1
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Zhang S, Zhao D, Xia C, Tanimoto J. Impact of simplicial complexes on epidemic spreading in partially mapping activity-driven multiplex networks. CHAOS (WOODBURY, N.Y.) 2023; 33:2895981. [PMID: 37307162 DOI: 10.1063/5.0151881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 05/22/2023] [Indexed: 06/14/2023]
Abstract
Over the past decade, the coupled spread of information and epidemic on multiplex networks has become an active and interesting topic. Recently, it has been shown that stationary and pairwise interactions have limitations in describing inter-individual interactions , and thus, the introduction of higher-order representation is significant. To this end, we present a new two-layer activity-driven network epidemic model, which considers the partial mapping relationship among nodes across two layers and simultaneously introduces simplicial complexes into one layer, to investigate the effect of 2-simplex and inter-layer mapping rate on epidemic transmission. In this model, the top network, called the virtual information layer, characterizes information dissemination in online social networks, where information can be diffused through simplicial complexes and/or pairwise interactions. The bottom network, named as the physical contact layer, denotes the spread of infectious diseases in real-world social networks. It is noteworthy that the correspondence among nodes between two networks is not one-to-one but partial mapping. Then, a theoretical analysis using the microscopic Markov chain (MMC) method is performed to obtain the outbreak threshold of epidemics, and extensive Monte Carlo (MC) simulations are also carried out to validate the theoretical predictions. It is obviously shown that MMC method can be used to estimate the epidemic threshold; meanwhile, the inclusion of simplicial complexes in the virtual layer or introductory partial mapping relationship between layers can inhibit the spread of epidemics. Current results are conducive to understanding the coupling behaviors between epidemics and disease-related information.
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Affiliation(s)
- Shuofan Zhang
- Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China
| | - Dawei Zhao
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Chengyi Xia
- School of Artificial Intelligence, Tiangong University, Tianjin 300387, China
| | - Jun Tanimoto
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
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2
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Azizi A, Kazanci C, Komarova NL, Wodarz D. Effect of Human Behavior on the Evolution of Viral Strains During an Epidemic. Bull Math Biol 2022; 84:144. [PMID: 36334172 PMCID: PMC9638455 DOI: 10.1007/s11538-022-01102-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 10/17/2022] [Indexed: 11/08/2022]
Abstract
It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less “visible,” or less “symptomatic,” in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.
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Affiliation(s)
- Asma Azizi
- Department of Mathematics, Kennesaw State University, Marietta, GA, 30060, USA.
| | - Caner Kazanci
- Department of Mathematics, University of Georgia, Athens, GA, 30602, USA.,College of Engineering, University of Georgia, Athens, GA, 30602, USA
| | - Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine, CA, 92697, USA
| | - Dominik Wodarz
- Department of Population Health and Disease Prevention Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA, 92697, USA
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3
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Teslya A, Nunner H, Buskens V, Kretzschmar ME. The effect of competition between health opinions on epidemic dynamics. PNAS NEXUS 2022; 1:pgac260. [PMID: 36712334 PMCID: PMC9802282 DOI: 10.1093/pnasnexus/pgac260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022]
Abstract
Past major epidemic events showed that when an infectious disease is perceived to cause severe health outcomes, individuals modify health behavior affecting epidemic dynamics. To investigate the effect of this feedback relationship on epidemic dynamics, we developed a compartmental model that couples a disease spread framework with competition of two mutually exclusive health opinions (health-positive and health-neutral) associated with different health behaviors. The model is based on the assumption that individuals switch health opinions as a result of exposure to opinions of others through interpersonal communications. To model opinion switch rates, we considered a family of functions and identified the ones that allow health opinions to coexist. Finally, the model includes assortative mixing by opinions. In the disease-free population, either the opinions cannot coexist and one of them is always dominating (mono-opinion equilibrium) or there is at least one stable coexistence of opinions equilibrium. In the latter case, there is multistability between the coexistence equilibrium and the two mono-opinion equilibria. When two opinions coexist, it depends on their distribution whether the infection can invade. If presence of the infection leads to increased switching to a health-positive opinion, the epidemic burden becomes smaller than indicated by the basic reproduction number. Additionally, a feedback between epidemic dynamics and health opinion dynamics may result in (sustained) oscillatory dynamics and a switch to a different stable opinion distribution. Our model captures feedback between spread of awareness through social interactions and infection dynamics and can serve as a basis for more elaborate individual-based models.
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Affiliation(s)
- Alexandra Teslya
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX Utrecht, The Netherlands
| | - Hendrik Nunner
- Department of Sociology/ICS, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands
- Centre for Complex System Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
| | - Vincent Buskens
- Department of Sociology/ICS, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands
- Centre for Complex System Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
| | - Mirjam E Kretzschmar
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX Utrecht, The Netherlands
- Centre for Complex System Studies (CCSS), Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands
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4
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Co-evolution dynamics of epidemic and information under dynamical multi-source information and behavioral responses. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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5
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Guo Y, Tu L, Shen H, Chai L. Transmission dynamics of disease spreading in multilayer networks with mass media. Phys Rev E 2022; 106:034307. [PMID: 36266902 DOI: 10.1103/physreve.106.034307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
On the basis of existing disease spreading research, in this paper we propose a Hesitant-Taken-Unaware-Aware-Susceptible-Asymptomatic-Symptomatic-Recovered (HTUA-SI^{a}I^{s}R) model with mass media in a two-layer network, which consists of a virtual communication layer and a physical contact layer. Based on the UAU-SIR model, we additionally consider three practical factors, including whether individuals will disseminate information or not, the influence of unaware individuals on aware individuals, and the direct recovery of asymptomatic infected individuals. Based on the microscopic Markov chain approach (MMCA), for the proposed HTUA-SI^{a}I^{s}R model, MMCA equations are generated and the analytical expression of the epidemic threshold is obtained. Compared with Monte Carlo techniques, numerical simulations show the feasibility and effectiveness of the MMCA equations, as well as the HTUA-SI^{a}I^{s}R model theoretically. Meanwhile, extensive simulations demonstrate that the acceleration of the awareness dissemination in the virtual communication layer can effectively block the epidemic spreading and raise the epidemic threshold. However, under certain conditions, the increasing of T-state individuals will increase the U-state individuals because the T-state and U-state individuals can influence the A-state individuals losing their awareness of protection, and then promote the epidemic spreading and decrease the epidemic threshold. In addition, reducing asymptomatic infections can hinder the epidemic spreading. But, when the recovery rate of asymptomatic infections is greater than that of symptomatic infections, decreasing the tendency of individuals acquiring asymptomatic infections will lower the epidemic threshold.
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Affiliation(s)
- Yifei Guo
- Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China and College of Science, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China
| | - Lilan Tu
- Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China and College of Science, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China
| | - Han Shen
- Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China and College of Science, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China
| | - Lang Chai
- Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China and College of Science, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China
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6
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Chen J, Liu Y, Yue J, Duan X, Tang M. Coevolving spreading dynamics of negative information and epidemic on multiplex networks. NONLINEAR DYNAMICS 2022; 110:3881-3891. [PMID: 36035014 PMCID: PMC9395805 DOI: 10.1007/s11071-022-07776-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
The widespread dissemination of negative information on vaccine may arise people's concern on the safety of vaccine and increase their hesitancy in vaccination, which can seriously impede the progress of epidemic control. Existing works on information-epidemic coupled dynamics focus on the suppression effects of information on epidemic. Here we propose a negative information and epidemic coupled propagation model on two-layer multiplex networks to study the effects of negative information of vaccination on epidemic spreading, where the negative information propagates on the virtual communication layer and the disease spreads on the physical contact layer. In our model, an individual getting an adverse event after vaccination will spread negative information and an individual affected by the negative information will reduce his/her willingness to get vaccinated and spread the negative information. By using the microscopic Markov chain method, we analytically predict the epidemic threshold and final infection density, which agree well with simulation results. We find that the spread of negative information leads to a lower epidemic outbreak threshold and a higher final infection density. However, the individuals' vaccination activities, but not the negative information spreading, has a leading impact on epidemic spreading. Only when the individuals obviously reduce their vaccination willingness due to negative information, the negative information can impact the epidemic spreading significantly.
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Affiliation(s)
- Jiaxing Chen
- School of Computer Science, Southwest Petroleum University, Chengdu, 610500 China
- Tianjin Key Lab of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384 China
| | - Ying Liu
- School of Computer Science, Southwest Petroleum University, Chengdu, 610500 China
| | - Jing Yue
- School of Computer Science, Southwest Petroleum University, Chengdu, 610500 China
| | - Xi Duan
- School of Science, Southwest Petroleum University, Chengdu, 610500 China
| | - Ming Tang
- School of Physics and Electronic Science, East China Normal University, Shanghai, 200241 China
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241 China
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7
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Wang H, Zhang HF, Zhu PC, Ma C. Interplay of simplicial awareness contagion and epidemic spreading on time-varying multiplex networks. CHAOS (WOODBURY, N.Y.) 2022; 32:083110. [PMID: 36049933 DOI: 10.1063/5.0099183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
There has been growing interest in exploring the dynamical interplay of epidemic spreading and awareness diffusion within the multiplex network framework. Recent studies have demonstrated that pairwise interactions are not enough to characterize social contagion processes, but the complex mechanisms of influence and reinforcement should be considered. Meanwhile, the physical social interaction of individuals is not static but time-varying. Therefore, we propose a novel sUAU-tSIS model to characterize the interplay of simplicial awareness contagion and epidemic spreading on time-varying multiplex networks, in which one layer with 2-simplicial complexes is considered the virtual information layer to address the complex contagion mechanisms in awareness diffusion and the other layer with time-varying and memory effects is treated as the physical contact layer to mimic the temporal interaction pattern among population. The microscopic Markov chain approach based theoretical analysis is developed, and the epidemic threshold is also derived. The experimental results show that our theoretical method is in good agreement with the Monte Carlo simulations. Specifically, we find that the synergistic reinforcement mechanism coming from the group interactions promotes the diffusion of awareness, leading to the suppression of the spreading of epidemics. Furthermore, our results illustrate that the contact capacity of individuals, activity heterogeneity, and memory strength also play important roles in the two dynamics; interestingly, a crossover phenomenon can be observed when investigating the effects of activity heterogeneity and memory strength.
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Affiliation(s)
- Huan Wang
- The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Mathematical Science, Anhui University, Hefei 230601, China
| | - Hai-Feng Zhang
- The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Mathematical Science, Anhui University, Hefei 230601, China
| | - Pei-Can Zhu
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NWPU), Xi'an 710072, Shaanxi, China
| | - Chuang Ma
- School of Internet, Anhui University, Hefei 230601, China
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8
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Xu H, Zhao Y, Han D. The impact of the global and local awareness diffusion on epidemic transmission considering the heterogeneity of individual influences. NONLINEAR DYNAMICS 2022; 110:901-914. [PMID: 35847410 PMCID: PMC9272667 DOI: 10.1007/s11071-022-07640-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we propose a coupled awareness-epidemic spreading model considering the heterogeneity of individual influences, which aims to explore the interaction between awareness diffusion and epidemic transmission. The considered heterogeneities of individual influences are threefold: the heterogeneity of individual influences in the information layer, the heterogeneity of individual influences in the epidemic layer and the heterogeneity of individual behavioral responses to epidemics. In addition, the individuals' receptive preference for information and the impacts of individuals' perceived local awareness ratio and individuals' perceived epidemic severity on self-protective behavior are included. The epidemic threshold is theoretically established by the microscopic Markov chain approach and the mean-field approach. Results indicate that the critical local and global awareness ratios have two-stage effects on the epidemic threshold. Besides, either the heterogeneity of individual influences in the information layer or the strength of individuals' responses to epidemics can influence the epidemic threshold with a nonlinear way. However, the heterogeneity of individual influences in the epidemic layer has few effect on the epidemic threshold, but can affects the magnitude of the final infected density.
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Affiliation(s)
- Haidong Xu
- School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu 212013 China
| | - Ye Zhao
- School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu 212013 China
| | - Dun Han
- School of Mathematical Sciences, Jiangsu University, Zhenjiang, Jiangsu 212013 China
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9
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Wu J, Zuo R, He C, Xiong H, Zhao K, Hu Z. The effect of information literacy heterogeneity on epidemic spreading in information and epidemic coupled multiplex networks. PHYSICA A 2022; 596:127119. [PMID: 35342220 PMCID: PMC8936001 DOI: 10.1016/j.physa.2022.127119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/02/2022] [Indexed: 06/14/2023]
Abstract
With the COVID-19 pandemic, better understanding of the co-evolution of information and epidemic diffusion networks is important for pandemic-related policies. Using the microscopic Markov chain method, this study proposed an aware-susceptible-infected model (ASI) to explore the effect of information literacy on the spreading process in such multiplex networks. We first introduced a parameter that adjusts the self-protection related execution ability of aware individuals in order to emphasis the importance of protective behaviors compared to awareness in decreasing the infection probability. The model also captures individuals' heterogeneity in their information literacy. Simulation experiments found that the high information-literate individuals are more sensitive to information adoption. In addition, epidemic information can help to suppress the epidemic diffusion only when individuals' abilities of transforming awareness into actual protective behaviors attain a threshold. In communities dominated by highly literate individuals, a larger information literacy gap can improve awareness acquisition and thus help to suppress the epidemic among the whole group. By contrast, in communities dominated by low information-literate individuals, a smaller information literacy gap can better prevent the epidemic diffusion. This study contributes to the literature by revealing the importance of individuals' heterogeneity of information literacy on epidemic spreading in different communities and has implications for how to inform people when a new epidemic disease emerges.
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Affiliation(s)
- Jiang Wu
- School of Information Management, Wuhan University, Wuhan, 430072, China
- Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, China
| | - Renxian Zuo
- School of Information Management, Wuhan University, Wuhan, 430072, China
- Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, China
| | - Chaocheng He
- School of Information Management, Wuhan University, Wuhan, 430072, China
- Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, China
| | - Hang Xiong
- College of Economics and Management, Huazhong Agricultural University, Wuhan, 430070, China
| | - Kang Zhao
- Tippie College of Business, The University of Iowa, Iowa City, IA 52242, USA
| | - Zhongyi Hu
- School of Information Management, Wuhan University, Wuhan, 430072, China
- Center for Ecommerce Research and Development, Wuhan University, Wuhan, 430072, China
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10
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Epidemic Dynamics of a Fractional-Order SIR Weighted Network Model and Its Targeted Immunity Control. FRACTAL AND FRACTIONAL 2022. [DOI: 10.3390/fractalfract6050232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
With outbreaks of epidemics, an enormous loss of life and property has been caused. Based on the influence of disease transmission and information propagation on the transmission characteristics of infectious diseases, in this paper, a fractional-order SIR epidemic model is put forward on a two-layer weighted network. The local stability of the disease-free equilibrium is investigated. Moreover, a conclusion is obtained that there is no endemic equilibrium. Since the elderly and the children have fewer social tiers, a targeted immunity control that is based on age structure is proposed. Finally, an example is presented to demonstrate the effectiveness of the theoretical results. These studies contribute to a more comprehensive understanding of the epidemic transmission mechanism and play a positive guiding role in the prevention and control of some epidemics.
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11
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Sontag A, Rogers T, Yates CA. Misinformation can prevent the suppression of epidemics. J R Soc Interface 2022; 19:20210668. [PMID: 35350880 PMCID: PMC8965399 DOI: 10.1098/rsif.2021.0668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 03/01/2022] [Indexed: 12/21/2022] Open
Abstract
The effectiveness of non-pharmaceutical interventions, such as mask-wearing and social distancing, as control measures for pandemic disease relies upon a conscientious and well-informed public who are aware of and prepared to follow advice. Unfortunately, public health messages can be undermined by competing misinformation and conspiracy theories, spread virally through communities that are already distrustful of expert opinion. In this article, we propose and analyse a simple model of the interaction between disease spread and awareness dynamics in a heterogeneous population composed of both trusting individuals who seek better quality information and will take precautionary measures, and distrusting individuals who reject better quality information and have overall riskier behaviour. We show that, as the density of the distrusting population increases, the model passes through a phase transition to a state in which major outbreaks cannot be suppressed. Our work highlights the urgent need for effective interventions to increase trust and inform the public.
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Affiliation(s)
- Andrei Sontag
- Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK
| | - Tim Rogers
- Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK
| | - Christian A. Yates
- Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK
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12
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Zuo C, Zhu F, Ling Y. Analyzing COVID-19 Vaccination Behavior Using an SEIRM/V Epidemic Model With Awareness Decay. Front Public Health 2022; 10:817749. [PMID: 35155327 PMCID: PMC8829349 DOI: 10.3389/fpubh.2022.817749] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/03/2022] [Indexed: 12/15/2022] Open
Abstract
Information awareness about COVID-19 spread through multiple channels can stimulate individuals to vaccinate to protect themselves and reduce the infection rate. However, the awareness individuals may lose competency over time due to the decreasing quality of the information and fading of awareness. This paper introduces awareness programs, which can not only change people from unaware to aware state, but also from aware to unaware state. Then an SEIRM/V mathematical model is derived to study the influence of awareness programs on individual vaccination behavior. We evaluate the dynamical evolution of the system model and perform the numerical simulation, and examine the effects of awareness transformation based on the COVID-19 vaccination case in China. The results show that awareness spread through various information sources is positively associated with epidemic containment while awareness fading negatively correlates with vaccination coverage.
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13
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An F, Wu S, Gao X, Eugene Stanley H, Gao J. A quantification method of non-failure cascading spreading in a network of networks. CHAOS (WOODBURY, N.Y.) 2021; 31:123122. [PMID: 34972350 DOI: 10.1063/5.0067479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
The cascading spreading process in social and economic networks is more complicated than that in physical systems. These networks' multiple nodes and edges increase their structural complexity and recoverability, enabling the system to lose partial functionality rather than completely fail. However, these phenomena in social and economic networks introduce challenges to the existing network robustness models, where a node is either in a functional state or a failed state. This research uses a network of networks (NoN) to simulate multiple types of nodes and edges. A non-failure cascading process is utilized to model the nodes' self-adaptation and recoverability. The main contribution of this research is proposing a spreading model to extend the non-failure cascading process to the NoN, which can be used in predicting real-world system damage suffering from special events. The case study of this research evaluated the effect degree of crude oil trade changes on each sector from 2015 to 2016.
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Affiliation(s)
- Feng An
- School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
| | - Sen Wu
- School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiangyun Gao
- School of Economics and Management, China University of Geosciences Beijing, Beijing 100083, China
| | - H Eugene Stanley
- Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA
| | - Jianxi Gao
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
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14
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Sun M, Tao Y, Fu X. Asymmetrical dynamics of epidemic propagation and awareness diffusion in multiplex networks. CHAOS (WOODBURY, N.Y.) 2021; 31:093134. [PMID: 34598447 DOI: 10.1063/5.0061086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
To better explore asymmetrical interaction between epidemic spreading and awareness diffusion in multiplex networks, we distinguish susceptibility and infectivity between aware and unaware individuals, relax the degree of immunization, and take into account three types of generation mechanisms of individual awareness. We use the probability trees to depict the transitions between distinct states for nodes and then write the evolution equation of each state by means of the microscopic Markovian chain approach (MMCA). Based on the MMCA, we theoretically analyze the possible steady states and calculate the critical threshold of epidemics, related to the structure of epidemic networks, the awareness diffusion, and their coupling configuration. The achieved analytical results of the mean-field approach are consistent with those of the numerical Monte Carlo simulations. Through the theoretical analysis and numerical simulations, we find that global awareness can reduce the final scale of infection when the regulatory factor of the global awareness ratio is less than the average degree of the epidemic network but it cannot alter the onset of epidemics. Furthermore, the introduction of self-awareness originating from infected individuals not only reduces the epidemic prevalence but also raises the epidemic threshold, which tells us that it is crucial to enhance the early warning of symptomatic individuals during pandemic outbreaks. These results give us a more comprehensive and deep understanding of the complicated interaction between epidemic transmission and awareness diffusion and also provide some practical and effective recommendations for the prevention and control of epidemics.
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Affiliation(s)
- Mengfeng Sun
- School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China
| | - Yizhou Tao
- College of Science, Shanghai Institute of Technology, Shanghai 201418, China
| | - Xinchu Fu
- Department of Mathematics, Shanghai University, Shanghai 200444, China
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15
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Meng X, Cai Z, Si S, Duan D. Analysis of epidemic vaccination strategies on heterogeneous networks: Based on SEIRV model and evolutionary game. APPLIED MATHEMATICS AND COMPUTATION 2021; 403:126172. [PMID: 33758440 PMCID: PMC7977478 DOI: 10.1016/j.amc.2021.126172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/31/2021] [Accepted: 03/04/2021] [Indexed: 06/12/2023]
Abstract
Nowadays, vaccination is the most effective way to control the epidemic spreading. In this paper, an epidemic SEIRV (susceptible-exposed-infected-removed -vaccinated) model and an evolutionary game model are established to analyze the difference between mandatory vaccination method and voluntary vaccination method on heterogeneous networks. Firstly, we divide the population into four categories, including susceptible individuals, exposed individuals, infected individuals and removed individuals. Based on the mean field approximation theory, differential equations are developed to characterize the changes of the proportions of the four groups over time under mandatory vaccination. Then through the analysis of the differential equations, the disease-free equilibrium point (DFE) and the endemic disease equilibrium point (EDE) are obtained. Also, the basic reproduction number is obtained by the next-generation matrix method and the stability analysis of the equilibrium points is performed. Next, by considering factors such as vaccination cost, treatment cost and government subsidy rate, differential equations are established to represent the change of vaccination rate over time. By analyzing the final vaccination coverage rate, we can get the minimum vaccination cost to make infectious disease disappear. Finally, the Monte Carlo method is used for numerical simulation to verify the results obtained from the theoretical analysis. Using the SARS-Cov-2 pandemic data from Wuhan, China, the experimental results show that when the effectiveness rate of vaccination is 0.75, the vaccination cost is not higher than 0.886 so that the vaccination strategy can be spread among the population. If mandatory vaccination is adopted, the minimum vaccination rate is 0.146.
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Affiliation(s)
- Xueyu Meng
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zhiqiang Cai
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shubin Si
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
| | - Dongli Duan
- School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710311, China
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16
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Zhao X, Zhou Q, Wang A, Zhu F, Meng Z, Zuo C. The impact of awareness diffusion on the spread of COVID-19 based on a two-layer SEIR/V-UA epidemic model. J Med Virol 2021; 93:4342-4350. [PMID: 33738825 PMCID: PMC8250399 DOI: 10.1002/jmv.26945] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 02/02/2021] [Accepted: 03/15/2021] [Indexed: 12/24/2022]
Abstract
In this paper, we propose a new susceptible-vaccinated-exposed-infected-recovered with unaware-aware (SEIR/V-UA) model to study the mutual effect between the epidemic spreading and information diffusion. We investigate the dynamic processes of the model with a Kinetic equation and derive the expression for epidemic stability by the eigenvalues of the Jacobian matrix. Then, we validate the model by the Monte Carlo method and numerical simulation on a two-layer scale-free network. With the outbreak of COVID-19, the spread of the epidemic in China prompted drastic measures for transmission containment. We examine the effects of these interventions based on modeling of the information-epidemic and the data of the COVID-19 epidemic case. The results further demonstrate that the epidemic spread can be affected by the effective transmission rate of awareness.
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Affiliation(s)
- Xueke Zhao
- School of Management Engineering and E‐commerceZhejiang Gongshang UniversityHangzhouChina
| | - Qingming Zhou
- School of Management Engineering and E‐commerceZhejiang Gongshang UniversityHangzhouChina
| | - Anjing Wang
- School of Management Engineering and E‐commerceZhejiang Gongshang UniversityHangzhouChina
| | - Fenping Zhu
- School of Management Engineering and E‐commerceZhejiang Gongshang UniversityHangzhouChina
| | - Zeyang Meng
- School of Management Engineering and E‐commerceZhejiang Gongshang UniversityHangzhouChina
| | - Chao Zuo
- School of Management Engineering and E‐commerceZhejiang Gongshang UniversityHangzhouChina
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17
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Huang H, Chen Y, Yan Z. Impacts of social distancing on the spread of infectious diseases with asymptomatic infection: A mathematical model. APPLIED MATHEMATICS AND COMPUTATION 2021; 398:125983. [PMID: 33518834 PMCID: PMC7833012 DOI: 10.1016/j.amc.2021.125983] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 01/04/2021] [Accepted: 01/09/2021] [Indexed: 06/02/2023]
Abstract
Social distancing can be divided into two categories: spontaneous social distancing adopted by the individuals themselves, and public social distancing promoted by the government. Both types of social distancing have been proved to suppress the spread of infectious disease effectively. While previous studies examined the impact of each social distancing separately, the simultaneous impacts of them are less studied. In this research, we develop a mathematical model to analyze how spontaneous social distancing and public social distancing simultaneously affect the outbreak threshold of an infectious disease with asymptomatic infection. A communication-contact two-layer network is constructed to consider the difference between spontaneous social distancing and public social distancing. Based on link overlap of the two layers, the two-layer network is divided into three subnetworks: communication-only network, contact-only network, and overlapped network. Our results show that public social distancing can significantly increase the outbreak threshold of an infectious disease. To achieve better control effect, the subnetwork of higher infection risk should be more targeted by public social distancing, but the subnetworks of lower infection risk shouldn't be overlooked. The impact of spontaneous social distancing is relatively weak. On the one hand, spontaneous social distancing in the communication-only network has no impact on the outbreak threshold of the infectious disease. On the other hand, the impact of spontaneous social distancing in the overlapped network is highly dependent on the detection of asymptomatic infection sources. Moreover, public social distancing collaborates with infection detection on controlling an infectious disease, but their impacts can't add up perfectly. Besides, public social distancing is slightly less effective than infection detection, because infection detection can also promote spontaneous social distancing.
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Affiliation(s)
- He Huang
- School of Economics and Management, China University of Geosciences (Beijing), Beijing 100083, China
| | - Yahong Chen
- School of Information, Beijing Wuzi University, Beijing 101149, China
| | - Zhijun Yan
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
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18
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Chen X, Gong K, Wang R, Cai S, Wang W. Effects of heterogeneous self-protection awareness on resource-epidemic coevolution dynamics. APPLIED MATHEMATICS AND COMPUTATION 2020; 385:125428. [PMID: 32834189 PMCID: PMC7305516 DOI: 10.1016/j.amc.2020.125428] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 05/11/2020] [Accepted: 05/31/2020] [Indexed: 06/11/2023]
Abstract
Recent studies have demonstrated that the allocation of individual resources has a significant influence on the dynamics of epidemic spreading. In the real scenario, individuals have a different level of awareness for self-protection when facing the outbreak of an epidemic. To investigate the effects of the heterogeneous self-awareness distribution on the epidemic dynamics, we propose a resource-epidemic coevolution model in this paper. We first study the effects of the heterogeneous distributions of node degree and self-awareness on the epidemic dynamics on artificial networks. Through extensive simulations, we find that the heterogeneity of self-awareness distribution suppresses the outbreak of an epidemic, and the heterogeneity of degree distribution enhances the epidemic spreading. Next, we study how the correlation between node degree and self-awareness affects the epidemic dynamics. The results reveal that when the correlation is positive, the heterogeneity of self-awareness restrains the epidemic spreading. While, when there is a significant negative correlation, strong heterogeneous or strong homogeneous distribution of the self-awareness is not conducive for disease suppression. We find an optimal heterogeneity of self-awareness, at which the disease can be suppressed to the most extent. Further research shows that the epidemic threshold increases monotonously when the correlation changes from most negative to most positive, and a critical value of the correlation coefficient is found. When the coefficient is below the critical value, an optimal heterogeneity of self-awareness exists; otherwise, the epidemic threshold decreases monotonously with the decline of the self-awareness heterogeneity. At last, we verify the results on four typical real-world networks and find that the results on the real-world networks are consistent with those on the artificial network.
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Affiliation(s)
- Xiaolong Chen
- School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
- Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province, School of Economic Information Engineering, Chengdu 611130, China
| | - Kai Gong
- School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
| | - Ruijie Wang
- A Ba Teachers University, A Ba 623002, China
| | - Shimin Cai
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Wang
- Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
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19
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Li X, Liu Q. Social Media Use, eHealth Literacy, Disease Knowledge, and Preventive Behaviors in the COVID-19 Pandemic: Cross-Sectional Study on Chinese Netizens. J Med Internet Res 2020; 22:e19684. [PMID: 33006940 PMCID: PMC7581310 DOI: 10.2196/19684] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/10/2020] [Accepted: 09/23/2020] [Indexed: 01/17/2023] Open
Abstract
Background Since its outbreak in January 2020, COVID-19 has quickly spread worldwide and has become a global pandemic. Social media platforms have been recognized as important tools for health-promoting practices in public health, and the use of social media is widespread among the public. However, little is known about the effects of social media use on health promotion during a pandemic such as COVID-19. Objective In this study, we aimed to explore the predictive role of social media use on public preventive behaviors in China during the COVID-19 pandemic and how disease knowledge and eHealth literacy moderated the relationship between social media use and preventive behaviors. Methods A national web-based cross-sectional survey was conducted by a proportionate probability sampling among 802 Chinese internet users (“netizens”) in February 2020. Descriptive statistics, Pearson correlations, and hierarchical multiple regressions were employed to examine and explore the relationships among all the variables. Results Almost half the 802 study participants were male (416, 51.9%), and the average age of the participants was 32.65 years. Most of the 802 participants had high education levels (624, 77.7%), had high income >¥5000 (US $736.29) (525, 65.3%), were married (496, 61.8%), and were in good health (486, 60.6%). The average time of social media use was approximately 2 to 3 hours per day (mean 2.34 hours, SD 1.11), and the most frequently used media types were public social media (mean score 4.49/5, SD 0.78) and aggregated social media (mean score 4.07/5, SD 1.07). Social media use frequency (β=.20, P<.001) rather than time significantly predicted preventive behaviors for COVID-19. Respondents were also equipped with high levels of disease knowledge (mean score 8.15/10, SD 1.43) and eHealth literacy (mean score 3.79/5, SD 0.59). Disease knowledge (β=.11, P=.001) and eHealth literacy (β=.27, P<.001) were also significant predictors of preventive behaviors. Furthermore, eHealth literacy (P=.038) and disease knowledge (P=.03) positively moderated the relationship between social media use frequency and preventive behaviors, while eHealth literacy (β=.07) affected this relationship positively and disease knowledge (β=–.07) affected it negatively. Different social media types differed in predicting an individual’s preventive behaviors for COVID-19. Aggregated social media (β=.22, P<.001) was the best predictor, followed by public social media (β=.14, P<.001) and professional social media (β=.11, P=.002). However, official social media (β=.02, P=.597) was an insignificant predictor. Conclusions Social media is an effective tool to promote behaviors to prevent COVID-19 among the public. Health literacy is essential for promotion of individual health and influences the extent to which the public engages in preventive behaviors during a pandemic. Our results not only enrich the theoretical paradigm of public health management and health communication but also have practical implications in pandemic control for China and other countries.
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Affiliation(s)
- Xiaojing Li
- Center for Health and Medical Communication, School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China
| | - Qinliang Liu
- Center for Health and Medical Communication, School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China
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20
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Karlsson CJ, Rowlett J. Decisions and disease: a mechanism for the evolution of cooperation. Sci Rep 2020; 10:13113. [PMID: 32753581 PMCID: PMC7403384 DOI: 10.1038/s41598-020-69546-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 07/13/2020] [Indexed: 01/01/2023] Open
Abstract
In numerous contexts, individuals may decide whether they take actions to mitigate the spread of disease, or not. Mitigating the spread of disease requires an individual to change their routine behaviours to benefit others, resulting in a 'disease dilemma' similar to the seminal prisoner's dilemma. In the classical prisoner's dilemma, evolutionary game dynamics predict that all individuals evolve to 'defect.' We have discovered that when the rate of cooperation within a population is directly linked to the rate of spread of the disease, cooperation evolves under certain conditions. For diseases which do not confer immunity to recovered individuals, if the time scale at which individuals receive accurate information regarding the disease is sufficiently rapid compared to the time scale at which the disease spreads, then cooperation emerges. Moreover, in the limit as mitigation measures become increasingly effective, the disease can be controlled; the number of infections tends to zero. It has been suggested that disease spreading models may also describe social and group dynamics, indicating that this mechanism for the evolution of cooperation may also apply in those contexts.
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Affiliation(s)
- Carl-Joar Karlsson
- Department of Mathematical Sciences, Chalmers University of Technology and The University of Gothenburg, 41296, Gothenburg, Sweden
| | - Julie Rowlett
- Department of Mathematical Sciences, Chalmers University of Technology and The University of Gothenburg, 41296, Gothenburg, Sweden.
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21
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Wu Q, Chen S. Spreading of two interacting diseases in multiplex networks. CHAOS (WOODBURY, N.Y.) 2020; 30:073115. [PMID: 32752628 DOI: 10.1063/5.0009588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 06/23/2020] [Indexed: 05/27/2023]
Abstract
We consider the interacting processes between two diseases on multiplex networks, where each node can be infected by two interacting diseases with general interacting schemes. A discrete-time individual-based probability model is rigorously derived. By the bifurcation analysis of the equilibrium, we analyze the outbreak condition of one disease. The theoretical predictions are in good agreement with discrete-time stochastic simulations on scale-free networks. Furthermore, we discuss the influence of network overlap and dynamical parameters on the epidemic dynamical behaviors. The simulation results show that the network overlap has almost no effect on both epidemic threshold and prevalence. We also find that the epidemic threshold of one disease does not depend on all system parameters. Our method offers an analytical framework for the spreading dynamics of multiple processes in multiplex networks.
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Affiliation(s)
- Qingchu Wu
- College of Mathematics and Information Science, Jiangxi Normal University, Jiangxi 330022, China
| | - Shufang Chen
- Academic Affairs Office, Jiangxi Normal University, Jiangxi 330022, China
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22
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Luo X, Yang J, Jin Z, Li J. An edge-based model for non-Markovian sexually transmitted infections in coupled network. INT J BIOMATH 2020. [DOI: 10.1142/s179352452050014x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, allowing for general transmission and recovery times distributions, we proposed an edge-based age-structured-like compartmental model for STIs (EBACMS) in a coupled network. We considered sexual transmissions between men with also heterosexual contacts. Mathematically, we gave the general approach of proving the nonnegativity of solutions for the system coupling ordinary and partial differential equations, which can be applied to all edge-based compartment models. We then analyzed the epidemic threshold [Formula: see text] with different distributions which couples the thresholds of the single-layer and bipartite networks in the percolation theory. We also studied the global stability of disease-free equilibrium with [Formula: see text] and the final epidemic size [Formula: see text] (the proportion of the population experiencing infection during the epidemic) with [Formula: see text]. In addition, numerical simulations indicated that given a fixed exponential transmission distribution, a higher variance (with same mean) in general recovery distribution gives smaller [Formula: see text] and [Formula: see text]. Sensitivity analysis on [Formula: see text] and [Formula: see text] in terms of the parameters illustrated that male-to-male transmission routes have a greater impact on [Formula: see text] and [Formula: see text] than the heterosexual transmission routes for the Markovian transmission process and arbitrary recovery process. The results provide a good theoretical guideline to consider the distributions of real-world STIs.
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Affiliation(s)
- Xiaofeng Luo
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, P. R. China
- Complex System Research Center, Shanxi University, Taiyuan 030006, Shanxi, P. R. China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan 030006, Shanxi, P. R. China
- Key Laboratory of Computational Intelligence, and Chinese Information, Processing of Ministry of Education, Taiyuan 030006, P. R. China
| | - Junyuan Yang
- Complex System Research Center, Shanxi University, Taiyuan 030006, Shanxi, P. R. China
| | - Zhen Jin
- Complex System Research Center, Shanxi University, Taiyuan 030006, Shanxi, P. R. China
| | - Jia Li
- Department of Mathematical Sciences, University of Alabama in Huntsville, Huntsville, AL 35899, USA
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23
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Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks. PHYSICA A 2019; 536:121030. [PMID: 32288109 PMCID: PMC7125818 DOI: 10.1016/j.physa.2019.04.266] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 03/05/2019] [Indexed: 06/02/2023]
Abstract
Multiplex network theory is widely introduced to deepen the understanding of the dynamical interplay between self-protective behavior and epidemic spreading. Most of the existing studies assumed that all infected individuals can transmit disease- related information or can be perceived by their neighbors. However, owing to lack of distinct symptoms for patients in the initial stage of infection, the disease information cannot be transmitted in the population, which may lead to the wrong perception of infection risk and inappropriate behavior response. In this work, we divide infected individuals into Exposed-state (without obvious clinical symptoms) individuals and Infected-state (with evident clinical symptoms) individuals, both of whom can spread disease, but only Infected-state individuals can diffuse disease information. Then, in this work we establish UAU-SEIS (Unaware–Aware–Unaware–Susceptible–Exposed–Infected–Susceptible) model in multiplex networks and analyze the effect of asymptomatic infection and the isolation of Infected-state individuals on the density of infection and the epidemic threshold. Furthermore, we extend the UAU-SEIS model by taking the individual heterogeneity into consideration. Combined Markov chain approach and Monte-Carlo Simulations, we find that asymptomatic infection has an effect on the density of infected individuals and the epidemic threshold, and the extent of the effect is influenced by whether Infected-state individuals are isolated or treated. In addition, results show that the individual heterogeneity can lower the density of infected individuals, but cannot enhance the epidemic threshold. We study the impact of asymptomatic infection on the epidemic spread dynamics in multiplex networks. We assume infected can be isolation and non isolation, then compare the research results of these two cases. We take the individual heterogeneity into consideration and study whether it affect research results.
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24
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Li J, Yang C, Ma X, Gao Y, Fu C, Yang H. Suppressing epidemic spreading by optimizing the allocation of resources between prevention and treatment. CHAOS (WOODBURY, N.Y.) 2019; 29:113108. [PMID: 31779370 DOI: 10.1063/1.5114873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
The rational allocation of resources is crucial to suppress the outbreak of epidemics. Here, we propose an epidemic spreading model in which resources are used simultaneously to prevent and treat disease. Based on the model, we study the impacts of different resource allocation strategies on epidemic spreading. First, we analytically obtain the epidemic threshold of disease using the recurrent dynamical message passing method. Then, we simulate the spreading of epidemics on the Erdős-Rényi (ER) network and the scale-free network and investigate the infection density of disease as a function of the disease infection rate. We find hysteresis loops in the phase transition of the infection density on both types of networks. Intriguingly, when different resource allocation schemes are adopted, the phase transition on the ER network is always a first-order phase transition, while the phase transition on the scale-free network transforms from a hybrid phase transition to a first-order phase transition. Particularly, through extensive numerical simulations, we find that there is an optimal resource allocation scheme, which can best suppress epidemic spreading. In addition, we find that the degree heterogeneity of the network promotes the spreading of disease. Finally, by comparing theoretical and numerical results on a real-world network, we find that our method can accurately predict the spreading of disease on the real-world network.
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Affiliation(s)
- Jiayang Li
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chun Yang
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiaotian Ma
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yachun Gao
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chuanji Fu
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hongchun Yang
- School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China
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25
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Wang W, Liu QH, Liang J, Hu Y, Zhou T. Coevolution spreading in complex networks. PHYSICS REPORTS 2019; 820:1-51. [PMID: 32308252 PMCID: PMC7154519 DOI: 10.1016/j.physrep.2019.07.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 06/27/2019] [Accepted: 07/18/2019] [Indexed: 05/03/2023]
Abstract
The propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.
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Affiliation(s)
- Wei Wang
- Cybersecurity Research Institute, Sichuan University, Chengdu 610065, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Quan-Hui Liu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Compleχ Lab, University of Electronic Science and Technology of China, Chengdu 610054, China
- College of Computer Science, Sichuan University, Chengdu 610065, China
| | - Junhao Liang
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China
| | - Yanqing Hu
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
- Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, 519082, China
| | - Tao Zhou
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Compleχ Lab, University of Electronic Science and Technology of China, Chengdu 610054, China
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26
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Wang Z, Tang M, Cai S, Liu Y, Zhou J, Han D. Self-awareness control effect of cooperative epidemics on complex networks. CHAOS (WOODBURY, N.Y.) 2019; 29:053123. [PMID: 31154796 DOI: 10.1063/1.5063960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 04/23/2019] [Indexed: 06/09/2023]
Abstract
Coinfection mechanism is a common interacting mode between multiple diseases in real spreading processes, where the diseases mutually increase their susceptibility, and has aroused widespread studies in network science. We use the bond percolation theory to characterize the coinfection model under two self-awareness control strategies, including immunization strategy and quarantine strategy, and to study the impacts of the synergy effect and control strategies on cooperative epidemics. We find that strengthening the synergy effect can reduce the epidemic threshold and enhance the outbreak size of coinfected networks. On Erdős-Rényi networks, the synergy effect will induce a crossover phenomenon of phase transition, i.e., make the type of phase transition from being continuous to discontinuous. Self-awareness control strategies play a non-negligible role in suppressing cooperative epidemics. In particular, increasing immunization or the quarantine rate can enhance the epidemic threshold and reduce the outbreak size of cooperative epidemics, and lead to a crossover phenomenon of transition from being discontinuous to continuous. The impact of quarantine strategy on cooperative epidemics is more significant than the immunization strategy, which is verified on scale-free networks.
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Affiliation(s)
- Zexun Wang
- School of Information Science Technology, East China Normal University, Shanghai 200241, China
| | - Ming Tang
- School of Information Science Technology, East China Normal University, Shanghai 200241, China
| | - Shimin Cai
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ying Liu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jie Zhou
- School of Physics and Materials Science, East China Normal University, Shanghai 200241, China
| | - Dingding Han
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
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27
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Xia C, Wang Z, Zheng C, Guo Q, Shi Y, Dehmer M, Chen Z. A new coupled disease-awareness spreading model with mass media on multiplex networks. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2018.08.050] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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28
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Xu Z, Li K, Sun M, Fu X. Interaction between epidemic spread and collective behavior in scale-free networks with community structure. J Theor Biol 2018; 462:122-133. [PMID: 30423306 DOI: 10.1016/j.jtbi.2018.11.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 10/28/2018] [Accepted: 11/04/2018] [Indexed: 01/26/2023]
Abstract
Many real-world networks exhibit community structure: the connections within each community are dense, while connections between communities are sparser. Moreover, there is a common but non-negligible phenomenon, collective behaviors, during the outbreak of epidemics, are induced by the emergence of epidemics and in turn influence the process of epidemic spread. In this paper, we explore the interaction between epidemic spread and collective behavior in scale-free networks with community structure, by constructing a mathematical model that embeds community structure, behavioral evolution and epidemic transmission. In view of the differences among individuals' responses in different communities to epidemics, we use nonidentical functions to describe the inherent dynamics of individuals. In practice, with the progress of epidemics, individual behaviors in different communities may tend to cluster synchronization, which is indicated by the analysis of our model. By using comparison principle and Gers˘gorin theorem, we investigate the epidemic threshold of the model. By constructing an appropriate Lyapunov function, we present the stability analysis of behavioral evolution and epidemic dynamics. Some numerical simulations are performed to illustrate and complement our theoretical results. It is expected that our work can deepen the understanding of interaction between cluster synchronization and epidemic dynamics in scale-free community networks.
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Affiliation(s)
- Zhongpu Xu
- Department of Mathematics, Shanghai University, Shanghai 200444, China
| | - Kezan Li
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China
| | - Mengfeng Sun
- Department of Mathematics, Shanghai University, Shanghai 200444, China
| | - Xinchu Fu
- Department of Mathematics, Shanghai University, Shanghai 200444, China.
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29
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Zhou S, Xu S, Wang L, Liu Z, Chen G, Wang X. Propagation of interacting diseases on multilayer networks. Phys Rev E 2018; 98:012303. [PMID: 30110758 DOI: 10.1103/physreve.98.012303] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Indexed: 11/07/2022]
Abstract
The study on the dynamics of interacting diseases has attracted considerable attention in recent years. This paper proposes a model for two interacting epidemics spreading concurrently on a two-layer network, where both the epidemic thresholds and the dynamics of disease outbreaks are investigated. The analytical expression of the epidemic threshold shows strong coupling between the two interacting epidemics. Moreover, two metrics, the maximum prevalence and the highest spreading speed, are proposed to describe the outbreak process. Theoretical analysis together with extensive simulations illustrate the functions of various factors, including the network topological parameters, percentage of overlapped network links, vulnerable individuals, and the reciprocity of the two diseases. It is found that the seemingly important factor, i.e., the percentage of overlapped links, possesses no effect on the propagation, while the frequently overlooked factor, i.e., the percentage of vulnerable individuals, has significant effects. For the interaction of the two diseases, the recovery state of one disease is more influential than the other in both the mutually enhanced and the mutually impaired situations.
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Affiliation(s)
- Shenyu Zhou
- Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
| | - Shuangdie Xu
- Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
| | - Lin Wang
- Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
| | - Zhixin Liu
- Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, CAS, Beijing, 100190, China
| | - Guanrong Chen
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Xiaofan Wang
- Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
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30
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Mao X, Shang P, Wang J, Ma Y. Characterizing time series by extended complexity-entropy curves based on Tsallis, Rényi, and power spectral entropy. CHAOS (WOODBURY, N.Y.) 2018; 28:113106. [PMID: 30501212 PMCID: PMC9984240 DOI: 10.1063/1.5038758] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 10/14/2018] [Indexed: 06/09/2023]
Abstract
In this paper, we create three different entropy curves, Tsallis q-complexity-entropy curve, Rényi r-complexity-entropy curve, and Tsallis-Rényi entropy curve via extending the traditional complexity-entropy causality plane and replacing the permutation entropy into power spectral entropy. This kind of method is free of any parameters and some features that are obscure in the time domain can be extracted in the frequency domain. Results from numerical simulations verify that these three entropy curves can characterize time series efficiently. Chaotic and stochastic time series can be distinguished based on whether the q-complexity-entropy curves are opened or closed. The unrelated stochastic process has a negative curvature associated with the Rényi r-complexity-entropy curve, whereas there are positive curvatures for related cases. In addition, the Tsallis-Rényi entropy curve can display the relationship between two entropies. Finally, we apply this method to sleep electrocardiogram and electroencephalography signals. It is proved that these signals possess similar features with long-range correlated 1/f noise. It is robust enough to exhibit different characteristics for each sleep stage. By using surrogate data sets, the nonlinearity of simulated chaotic time series and sleep data can be identified.
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Affiliation(s)
- Xuegeng Mao
- School of Science, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
| | - Pengjian Shang
- School of Science, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
| | - Jing Wang
- Department of Computer Science and Technology, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, People’s Republic of China
| | - Yan Ma
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215-5400, USA
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31
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Sagar V, Zhao Y, Sen A. Effect of time varying transmission rates on the coupled dynamics of epidemic and awareness over a multiplex network. CHAOS (WOODBURY, N.Y.) 2018; 28:113125. [PMID: 30501210 DOI: 10.1063/1.5042575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 11/08/2018] [Indexed: 06/09/2023]
Abstract
A non-linear stochastic model is presented to study the effect of time variation of transmission rates on the co-evolution of epidemics and its corresponding awareness over a two layered multiplex network. In the model, the infection transmission rate of a given node in the epidemic layer depends upon its awareness probability in the awareness layer. Similarly, the infection information transmission rate of a node in the awareness layer depends upon its infection probability in the epidemic layer. The spread of disease resulting from physical contacts is described in terms of a Susceptible Infected Susceptible process over the epidemic layer and the spread of information about the disease outbreak is described in terms of an Unaware Aware Unaware process over the virtual interaction mediated awareness layer. The time variation of the transmission rates and the resulting co-evolution of these mutually competing processes are studied in terms of a network topology dependent parameter ( α ). Using a second order linear theory, it is shown that in the continuous time limit, the co-evolution of these processes can be described in terms of damped and driven harmonic oscillator equations. From the results of a Monte-Carlo simulation, it is shown that for a suitable choice of the parameter ( α ) , the two processes can either exhibit sustained oscillatory or damped dynamics. The damped dynamics corresponds to the endemic state. Furthermore, for the case of an endemic state, it is shown that the inclusion of the awareness layer significantly lowers the disease transmission rate and reduces the size of the epidemic. The infection probability of the nodes in the endemic state is found to have a dependence on both the transmission rates and on their absolute degrees in each of the network layers and on the relative differences between their degrees in the respective layers.
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Affiliation(s)
- Vikram Sagar
- Harbin Institute of Technology, Shenzhen 518055, China
| | - Yi Zhao
- Harbin Institute of Technology, Shenzhen 518055, China
| | - Abhijit Sen
- Institute For Plasma Research, Gandhinagar 382428, India
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32
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Pan Y, Yan Z. The impact of individual heterogeneity on the coupled awareness-epidemic dynamics in multiplex networks. CHAOS (WOODBURY, N.Y.) 2018. [PMID: 29960396 DOI: 10.1016/j.physa.2017.08.082] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Awareness of disease outbreaks can trigger changes in human behavior and has a significant impact on the spread of epidemics. Previous studies usually considered the coupled awareness-epidemic dynamics to be two competing processes that interact in the information and epidemic layers. However, these studies mostly assumed that all aware individuals have the same reduced infectivity and that different neighbors have the same influence on one's perception, ignoring the heterogeneity of individuals. In this paper, we propose a coupled awareness-epidemic spreading model in multiplex networks incorporating three types of heterogeneity: (1) the heterogeneity of individual responses to disease outbreaks, (2) the influence heterogeneity in the epidemic layer, and (3) the influence heterogeneity in the information layer. The theoretical analysis shows that the influence heterogeneity in the information layer has two-stage effects on the epidemic threshold. Moreover, we find that the epidemic threshold in the higher stage depends on the heterogeneity of individual responses and the influence heterogeneity in the epidemic layer, while the epidemic threshold in the lower stage is independent of awareness spreading and individual behaviors. The results give us a better understanding of how individual heterogeneity affects epidemic spreading and provide some practical implications for the control of epidemics.
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Affiliation(s)
- Yaohui Pan
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
| | - Zhijun Yan
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
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33
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Pan Y, Yan Z. The impact of individual heterogeneity on the coupled awareness-epidemic dynamics in multiplex networks. CHAOS (WOODBURY, N.Y.) 2018; 28:063123. [PMID: 29960396 DOI: 10.1063/1.5000280] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Awareness of disease outbreaks can trigger changes in human behavior and has a significant impact on the spread of epidemics. Previous studies usually considered the coupled awareness-epidemic dynamics to be two competing processes that interact in the information and epidemic layers. However, these studies mostly assumed that all aware individuals have the same reduced infectivity and that different neighbors have the same influence on one's perception, ignoring the heterogeneity of individuals. In this paper, we propose a coupled awareness-epidemic spreading model in multiplex networks incorporating three types of heterogeneity: (1) the heterogeneity of individual responses to disease outbreaks, (2) the influence heterogeneity in the epidemic layer, and (3) the influence heterogeneity in the information layer. The theoretical analysis shows that the influence heterogeneity in the information layer has two-stage effects on the epidemic threshold. Moreover, we find that the epidemic threshold in the higher stage depends on the heterogeneity of individual responses and the influence heterogeneity in the epidemic layer, while the epidemic threshold in the lower stage is independent of awareness spreading and individual behaviors. The results give us a better understanding of how individual heterogeneity affects epidemic spreading and provide some practical implications for the control of epidemics.
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Affiliation(s)
- Yaohui Pan
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
| | - Zhijun Yan
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
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34
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Liu G, Liu Z, Jin Z. Dynamics analysis of epidemic and information spreading in overlay networks. J Theor Biol 2018; 444:28-37. [PMID: 29452174 DOI: 10.1016/j.jtbi.2018.02.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 01/04/2018] [Accepted: 02/13/2018] [Indexed: 11/19/2022]
Abstract
We establish an SIS-UAU model to present the dynamics of epidemic and information spreading in overlay networks. The overlay network is represented by two layers: one where the dynamics of the epidemic evolves and another where the information spreads. We theoretically derive the explicit formulas for the basic reproduction number of awareness R0a by analyzing the self-consistent equation and the basic reproduction number of disease R0d by using the next generation matrix. The formula of R0d shows that the effect of awareness can reduce the basic reproduction number of disease. In particular, when awareness does not affect epidemic spreading, R0d is shown to match the existing theoretical results. Furthermore, we demonstrate that the disease-free equilibrium is globally asymptotically stable if R0d<1; and the endemic equilibrium is globally asymptotically stable if R0d>1. Finally, numerical simulations show that information plays a vital role in preventing and controlling disease and effectively reduces the final disease scale.
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Affiliation(s)
- Guirong Liu
- School of Mathematical Sciences, Shanxi University, Taiyuan, Shanxi 030006, PR China.
| | - Zhimei Liu
- School of Mathematical Sciences, Shanxi University, Taiyuan, Shanxi 030006, PR China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi 030006, PR China
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Esquivel-Gómez JDJ, Barajas-Ramírez JG. Efficiency of quarantine and self-protection processes in epidemic spreading control on scale-free networks. CHAOS (WOODBURY, N.Y.) 2018; 28:013119. [PMID: 29390620 DOI: 10.1063/1.5001176] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
One of the most effective mechanisms to contain the spread of an infectious disease through a population is the implementation of quarantine policies. However, its efficiency is affected by different aspects, for example, the structure of the underlining social network where highly connected individuals are more likely to become infected; therefore, the speed of the transmission of the decease is directly determined by the degree distribution of the network. Another aspect that influences the effectiveness of the quarantine is the self-protection processes of the individuals in the population, that is, they try to avoid contact with potentially infected individuals. In this paper, we investigate the efficiency of quarantine and self-protection processes in preventing the spreading of infectious diseases over complex networks with a power-law degree distribution [ P(k)∼k-ν] for different ν values. We propose two alternative scale-free models that result in power-law degree distributions above and below the exponent ν = 3 associated with the conventional Barabási-Albert model. Our results show that the exponent ν determines the effectiveness of these policies in controlling the spreading process. More precisely, we show that for the ν exponent below three, the quarantine mechanism loses effectiveness. However, the efficiency is improved if the quarantine is jointly implemented with a self-protection process driving the number of infected individuals significantly lower.
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Affiliation(s)
- Jose de Jesus Esquivel-Gómez
- División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica (IPICYT), Camino a la Presa San José 2055, Col. Lomas 4a Secc. C.P. 78216, San Luis Potosí, SLP, Mexico
| | - Juan Gonzalo Barajas-Ramírez
- División de Matemáticas Aplicadas, Instituto Potosino de Investigación Científica y Tecnológica (IPICYT), Camino a la Presa San José 2055, Col. Lomas 4a Secc. C.P. 78216, San Luis Potosí, SLP, Mexico
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36
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Wu Q, Chen S. Susceptible-infected-recovered epidemics in random networks with population awareness. CHAOS (WOODBURY, N.Y.) 2017; 27:103107. [PMID: 29092430 DOI: 10.1063/1.4994893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The influence of epidemic information-based awareness on the spread of infectious diseases on networks cannot be ignored. Within the effective degree modeling framework, we discuss the susceptible-infected-recovered model in complex networks with general awareness and general degree distribution. By performing the linear stability analysis, the conditions of epidemic outbreak can be deduced and the results of the previous research can be further expanded. Results show that the local awareness can suppress significantly the epidemic spreading on complex networks via raising the epidemic threshold and such effects are closely related to the formulation of awareness functions. In addition, our results suggest that the recovered information-based awareness has no effect on the critical condition of epidemic outbreak.
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Affiliation(s)
- Qingchu Wu
- College of Mathematics and Information Science, Jiangxi Normal University, Nanchang, Jiangxi 330022, People's Republic of China
| | - Shufang Chen
- College of Physics and Communication Electronics, Jiangxi Normal University, Nanchang, Jiangxi 330022, People's Republic of China
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37
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Wang Z, Andrews MA, Wu ZX, Wang L, Bauch CT. Coupled disease-behavior dynamics on complex networks: A review. Phys Life Rev 2015; 15:1-29. [PMID: 26211717 PMCID: PMC7105224 DOI: 10.1016/j.plrev.2015.07.006] [Citation(s) in RCA: 163] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 06/24/2015] [Accepted: 06/25/2015] [Indexed: 01/30/2023]
Abstract
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.
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Affiliation(s)
- Zhen Wang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan.
| | - Michael A Andrews
- Department of Mathematics and Statistics, University of Guelph, Guelph, ON, N1G 2W1, Canada.
| | - Zhi-Xi Wu
- Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou, Gansu 730000, China.
| | - Lin Wang
- School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China.
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
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