Статті в журналах: "Information epidemic"

1

Brierly, Joseph E. "Epidemic Cycle." Journal of Biotechnology & Bioinformatics Research 2, no. 1 (March 31, 2020): 1–4. http://dx.doi.org/10.47363/jbbr/2019(1)104.

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This article explains the natural progression of a typical viral epidemic. Epidemics historically go through a progressive cycle because once a person is victimized normally there is an immune and non-infectious period of one or more years. At this time both immunity and infectiousness has not been scientifically verified for the Covid-19 virus. However, likely the Covid-19 virus will progress the way of other past virus epidemics. At present there is much untested and possibly unreliable information regarding the Covid-19 epidemic. This article shows the most likely way the Covid-19 epidemic will progress over time.

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2

The Lancet. "Fast-tracking epidemic information." Lancet 348, no. 9042 (December 1996): 1599. http://dx.doi.org/10.1016/s0140-6736(96)21050-5.

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3

Nikulina, Olena, Valerii Severyn, Mariia Naduieva, and Anton Bubnov. "MODELING THE DEVELOPMENT OF EPIDEMIS BASED ON INFORMATION TECHNOLOGIES OF OPTIMIZATION." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 2 (6) (December 28, 2021): 47–52. http://dx.doi.org/10.20998/2079-0023.2021.02.08.

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Mathematical models of the epidemic have been developed and researched to predict the development of the COVID-19 coronavirus epidemic on thebasis of information technology for optimizing complex dynamic systems. Mathematical models of epidemics SIR, SIRS, SEIR, SIS, MSEIR in theform of nonlinear systems of differential equations are considered and the analysis of use of mathematical models for research of development ofepidemic of coronavirus epidemic COVID-19 is carried out. Based on the statistics of the COVID-19 coronavirus epidemic in the Kharkiv region, theinitial values of the parameters of the models of the last wave of the epidemic were calculated. Using these models, the program of the first-degreesystem method from the module of information technology integration methods for solving nonlinear systems of differential equations simulated thedevelopment of the last wave of the epidemic. Simulation shows that the number of healthy people will decrease and the number of infected peoplewill increase. In 12 months, the number of infected people will reach its maximum and then begin to decline. The information technology ofoptimization of dynamic systems is used to identify the parameters of the COVID-19 epidemic models on the basis of statistical data on diseases in theKharkiv region. Using the obtained models, the development of the last wave of the COVID-19 epidemic in Kharkiv region was predicted. Theprocesses of epidemic development according to the SIR-model with weakening immunity are given, with the values of the model parameters obtainedas a result of identification. Approximately 13 months after the outbreak of the epidemic, the number of infected people will reach its maximum andthen begin to decline. In 10 months, the entire population of Kharkiv region will be infected. These results will allow us to predict possible options forthe development of the epidemic of coronavirus COVID-19 in the Kharkiv region for the timely implementation of adequate anti-epidemic measures.

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4

Fischer, T., T. Gerwald, S. Lajos, S. Woellert, Ch Kuttler, and J. Draeger. "Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19." Journal of Physics: Conference Series 2514, no. 1 (May 1, 2023): 012009. http://dx.doi.org/10.1088/1742-6596/2514/1/012009.

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Abstract A common way to model an epidemic — restricted to contagion aspects only — is a modification of the Kermack-McKendrick SIR Epidemic model (SIR model) with differential equations. (Mis-)Information about epidemics may influence the behavior of the people and thus the course of epidemics as well. We have thus coupled an extended SIR model of the COVID-19 pandemic with a compartment model of the (mis-)information-based attitude of the population towards epidemic countermeasures. The resulting combined model is checked concerning basic plausibility properties like positivity and boundedness. It is calibrated using COVID-19 data from RKI and attitude data provided by the COVID-19 Snapshot Monitoring (COSMO) study. The values of parameters without corresponding observation data have been determined using an L2 -fit under mild additional assumptions. The predictions of the calibrated model are essentially in accordance with observations. An uncertainty analysis of the model shows, that our results are in principle stable under measurement errors. We also assessed the scale, at which specific parameters can influence the evolution of epidemics. Another result of the paper is that in a multi-domain epidemic model, the notion of controlled reproduction number has to be redefined when being used as an indicator of the future evolution of epidemics.

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5

Shrum, Wesley, John Aggrey, Andre Campos, Janaina Pamplona da Costa, Jan Joseph, Pablo Kreimer, Rhiannon Kroeger, et al. "Who’s afraid of Ebola? Epidemic fires and locative fears in the Information Age." Social Studies of Science 50, no. 5 (June 29, 2020): 707–27. http://dx.doi.org/10.1177/0306312720927781.

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Epidemics have traditionally been viewed as the widespread occurrence of infectious disease within a community, or a sudden increase above what is typical. But modern epidemics are both more and less than the diffusion of viral entities. We argue that epidemics are ‘fire objects’, using a term coined by Law and Singleton: They generate locative fears through encounters that focus attention on entities that are unknown or imprecisely known, transforming spaces and humans into indeterminate dangers, alternating appearance and absence. The Ebola epidemic of 2014 had more complex impacts than the number of infections would suggest. We employ multi-sited qualitative interviews to argue that locative fear is the essence of modern global epidemics. In the discussion we contrast Ebola with both the Zika epidemic that followed and the ongoing coronavirus (COVID-19) pandemic.

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6

PESEN, Birgül, and Musaye KONAK ÖZÇELİK. "THE IMPACT OF SOME OUTSTANDING DISEASES FROM PAST TO PRESENT ON SOCIETY." Zeitschrift für die Welt der Türken / Journal of World of Turks 13, no. 1 (April 15, 2021): 227–48. http://dx.doi.org/10.46291/zfwt/130112.

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Diseases can be seen in people in different periods throughout history. Some of these diseases have become epidemics. Epidemic diseases from the past to the present have left deep marks in the society. Measures against epidemics have also been attempted in the past. Since the source of the epidemic diseases seen in the history and the effects of the disease are unknown, fear prevailed in the society. With the changes in the process and the steps taken in the field of health, the appropriate vaccine against epidemic diseases was found and the quarantine system was put into operation. However, despite the steps taken, it was understood that the public did not have enough information, so efforts were made to raise the awareness of the public. Despite the studies, the lethal effect of epidemics has led to ruptures in relations within society. The epidemic also had an impact on the economy and famines appeared in the society. Epidemic diseases affect the socio-economic life of the society very badly, and negativities have been noticed in individuals due to the epidemic. It has been found that the epidemic mostly affects people with weak body resistance (elderly, children). The Ottoman State continued its determination and acted within the framework of its activities in the fight against epidemic diseases. This determination continues today, and the state continues its struggle against the epidemic with its policies. In this study, after giving historical information about some epidemic diseases such as plague, syphilis, cholera, smallpox, malaria, measles, new coronavirus (covit-19), the effect of these diseases on the society was tried to be explained. Keywords: Epidemic, Society, Ottoman State, Disease, Health.

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7

Feng, Minyu, Xiangxi Li, Yuhan Li, and Qin Li. "The impact of nodes of information dissemination on epidemic spreading in dynamic multiplex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 4 (April 2023): 043112. http://dx.doi.org/10.1063/5.0142386.

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Epidemic spreading processes on dynamic multiplex networks provide a more accurate description of natural spreading processes than those on single layered networks. To describe the influence of different individuals in the awareness layer on epidemic spreading, we propose a two-layer network-based epidemic spreading model, including some individuals who neglect the epidemic, and we explore how individuals with different properties in the awareness layer will affect the spread of epidemics. The two-layer network model is divided into an information transmission layer and a disease spreading layer. Each node in the layer represents an individual with different connections in different layers. Individuals with awareness will be infected with a lower probability compared to unaware individuals, which corresponds to the various epidemic prevention measures in real life. We adopt the micro-Markov chain approach to analytically derive the threshold for the proposed epidemic model, which demonstrates that the awareness layer affects the threshold of disease spreading. We then explore how individuals with different properties would affect the disease spreading process through extensive Monte Carlo numerical simulations. We find that individuals with high centrality in the awareness layer would significantly inhibit the transmission of infectious diseases. Additionally, we propose conjectures and explanations for the approximately linear effect of individuals with low centrality in the awareness layer on the number of infected individuals.

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8

Kleczkowski, A., and C. A. Gilligan. "Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease." Journal of The Royal Society Interface 4, no. 16 (July 17, 2007): 865–77. http://dx.doi.org/10.1098/rsif.2007.1036.

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Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about ‘similar’ replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride . The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a ‘library’ of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.

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9

Feng, Qingxiang, Haipeng Wei, Jun Hu, Wenzhe Xu, Fan Li, Panpan Lv, and Peng Wu. "Analysis of the attention to COVID-19 epidemic based on visibility graph network." Modern Physics Letters B 35, no. 19 (June 1, 2021): 2150316. http://dx.doi.org/10.1142/s0217984921503164.

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Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

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10

Wu, Xifen, and Haibo Bao. "The impact of positive and negative information on SIR-like epidemics in delayed multiplex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 11 (November 2022): 113141. http://dx.doi.org/10.1063/5.0126799.

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In order to better study the interaction between epidemic propagation and information diffusion, a new coupling model on multiplex networks with time delay is put forward in this paper. One layer represents the information diffusion about epidemics. There is not only information about the positive prevention of infectious diseases but also negative preventive information. Meanwhile, the dissemination of information at this layer will be influenced by the mass media, which can convey positive and reliable preventive measures to help the public avoid exposure to contagion. The other layer represents the transmission of infectious diseases, and the public in this layer no longer only exchange information related to infectious diseases in the virtual social network like the information layer but spread infectious diseases through contact among people. The classical SIR model is used to model for epidemic propagation. Since each infected individual needs to spend enough time to recover, the infected one at one time does not necessarily change to the recovered one at the next time, so time delay is an essential factor to be considered in the model. Based on the microscopic Markov chain approach, this paper obtains an explicit expression for epidemic threshold in the two-layered multiplex networks with time delay, which reveals some main factors affecting epidemic threshold. In particular, the time delay has a noticeable effect on the epidemic threshold to some extent. Finally, the influence of these main factors on the epidemic threshold and their interaction are proved through numerical simulations.

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11

Zhang, Libo, Cong Guo, and Minyu Feng. "Effect of local and global information on the dynamical interplay between awareness and epidemic transmission in multiplex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 8 (August 2022): 083138. http://dx.doi.org/10.1063/5.0092464.

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Recent few years have witnessed a growing interest in exploring the dynamical interplay between awareness and epidemic transmission within the framework of multiplex networks. However, both local and global information have significant impacts on individual awareness and behavior, which have not been adequately characterized in the existing works. To this end, we propose a local and global information controlled spreading model to explore the dynamics of two spreading processes. In the upper layer, we construct a threshold model to describe the awareness diffusion process and introduce local and global awareness information as variables into an individual awareness ratio. In the lower layer, we adopt the classical susceptible-infected-susceptible model to represent the epidemic propagation process and introduce local and global epidemic information into individual precaution degree to reflect individual heterogeneity. Using the microscopic Markov chain approach, we theoretically derive the threshold for epidemic outbreaks. Our findings suggest that the local and global information can motivate individuals to increase self-protection awareness and take more precaution measures, thereby reducing disease infection probability and suppressing the spread of epidemics.

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12

Eugster, P. T., R. Guerraoui, A. M. Kermarrec, and L. Massoulie. "Epidemic information dissemination in distributed systems." Computer 37, no. 5 (May 2004): 60–67. http://dx.doi.org/10.1109/mc.2004.1297243.

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13

Wu, Jiang, Renxian Zuo, Chaocheng He, Hang Xiong, Kang Zhao, and Zhongyi Hu. "The effect of information literacy heterogeneity on epidemic spreading in information and epidemic coupled multiplex networks." Physica A: Statistical Mechanics and its Applications 596 (June 2022): 127119. http://dx.doi.org/10.1016/j.physa.2022.127119.

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14

Earn, David J. D., Junling Ma, Hendrik Poinar, Jonathan Dushoff, and Benjamin M. Bolker. "Acceleration of plague outbreaks in the second pandemic." Proceedings of the National Academy of Sciences 117, no. 44 (October 19, 2020): 27703–11. http://dx.doi.org/10.1073/pnas.2004904117.

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Historical records reveal the temporal patterns of a sequence of plague epidemics in London, United Kingdom, from the 14th to 17th centuries. Analysis of these records shows that later epidemics spread significantly faster (“accelerated”). Between the Black Death of 1348 and the later epidemics that culminated with the Great Plague of 1665, we estimate that the epidemic growth rate increased fourfold. Currently available data do not provide enough information to infer the mode of plague transmission in any given epidemic; nevertheless, order-of-magnitude estimates of epidemic parameters suggest that the observed slow growth rates in the 14th century are inconsistent with direct (pneumonic) transmission. We discuss the potential roles of demographic and ecological factors, such as climate change or human or rat population density, in driving the observed acceleration.

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15

Séguy, Isabelle, Nicolas Bernigaud, Arnaud Bringé, Michel Signoli, and Stéfan Tzortzis. "A geographic information system for the study of past epidemics: The 1705 epidemic in Martigues (Bouches-du-Rhône, France)." Canadian Studies in Population 39, no. 3-4 (February 14, 2013): 107. http://dx.doi.org/10.25336/p6x024.

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At the beginning of the 18th century, the Provence region was hit by several severe epidemics whose causes are still not clearly understood.To draw up epidemic profiles and to identify the pathogenic agents concerned, we constituted a large onomastic database and built ageographic information system for Martigues, a medium-sized community in the south of France. The cross-linking of epidemiological,spatial and demographical data allows us to propose a new diagnosis for the epidemic which reached Martigues in the autumn of 1705.

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16

Liu, Ming, and Yihong Xiao. "Optimal Scheduling of Logistical Support for Medical Resource with Demand Information Updating." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/765098.

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This paper presents a discrete time-space network model for a dynamic resource allocation problem following an epidemic outbreak in a region. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. A custom genetic algorithm is adopted to solve the proposed model, and a numerical example is presented for sensitivity analysis of the parameters. We compare the proposed medical resource allocation mode with two traditional operation modes in practice and find that our model is superior to any of them in less waste of resource and less logistic cost. The results may provide some practical guidelines for a decision-maker who is in charge of medical resource allocation in an epidemics control effort.

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17

Yi, Quanyong, Xu Liu, Shanshan Ding, Xinyue Yao, and Lisha Luo. "Evaluation of Citizen Epidemic Prevention Information Literacy in the Post-Epidemic Era in Mainland China." International Journal of Environmental Research and Public Health 20, no. 6 (March 22, 2023): 5226. http://dx.doi.org/10.3390/ijerph20065226.

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Improving citizen epidemic prevention information literacy is one of the most cost-efficient and important measures to improve people’s epidemic prevention abilities to effectively deal with future public health crises. Epidemic prevention information literacy is beneficial to improve individuals’ ability to deal with public health crises in the future. By summarizing related domestic and international research, and utilizing an empirical methodology, we constructed an epidemic prevention information literacy assessment model with good reliability, validity, and model fit. The model is composed of four indicators: (1) “epidemic prevention information awareness”; (2) “epidemic prevention information knowledge”; (3) “epidemic prevention information ability”; (4) “epidemic prevention information morality”. We used the model to assess the epidemic prevention information literacy of Chinese citizens. The results showed the following: (1) the overall level of the epidemic prevention information literacy of Chinese citizens was comparatively high, however, its development was unbalanced, and the capability and moral levels of the epidemic prevention information were comparatively low; (2) the four dimensions of the epidemic prevention information literacy were different in terms of the citizens’ education levels and locations. We analyzed the probable causes of these problems, and we propose specific corresponding countermeasures. The research provides a set of methods and norms for the evaluation of citizen epidemic prevention information literacy in the post-epidemic era.

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18

Zhang, Yixiao, Xing Lu, Ni Cui, Jingtai Tang, and Xiyun Zhang. "Coevolving Dynamics between Epidemic and Information Spreading considering the Dependence between Vigilance and Awareness Prevalence." Complexity 2021 (May 21, 2021): 1–13. http://dx.doi.org/10.1155/2021/5515549.

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It has been demonstrated that the propagation of information and awareness regarding a disease can assist in containing the outbreak of epidemics. Previous models for this coevolving usually introduced the dependence between these two processes by setting a lower but time-independent infection rate for individuals with awareness. However, a realistic scenario can be more complicated, as individual vigilance and the adopted protective measures may depend on the extent of the discussion on the disease, whereas individuals may be irrational or lack relevant knowledge, leading to improper measures being taken. These can introduce a time-varying dependence between epidemic dynamics and awareness prevalence and may weaken the effect of spreading awareness in containing a pandemic. To better understand this effect, we introduce a nonlinear dependence of the epidemic infection rate on awareness prevalence, focusing on the effect of different forms of dependence on the coevolving dynamics. We demonstrate that a positive correlation between vigilance and awareness prevalence can enhance the effect of information spreading in suppressing epidemics. However, this enhancement can be weakened if some individuals are irrational. Our results demonstrate the importance of rational behavior in the strategy of containing epidemics by propagation of disease information.

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19

Stylidou, Andreana, Alexandros Zervopoulos, Aikaterini Georgia Alvanou, George Koufoudakis, Georgios Tsoumanis, and Konstantinos Oikonomou. "Evaluation of Epidemic-Based Information Dissemination in a Wireless Network Testbed." Technologies 8, no. 3 (June 28, 2020): 36. http://dx.doi.org/10.3390/technologies8030036.

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Information dissemination is an integral part of modern networking environments, such as Wireless Sensor Networks (WSNs). Probabilistic flooding, a common epidemic-based approach, is used as an efficient alternative to traditional blind flooding as it minimizes redundant transmissions and energy consumption. It shares some similarities with the Susceptible-Infected-Recovered (SIR) epidemic model, in the sense that the dissemination process and the epidemic thresholds, which achieve maximum coverage with the minimum required transmissions, have been found to be common in certain cases. In this paper, some of these similarities between probabilistic flooding and the SIR epidemic model are identified, particularly with respect to the epidemic thresholds. Both of these epidemic algorithms are experimentally evaluated on a university campus testbed, where a low-cost WSN, consisting of 25 nodes, is deployed. Both algorithm implementations are shown to be efficient at covering a large portion of the network’s nodes, with probabilistic flooding behaving largely in accordance with the considered epidemic thresholds. On the other hand, the implementation of the SIR epidemic model behaves quite unexpectedly, as the epidemic thresholds underestimate sufficient network coverage, a fact that can be attributed to implementation limitations.

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20

Sun, Chiyue, Zihan Wang, Xuanyuan Wei, and Yuxuan Zhuo. "The Factors and Influence of Social Media Epidemic Information on Public." Lecture Notes in Education Psychology and Public Media 3, no. 1 (March 1, 2023): 67–72. http://dx.doi.org/10.54254/2753-7048/3/2022460.

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During the COVID-19 epidemic, social networks were one of the channels for the public to learn information about the epidemic, and the information has had many effects on the public's cognition and behavior. The information related to the epidemic on social media was mainly composed of two official and private parts, among which the private information was published by we-media and netizens. And previous studies have studied the causes of rumors during the COVID-19 pandemic. This paper summarized the factors and influences of epidemic-related information on social media during the COVID-19 epidemic on their cognition and behavior. Based on studying the impact of epidemic related information on social media on the public, the public would be guided to rationally view epidemic related information, do not believe in rumors, and understand authoritative information through correct channels.

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21

Zou, Rongcheng, Xiaofang Duan, Zhen Han, Yikang Lu, and Kewei Ma. "What information sources can prevent the epidemic: Local information or kin information?" Chaos, Solitons & Fractals 168 (March 2023): 113104. http://dx.doi.org/10.1016/j.chaos.2023.113104.

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22

Kopić, Jasminka, and Maja Tomić Paradžik. "Expanding the Use of Noninvasive Ventilation During an Epidemic." Disaster Medicine and Public Health Preparedness 8, no. 4 (August 2014): 310–14. http://dx.doi.org/10.1017/dmp.2014.71.

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ABSTRACTNoninvasive ventilation (NIV) is a proved and effective therapeutic option for some patients with respiratory failure. During an epidemic, NIV can free up respirators and other intensive care unit equipment for patients with respiratory insufficiency whose survival depends exclusively on invasive ventilation. Some guidelines have indicated that NIV is potentially hazardous and should not be recommended for use during epidemics, given the perceived potential risk of transmission from aerosolized pathogen dispersion to other patients or medical staff. Conversely, some reports of previous epidemics describe NIV as a very efficient and safe modality of respiratory support, if strict infection control measures are implemented.We discuss NIV use during epidemics and indicate the need for prospective randomized clinical studies on the efficacy of NIV in epidemic conditions to provide important information to the current body of literature. Meanwhile, the use of NIV under strict infection control guidelines should be incorporated into epidemic preparedness planning. (Disaster Med Public Health Preparedness. 2014;8:1-5)

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23

He, Ping, Yu Gao, Longfei Guo, Tongtong Huo, Yuxin Li, Xingren Zhang, Yunfeng Li, Cheng Peng, and Fanyun Meng. "Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model." Sustainability 13, no. 21 (October 22, 2021): 11667. http://dx.doi.org/10.3390/su132111667.

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Since 2019, the novel coronavirus has spread rapidly worldwide, greatly affecting social stability and human health. Pandemic prevention has become China’s primary task in responding to the transmission of COVID-19. Risk mapping and the proposal and implementation of epidemic prevention measures emphasize many research efforts. In this study, we collected location information for confirmed COVID-19 cases in Beijing, Shenyang, Dalian, and Shijiazhuang from 5 October 2020 to 5 January 2021, and selected 15 environmental variables to construct a model that comprehensively considered the parameters affecting the outbreak and spread of COVID-19 epidemics. Annual average temperature, catering, medical facilities, and other variables were processed using ArcGIS 10.3 and classified into three groups, including natural environmental variables, positive socio-environmental variables, and benign socio-environmental variables. We modeled the epidemic risk distribution for each area using the MaxEnt model based on the case occurrence data and environmental variables in four regions, and evaluated the key environmental variables influencing the epidemic distribution. The results showed that medium-risk zones were mainly distributed in Changping and Shunyi in Beijing, while Huanggu District in Shenyang and the southern part of Jinzhou District and the eastern part of Ganjingzi District in Dalian also represented areas at moderate risk of epidemics. For Shijiazhuang, Xinle, Gaocheng and other places were key COVID-19 epidemic spread areas. The jackknife assessment results revealed that positive socio-environmental variables are the most important factors affecting the outbreak and spread of COVID-19. The average contribution rate of the seafood market was 21.12%, and this contribution reached as high as 61.3% in Shenyang. The comprehensive analysis showed that improved seafood market management, strengthened crowd control and information recording, industry-catered specifications, and well-trained employees have become urgently needed prevention strategies in different regions. The comprehensive analysis indicated that the niche model could be used to classify the epidemic risk and propose prevention and control strategies when combined with the assessment results of the jackknife test, thus providing a theoretical basis and information support for suppressing the spread of COVID-19 epidemics.

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24

Parag, Kris V., Christl A. Donnelly, and Alexander E. Zarebski. "Quantifying the information in noisy epidemic curves." Nature Computational Science 2, no. 9 (September 26, 2022): 584–94. http://dx.doi.org/10.1038/s43588-022-00313-1.

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25

Vojnović, Milan, Varun Gupta, Thomas Karagiannis, and Christos Gkantsidis. "Sampling Strategies for Epidemic-Style Information Dissemination." IEEE/ACM Transactions on Networking 18, no. 4 (August 2010): 1013–25. http://dx.doi.org/10.1109/tnet.2010.2051233.

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26

NAKANO, Keisuke. "Epidemic Communication, Information Floating and Safety/Security." IEICE ESS Fundamentals Review 10, no. 4 (2017): 282–92. http://dx.doi.org/10.1587/essfr.10.4_282.

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27

Zhan, Xiu-Xiu, Chuang Liu, Gui-Quan Sun, and Zi-Ke Zhang. "Epidemic dynamics on information-driven adaptive networks." Chaos, Solitons & Fractals 108 (March 2018): 196–204. http://dx.doi.org/10.1016/j.chaos.2018.02.010.

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28

Li, Jinhai, Yunlei Ma, Xinglong Xu, Jiaming Pei, and Youshi He. "A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19." International Journal of Environmental Research and Public Health 19, no. 16 (August 9, 2022): 9819. http://dx.doi.org/10.3390/ijerph19169819.

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The outbreak of the coronavirus disease 2019 (COVID-19) represents an alert for epidemic prevention and control in public health. Offline anti-epidemic work is the main battlefield of epidemic prevention and control. However, online epidemic information prevention and control cannot be ignored. The aim of this study was to identify reliable information sources and false epidemic information, as well as early warnings of public opinion about epidemic information that may affect social stability and endanger the people’s lives and property. Based on the analysis of health and medical big data, epidemic information screening and public opinion prevention and control research were decomposed into two modules. Eight characteristics were extracted from the four levels of coarse granularity, fine granularity, emotional tendency, and publisher behavior, and another regulatory feature was added, to build a false epidemic information identification model. Five early warning indicators of public opinion were selected from the macro level and the micro level to construct the early warning model of public opinion about epidemic information. Finally, an empirical analysis on COVID-19 information was conducted using big data analysis technology.

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Xian, Jiajun, Zhihong Zhang, Zongyi Li, and Dan Yang. "Coupled Information–Epidemic Spreading Dynamics with Selective Mass Media." Entropy 25, no. 6 (June 12, 2023): 927. http://dx.doi.org/10.3390/e25060927.

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As a pandemic emerges, information on epidemic prevention disseminates among the populace, and the propagation of that information interacts with the proliferation of the disease. Mass media serve a pivotal function in facilitating the dissemination of epidemic-related information. Investigating coupled information–epidemic dynamics, while accounting for the promotional effect of mass media in information dissemination, is of significant practical relevance. Nonetheless, in the extant research, scholars predominantly employ an assumption that mass media broadcast to all individuals equally within the network: this assumption overlooks the practical constraint imposed by the substantial social resources required to accomplish such comprehensive promotion. In response, this study introduces a coupled information–epidemic spreading model with mass media that can selectively target and disseminate information to a specific proportion of high-degree nodes. We employed a microscopic Markov chain methodology to scrutinize our model, and we examined the influence of the various model parameters on the dynamic process. The findings of this study reveal that mass media broadcasts directed towards high-degree nodes within the information spreading layer can substantially reduce the infection density of the epidemic, and raise the spreading threshold of the epidemic. Additionally, as the mass media broadcast proportion increases, the suppression effect on the disease becomes stronger. Moreover, with a constant broadcast proportion, the suppression effect of mass media promotion on epidemic spreading within the model is more pronounced in a multiplex network with a negative interlayer degree correlation, compared to scenarios with positive or absent interlayer degree correlation.

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Дмитрий Николаевич, Савищенко,, Шварцкопф, Евгения Андреевна, and Юрасов, Владислав Георгиевич. "AUTOMATED INFORMATION SYSTEM FOR DISCRETE MODELING OF NETWORK EPIDEMIC PROCESSES. PART 2." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 3(-) (October 24, 2022): 397–402. http://dx.doi.org/10.36622/vstu.2022.25.3.008.

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Стремительное развитие информационных технологий и внедрение их в различные сферы деятельности обуславливает их применение при построении современных корпоративных сетей, что значительно расширяет их функционал и повышает эффективность. Вместе с тем, постоянно возрастает количество атак с применением вредоносного программного обеспечения (ВПО) различного типа, способных наносить значительный ущерб. Особую угрозу для корпоративных сетей представляют вирусы, способные порождать масштабные сетевые эпидемии, деструктивное воздействие которых за последние десятилетия нанесло значительный финансовый ущерб как организациям, так и частным лицам. В представленном исследовании описан процесс проведения специализированной алгоритмизации моделирования сетевых эпидемических процессов. Разработанные алгоритмы описывают основные функции, выполняемые при моделировании эпидемий, ключевыми особенностями которых стали: возможность загрузки топологии пользователем; поддержка нескольких моделей моделирования эпидемических процессов, в том числе многоэтапных. The rapid development of information technologies and their introduction into various fields of activity determines their use in the construction of modern corporate networks, which significantly expands their functionality and increases efficiency. At the same time, the number of attacks using various types of malware that can cause significant damage is constantly increasing. A particular threat to corporate networks is viruses that can generate large-scale network epidemics, the destructive impact of which over the past decades has caused significant financial damage to both organizations and individuals. The present study describes the process of conducting a specialized algorithmization of modeling network epidemic processes. The developed algorithms describe the main functions performed in modeling epidemics, the key features of which are: the ability to load the topology by the user; support for several models of modeling epidemic processes, including multi-stage ones.

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Schnyder, Simon K., John J. Molina, Ryoichi Yamamoto, and Matthew S. Turner. "Rational social distancing in epidemics with uncertain vaccination timing." PLOS ONE 18, no. 7 (July 21, 2023): e0288963. http://dx.doi.org/10.1371/journal.pone.0288963.

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During epidemics people may reduce their social and economic activity to lower their risk of infection. Such social distancing strategies will depend on information about the course of the epidemic but also on when they expect the epidemic to end, for instance due to vaccination. Typically it is difficult to make optimal decisions, because the available information is incomplete and uncertain. Here, we show how optimal decision-making depends on information about vaccination timing in a differential game in which individual decision-making gives rise to Nash equilibria, and the arrival of the vaccine is described by a probability distribution. We predict stronger social distancing the earlier the vaccination is expected and also the more sharply peaked its probability distribution. In particular, equilibrium social distancing only meaningfully deviates from the no-vaccination equilibrium course if the vaccine is expected to arrive before the epidemic would have run its course. We demonstrate how the probability distribution of the vaccination time acts as a generalised form of discounting, with the special case of an exponential vaccination time distribution directly corresponding to regular exponential discounting.

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Childs, Marissa L., Morgan P. Kain, Mallory J. Harris, Devin Kirk, Lisa Couper, Nicole Nova, Isabel Delwel, Jacob Ritchie, Alexander D. Becker, and Erin A. Mordecai. "The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models." Proceedings of the Royal Society B: Biological Sciences 288, no. 1957 (August 25, 2021): 20210811. http://dx.doi.org/10.1098/rspb.2021.0811.

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Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March–June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ( R 0 ) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ( R E ) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.

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Sarasa Cabezuelo, Antonio. "Generation of infectious disease alerts through the use of geolocation." Bulletin of Electrical Engineering and Informatics 9, no. 4 (August 1, 2020): 1533–41. http://dx.doi.org/10.11591/eei.v9i4.1945.

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In recent years, there have been several cases of global epidemics such as influenza B or Ebola. In these cases, several factors are key to limit the effects of the epidemic and avoid contagion. Between of them is the speed of knowing which persons are infected, which persons has been in contact with any infected person or know what the focus of the epidemic. In general, obtaining this information requires a process of research among the first affected that can be slow and complicated. This article describes a tool that aims to generate alerts when there are data about an epidemic, and notify all persons who could be exposed to contagion and prevent new infections occurs.

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Wu, Peng, and Di Zhao. "Research on the Identification of Key Nodes in the Process of WeChat Epidemic Information Dissemination: A Supernetwork Perspective." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–10. http://dx.doi.org/10.1155/2020/6751686.

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It is of great significance to comprehensively and reasonably identify the key nodes in the WeChat epidemic information dissemination system. First, the recognition results can be used to guide the spread of epidemic-related information in WeChat, such as accelerating the spread of valuable information or monitoring the spread of rumors. Secondly, the analysis of key nodes helps us understand the evolution of the epidemic information dissemination network in WeChat, and the analysis of key nodes also helps us understand the modes and methods of epidemic-related information dissemination in WeChat. Finally, the results of these studies may be generalized to other fields of social life. This paper analyzes the composition of and relationship between epidemic-related information dissemination systems in WeChat and proposes a Supernetwork model for WeChat epidemic information dissemination on this basis. In this study, a comprehensive identification method of key nodes of the WeChat epidemic information dissemination system under the Supernetwork vision was constructed, and the method is analyzed and verified through examples in this paper.

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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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Chowdhury, Sohini Roy, Caterina Scoglio, and William H. Hsu. "Mitigation Strategies for Foot and Mouth Disease." International Journal of Artificial Life Research 2, no. 2 (April 2011): 42–76. http://dx.doi.org/10.4018/jalr.2011040103.

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Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected countries. In the absence of precise and accurate spatial information regarding disease dynamics, learning- based predictive models can be used to mimic latent spatial parameters so as to predict the spread of epidemics in time. This paper analyzes temporal predictions from four such learning-based models, namely: neural network, autoregressive, Bayesian network, and Monte-Carlo simulation models. The prediction qualities of these models have been validated using FMD incidence reports in Turkey. Additionally, the authors perform simulations of mitigation strategies based on the predictive models to curb the impact of the epidemic. This paper also analyzes the cost-effectiveness of these mitigation strategies to conclude that vaccinations and movement ban strategies are more cost-effective than premise culls before the onset of an epidemic outbreak; however, in the event of existing epidemic outbreaks, premise culling is more effective at controlling FMD.

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Ху, Зетао, Наталія Стоянець, and Лінчен Ніу. "RURAL DEVELOPMENT AND METHODS FOR IMPROVING THEIR CAPACITY EVIDENCE FROM DISTRICT G OF THE COVID-19 EPIDEMIC PREVENTION." Молодий вчений, no. 2 (90) (February 26, 2021): 247–52. http://dx.doi.org/10.32839/2304-5809/2021-2-90-50.

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The purpose of this article is tantamount to summarize the successful experience of rural epidemic prevention and provide a reference for other rural areas to enhance epidemic prevention and control capabilities. The second is to offer a reference for villages to enhance their ability to respond to future public health emergencies. Methodology. This article focus on the literature of rural epidemic prevention and control in China and the practice of epidemic prevention and control in G counties, collects relevant information and compares and analyzes it. Results of the survey showed that prevention of the epidemic is not only the prevention and control of the epidemic itself, but also is required to meet the production and living needs of the villagers. From a national level, the country’s epidemic prevention capabilities are undoubtedly strong and successful, but it cannot reflect the actual situation of epidemic prevention in rural areas; from a rural micro perspective, the village itself undoubtedly lacks sufficient capacity to deal with the epidemic. It cannot explain why the prevention of the rural epidemic has been wealthy. Practice of County G shows that the county has the ability to spontaneously prevent epidemics, and the county-based epidemic prevention and control system is the basis for the rural epidemic prevention capabilities. Practical implications. County G successfully responded to the impact of the two waves of the epidemic under the current prevention and control system. The system successfully controlled the epidemic to a small area and achieved the goals of epidemic prevention and control, social stability and economic development. Value/originality. Taking county area as the research unit is undoubtedly a suitable choice, which is the novelty of this article.

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Lu, Quan, Ting Liu, Chang Li, Jing Chen, Yongchun Zhu, Shengyi You, and Siwei Yu. "Investigation into Information Release of Chinese Government and Departments on COVID-19." Data and Information Management 4, no. 3 (July 22, 2020): 209–35. http://dx.doi.org/10.2478/dim-2020-0014.

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AbstractInformation release is an important way for governments to deal with public health emergencies, and plays an irreplaceable role in promoting epidemic prevention and control, enhancing public awareness of the epidemic situation and mobilizing social resources. Focusing on the coronavirus disease 2019 (COVID-19) epidemic in China, this investigation chose 133 information release accounts of the Chinese government and relevant departments at the national, provincial, and municipal levels, including Ministries of the State Council, Departments of Hubei Province Government, and Bureaus of Wuhan Government, covering their portals, apps, Weibos, and WeChats. Then, the characteristics such as scale, agility, frequency, originality, and impact of different levels, departments, and channels of the information releases by the Chinese government on the COVID-19 epidemic were analyzed. Finally, the overall situation was concluded by radar map analysis. It was found that the information release on the COVID-19 epidemic was coordinated effectively at different levels, departments, and channels, as evidenced by the complementarity between channels, the synergy between the national and local governments, and the coordination between departments, which guaranteed the rapid success of the epidemic prevention and control process in China. This investigation could be a reference for epidemic prevention and control for governments and international organizations, such as the World Health Organization (WHO), during public health emergencies, e.g. the COVID-19 pandemic.

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Sun, Xiaoyu. "The Effect of New Media News Push on College Students' Anxiety." Lecture Notes in Education Psychology and Public Media 3, no. 1 (March 1, 2023): 1044–51. http://dx.doi.org/10.54254/2753-7048/3/2022592.

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With the COVID-19 pandemic sweeping the world, new media platforms have played an important role in news feeds related to the epidemic with their quick and concise features. Such major public health emergencies not only cause great losses to the national economy, but also affect the public psychology. Based on the theory of expanded parallel process model (EPPM), through the questionnaire survey method to collect the relevant data, and the use of SPSS software for data statistics and analysis, build new crown epidemic situations of new media news push on college students' emotional impact model, and using correlation and regression analysis, explore the new media influence on college students' anxiety of epidemic information push. The results show that during the epidemic period, the surveyed college students have a strong initiative to contact and spread the epidemic information through social media, accounting for a large proportion of them. Browsing the epidemic information has become their daily routine. During the epidemic period, epidemic-related information showed the characteristics of all-platform dissemination, and college students generally expressed trust in epidemic-related information spread on social media. In terms of the mental health level of college students during the epidemic period, those who showed anxiety, depression and stress have a large proportion in the surveyed college students.

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40

Lin, Shanlang, Chao Ma, and Ruofei Lin. "Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19)." International Journal of Environmental Research and Public Health 19, no. 11 (June 2, 2022): 6801. http://dx.doi.org/10.3390/ijerph19116801.

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With the rapid development of the Mobile Internet in China, epidemic information is real-time and holographic, and the role of information diffusion in epidemic control is increasingly prominent. At the same time, the publicity of all kinds of big data also provides the possibility to explore the impact of media information diffusion on disease transmission. We explored the mechanism of the influence of information diffusion on the transmission of COVID-19, developed a model of the interaction between information diffusion and disease transmission based on the Susceptible–Infected–Recovered (SIR) model, and conducted an empirical test by using econometric methods. The benchmark result showed that there was a significant negative correlation between the information diffusion and the transmission of COVID-19. The result of robust test showed that the diffusion of both epidemic information and protection information hindered the further transmission of the epidemic. Heterogeneity test results showed that the effect of epidemic information on the suppression of COVID-19 is more significant in cities with weak epidemic control capabilities and higher Internet development levels.

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Miyamoto, Kuniaki. "An Analysis of the COVID-19 Epidemic in Japan Using a Logistic Model." Journal of Disaster Research 16, no. 1 (January 30, 2021): 12–15. http://dx.doi.org/10.20965/jdr.2021.p0012.

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The COVID-19 pandemic has been persistent. For example, the number of infections increased exponentially since mid-September in Europe. The SIR, among other models, is used to examine and detail such epidemics and the changes they bring about. However, the application of the models requires that we fix the parameters to govern the processes; it is difficult to set them up appropriately, especially during new epidemic cases such as COVID-19. If we can limit the purpose of analysis to understand current epidemic situations, then it would be better to use simple models and limit the number of parameters. The logistic model is one of such suitable models, which can reflect the basic characteristics of an epidemic to provide information on the state and tendency of the epidemic based on little information. This research uses daily cases, deaths, and recoveries to analyze the epidemic and derives interesting results. The first wave of the epidemic, which ran from March to May, almost complies with the logistic model. In the case of the second wave, since mid-June, the results show that the rising phase has characteristics similar to those of the first wave. However, the phase of decline has different characteristics. Currently, in mid-October, it is almost in a state of equilibrium. This result means that the data used in this analysis show some characteristics of the statistical population of the “epidemic field.” However, while we consider the fact that infected persons must be isolated and hence removed from the “field,” it is suggested that the number of infected and recovered persons must be significantly larger than that of the reported cases. Nevertheless, it is difficult to evaluate the statistical characteristics of the “epidemic field” using the data, as they are not the results of “random sampling.”

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Abu El Sood, Hanaa, Shimaa Ali Abu Kamer, Reham Kamel, Hesham Magdy, Fatma S. Osman, Manal Fahim, Amira Mohsen, et al. "The Impact of Implementing the Egypt Pandemic Preparedness Plan for Acute Respiratory Infections in Combating the Early Stage of the COVID-19 Pandemic, February-July 2020: Viewpoint." JMIR Public Health and Surveillance 7, no. 5 (May 7, 2021): e27412. http://dx.doi.org/10.2196/27412.

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This article briefly describes Egypt’s acute respiratory infection (ARI) epidemic preparedness and containment plan and illustrates the impact of implementation of the plan on combating the early stage of the COVID-19 epidemic in Egypt. Pillars of the plan include crisis management, enhancing surveillance systems and contact tracing, case and hospital management, raising community awareness, and quarantine and entry points. To identify the impact of the implementation of the plan on epidemic mitigation, a literature review was performed of studies published from Egypt in the early stage of the pandemic. In addition, data for patients with COVID-19 from February to July 2020 were obtained from the National Egyptian Surveillance system and studied to describe the situation in the early stage of the epidemic in Egypt. The lessons learned indicated that the single most important key to success in early-stage epidemic containment is the commitment of all partners to a predeveloped and agreed-upon preparedness plan. This information could be useful for other countries in the region and worldwide in mitigating future anticipated ARI epidemics and pandemics. Postepidemic evaluation is needed to better assess Egypt’s national response to the COVID-19 epidemic.

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Li, Xin, Xingyuan He, Lu Zhou, and Shushu Xie. "Impact of Epidemics on Enterprise Innovation: An Analysis of COVID-19 and SARS." Sustainability 14, no. 9 (April 26, 2022): 5223. http://dx.doi.org/10.3390/su14095223.

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This study analyzes the impact of SARS and COVID-19, the two most severe epidemics to occur in China since the 21st century, on corporate innovation, in order to find a path for sustained innovation growth under the epidemic. For COVID-19, the analysis used data from China’s A-share-listed companies from 2019 to 2020; a longer period (1999–2006) and a wider sample of Chinese industrial enterprises were used for the SARS epidemic. The empirical model was constructed using the difference-in-differences method. Both COVID-19 and SARS were found to have significantly reduced enterprise innovation. However, the effect of SARS disappeared after two years. For COVID-19, information asymmetry, financing constraints, and economic policy uncertainty moderated the epidemic’s effect on innovation. The results show that financing constraints and economic policy uncertainty reduce the epidemic’s negative impact. However, while most previous studies have found that an epidemic reduces the information asymmetry between investors and enterprises in the short term, thus raising enterprise innovation, we found that information asymmetry aggravated the epidemic’s negative impact. These findings can be applied to alleviate the current epidemic’s negative impact as well as improve enterprise innovation thereafter.

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Pechlivanoglou, Tilemachos, Jing Li, Jialin Sun, Farzaneh Heidari, and Manos Papagelis. "Epidemic Spreading in Trajectory Networks." Big Data Research 27 (February 2022): 100275. http://dx.doi.org/10.1016/j.bdr.2021.100275.

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45

Ver Steeg, Greg, Rumi Ghosh, and Kristina Lerman. "What Stops Social Epidemics?" Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 377–84. http://dx.doi.org/10.1609/icwsm.v5i1.14107.

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Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. We have observed information cascades on the social media site Digg that spread fast enough for one initial spreader to infect hundreds of people, yet end up affecting only 0.1% of the entire network. We find that two effects, previously studied in isolation, combine cooperatively to drastically limit the final size of cascades on Digg. First, because of the highly clustered structure of the Digg network, most people who are aware of a story have been exposed to it via multiple friends. This structure lowers the epidemic threshold while moderately slowing the overall growth of cascades. In addition, we find that the mechanism for social contagion on Digg points to a fundamental difference between information spread and other contagion processes: despite multiple opportunities for infection within a social group, people are less likely to become spreaders of information with repeated exposure. The consequences of this mechanism become more pronounced for more clustered graphs. Ultimately, this effect severely curtails the size of social epidemics on Digg.

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46

Wang, Jun, Weijie Xiong, Ruijie Wang, Shimin Cai, Die Wu, Wei Wang, and Xiaolong Chen. "Effects of the information-driven awareness on epidemic spreading on multiplex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 7 (July 2022): 073123. http://dx.doi.org/10.1063/5.0092031.

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In this study, we examine the impact of information-driven awareness on the spread of an epidemic from the perspective of resource allocation by comprehensively considering a series of realistic scenarios. A coupled awareness-resource-epidemic model on top of multiplex networks is proposed, and a Microscopic Markov Chain Approach is adopted to study the complex interplay among the processes. Through theoretical analysis, the infection density of the epidemic is predicted precisely, and an approximate epidemic threshold is derived. Combining both numerical calculations and extensive Monte Carlo simulations, the following conclusions are obtained. First, during a pandemic, the more active the resource support between individuals, the more effectively the disease can be controlled; that is, there is a smaller infection density and a larger epidemic threshold. Second, the disease can be better suppressed when individuals with small degrees are preferentially protected. In addition, there is a critical parameter of contact preference at which the effectiveness of disease control is the worst. Third, the inter-layer degree correlation has a “double-edged sword” effect on spreading dynamics. In other words, when there is a relatively lower infection rate, the epidemic threshold can be raised by increasing the positive correlation. By contrast, the infection density can be reduced by increasing the negative correlation. Finally, the infection density decreases when raising the relative weight of the global information, which indicates that global information about the epidemic state is more efficient for disease control than local information.

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47

Raman Sharma, Mahendra kumar, Rajeev Chauhan, Anita Kumari, Arti Saini, and Kusum R. Rohilla. "Infodemic: The epidemic of information during COVID-19." National Journal of Community Medicine 13, no. 3 (March 31, 2022): 200–202. http://dx.doi.org/10.55489/njcm.133202237.

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India is one of the world’s worst affected countries due to COVID-19 pandemic. The world is struglling to fight agaisnt centuries pandmemic. Globally goverments have been imposed lockdown and restrictions to control situation and minimise spread of infection. Social media was found the most practical and efficiant mediam to share information and opnions about pandmemic. At time of social distancing, social media helped people to share their feelings and find support. Same time overuse of social media palteform created panic and misinformation across countries. People sharing unconfirmed information about covid pandmemic and goverments were found it difficult to handle

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48

Thomas, Sandra P. "EDITORIAL: INFORMATION FATIGUE SYNDROME - IS THERE AN EPIDEMIC?" Issues in Mental Health Nursing 19, no. 6 (January 1998): 523–24. http://dx.doi.org/10.1080/016128498248818.

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Kiss, Istvan Z., Jackie Cassell, Mario Recker, and Péter L. Simon. "The impact of information transmission on epidemic outbreaks." Mathematical Biosciences 225, no. 1 (May 2010): 1–10. http://dx.doi.org/10.1016/j.mbs.2009.11.009.

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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 (January 2011): 87–104. http://dx.doi.org/10.1016/j.jpdc.2010.08.010.

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