarad, and Hassan Arfaeinia. "ILSHR Rumor Spreading Model by Combining SIHR and ILSR Models in Complex Networks." International Journal of Intelligent Systems and Applications 13, no. 6 (December 8, 2021): 51–59. http://dx.doi.org/10.5815/ijisa.2021.06.05.

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Rumor is an important form of social interaction. However, spreading harmful rumors can have a significant negative impact on social welfare. Therefore, it is important to examine rumor models. Rumors are often defined as unconfirmed details or descriptions of public things, events, or issues that are made and promoted through various tools. In this paper, the Ignorant-Lurker-Spreader-Hibernator-Removal (ILSHR) rumor spreading model has been developed by combining the ILSR and SIHR epidemic models. In addition to the characteristics of the lurker group of ILSR, this model also considers the characteristics of the hibernator group of the SIHR model. Due to the complexity of the complex network structure, the state transition function for each node is defined based on their degree to make the proposed model more efficient. Numerical simulations have been performed to compare the ILSHR rumor spreading model with other similar models on the Sina Weibo dataset. The results show more effective ILSHR performance with 95.83% accuracy than CSRT and SIR-IM models.

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Masood, Zaheer, Raza Samar, and Muhammad Asif Zahoor Raja. "Design of fractional order epidemic model for future generation tiny hardware implants." Future Generation Computer Systems 106 (May 2020): 43–54. http://dx.doi.org/10.1016/j.future.2019.12.053.

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Feng, Tao, Zhipeng Qiu, and Yi Song. "Global analysis of a vector-host epidemic model in stochastic environments." Journal of the Franklin Institute 356, no. 5 (March 2019): 2885–900. http://dx.doi.org/10.1016/j.jfranklin.2019.01.033.

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Chumachenko, Dmytro, Pavlo Pyrohov, Ievgen Meniailov, and Tetyana Chumachenko. "Impact of war on COVID-19 pandemic in Ukraine: the simulation study." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 2 (May 18, 2022): 6–23. http://dx.doi.org/10.32620/reks.2022.2.01.

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The COVID-19 pandemic has posed a challenge to public health systems worldwide. As of March 2022, almost 500 million cases have been reported worldwide. More than 6.2 million people died. The war that Russia launched for no reason on the territory of Ukraine is not only the cause of the death of thousands of people and the destruction of dozens of cities but also a large-scale humanitarian crisis. The military invasion also affected the public health sector. The impossibility of providing medical care, non-compliance with sanitary conditions in areas where active hostilities are occurring, high population density during the evacuation, and other factors contribute to a new stage in the spread of COVID-19 in Ukraine. Building an adequate model of the epidemic process will make it possible to assess the actual statistics of the incidence of COVID-19 and assess the risks and effectiveness of measures to curb the curse of the disease epidemic process. The article aims to develop a simulation model of the COVID-19 epidemic process in Ukraine and to study the results of an experimental study in war conditions. The research is targeted at the epidemic process of COVID-19 under military conditions. The subjects of the study are models and methods for modeling the epidemic process based on statistical machine learning methods. To achieve the study's aim, we used forecasting methods and built a model of the COVID-19 epidemic process based on the polynomial regression method. Because of the experiments, the accuracy of predicting new cases of COVID-19 in Ukraine for 30 days was 97,98%, and deaths of COVID-19 in Ukraine – was 99,87%. The model was applied to data on the incidence of COVID-19 in Ukraine for the first month of the war (02/24/22 - 03/25/22). The calculated predictive values showed a significant deviation from the registered statistics. Conclusions. This article describes experimental studies of implementing the COVID-19 epidemic process model in Ukraine based on the polynomial regression method. The constructed model was sufficiently accurate in deciding on anti-epidemic measures to combat the COVID-19 pandemic in the selected area. The study of the model in data on the incidence of COVID-19 in Ukraine during the war made it possible to assess the completeness of the recorded statistics, identify the risks of the spread of COVID-19 in wartime, and determine the necessary measures to curb the epidemic curse of the incidence of COVID-19 in Ukraine. The investigation of the experimental study results shows a significant decrease in the registration of the COVID-19 incidence in Ukraine. An analysis of the situation showed difficulty in accessing medical care, a reduction in diagnosis and registration of new cases, and the war led to the intensification of the COVID-19 epidemic process.

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