1
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Huo L, Yu Y. The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks. CHAOS, SOLITONS, AND FRACTALS 2023; 169:113229. [PMID: 36844432 PMCID: PMC9942607 DOI: 10.1016/j.chaos.2023.113229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/26/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
In recent years, as the COVID-19 global pandemic evolves, many unprecedented new patterns of epidemic transmission continue to emerge. Reducing the impact of negative information diffusion, calling for individuals to adopt immunization behaviors, and decreasing the infection risk are of great importance to maintain public health and safety. In this paper, we construct a coupled negative information-behavior-epidemic dynamics model by considering the influence of the individual's self-recognition ability and physical quality in multiplex networks. We introduce the Heaviside step function to explore the effect of decision-adoption process on the transmission for each layer, and assume the heterogeneity of the self-recognition ability and physical quality obey the Gaussian distribution. Then, we use the microscopic Markov chain approach (MMCA) to describe the dynamic process and derive the epidemic threshold. Our findings suggest that increasing the clarification strength of mass media and enhancing individuals' self-recognition ability can facilitate the control of the epidemic. And, increasing physical quality can delay the epidemic outbreak and leads to suppress the scale of epidemic transmission. Moreover, the heterogeneity of the individuals in the information diffusion layer leads to a two-stage phase transition, while it leads to a continuous phase transition in the epidemic layer. Our results can provide favorable references for managers in controlling negative information, urging immunization behaviors and suppressing epidemics.
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Affiliation(s)
- Liang'an Huo
- Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yue Yu
- Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
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2
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Wang B, Wu L, Hong X, Han Y. Risk perception and subsidy policy-based voluntary vaccination driven by multiple information sources. PLoS One 2022; 17:e0276177. [PMID: 36227953 PMCID: PMC9560505 DOI: 10.1371/journal.pone.0276177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/02/2022] [Indexed: 11/15/2022] Open
Abstract
Exploring vaccination behavior is fundamental to understand the role of vaccine in suppressing the epidemic. Motivated by the efficient role of the risk perception and the subsidy policy in promoting vaccination, we propose the Risk Perception and the Risk Perception with Subsidy Policy voluntary vaccination strategies with imperfect vaccine. The risk perception is driven by multiple information sources based on global information (released by Public Health Bureau) and local information (from first-order neighbors). In time-varying networks, we use the mean-field approach and the Monte Carlo simulations to analyze the epidemic dynamics under vaccination behavior with imperfect vaccine. We find that vaccination with the incorporation of risk perception and subsidy policy can effectively control the epidemic. Moreover, information from different sources plays different roles. Global information is more helpful in promoting vaccination than local information. In addition, to further understand the influence of vaccination strategies, we calculate the social cost as the cost for the vaccine and treatment, and find that excess vaccination cost results in a higher social cost after the herd immunity. Thus, for balancing the epidemic control and social cost, providing individuals with more global information as well as local information would be helpful in vaccination. These results are expected to provide insightful guidance for designing the policy to promote vaccination.
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Affiliation(s)
- Bing Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai, P. R. China
| | - Lili Wu
- School of Computer Engineering and Science, Shanghai University, Shanghai, P. R. China
| | - Xiao Hong
- School of Computer Engineering and Science, Shanghai University, Shanghai, P. R. China
| | - Yuexing Han
- School of Computer Engineering and Science, Shanghai University, Shanghai, P. R. China
- * E-mail:
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3
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Li XJ, Li C, Li X. The impact of information dissemination on vaccination in multiplex networks. SCIENCE CHINA INFORMATION SCIENCES 2022; 65:172202. [PMCID: PMC9244521 DOI: 10.1007/s11432-020-3076-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 07/25/2020] [Accepted: 10/01/2020] [Indexed: 06/18/2023]
Abstract
The impact of information dissemination on epidemic control is essentially subject to individual behaviors. Vaccination is one of the most effective strategies against the epidemic spread, whose correlation with the information dissemination should be better understood. To this end, we propose an evolutionary vaccination game model in multiplex networks by integrating an information-epidemic spreading process into the vaccination dynamics, and explore how information dissemination influences vaccination. The spreading process is described by a two-layer coupled susceptible-alert-infected-susceptible (SAIS) model, where the strength coefficient between two layers characterizes the tendency and intensity of information dissemination. We find that the impact of information dissemination on vaccination decision-making depends on not only the vaccination cost and network topology, but also the stage of the system evolution. For instance, in a two-layer BA scale-free network, information dissemination helps to improve vaccination density only at the early stage of the system evolution, as well as when the vaccination cost is smaller. A counter-intuitive conclusion that more information transmission cannot promote vaccination is obtained when the vaccination cost is larger. Moreover, we study the impact of the strength coefficient and individual sensitivity on the fraction of infected individuals and social cost, and unveil the role of information dissemination in controlling the epidemic.
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Affiliation(s)
- Xiao-Jie Li
- Adaptive Networks and Control Lab, Department of Electronic Engineering, Fudan University, Shanghai, 200433 China
| | - Cong Li
- Adaptive Networks and Control Lab, Department of Electronic Engineering, Fudan University, Shanghai, 200433 China
- Research Center of Smart Networks and Systems, School of Information Science and Engineering, Fudan University, Shanghai, 200433 China
- MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200433 China
| | - Xiang Li
- Adaptive Networks and Control Lab, Department of Electronic Engineering, Fudan University, Shanghai, 200433 China
- Research Center of Smart Networks and Systems, School of Information Science and Engineering, Fudan University, Shanghai, 200433 China
- MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200433 China
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4
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Cascante-Vega J, Torres-Florez S, Cordovez J, Santos-Vega M. How disease risk awareness modulates transmission: coupling infectious disease models with behavioural dynamics. ROYAL SOCIETY OPEN SCIENCE 2022; 9:210803. [PMID: 35035985 PMCID: PMC8753147 DOI: 10.1098/rsos.210803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 12/08/2021] [Indexed: 06/14/2023]
Abstract
Epidemiological models often assume that individuals do not change their behaviour or that those aspects are implicitly incorporated in parameters in the models. Typically, these assumptions are included in the contact rate between infectious and susceptible individuals. However, adaptive behaviours are expected to emerge and play an important role in the transmission dynamics across populations. Here, we propose a theoretical framework to couple transmission dynamics with behavioural dynamics due to infection awareness. We modelled the dynamics of social behaviour using a game theory framework, which is then coupled with an epidemiological model that captures the disease dynamics by assuming that individuals are aware of the actual epidemiological state to reduce their contacts. Results from the mechanistic model show that as individuals increase their awareness, the steady-state value of the final fraction of infected individuals in a susceptible-infected-susceptible (SIS) model decreases. We also incorporate theoretical contact networks, having the awareness parameter dependent on global or local contacts. Results show that even when individuals increase their awareness of the disease, the spatial structure itself defines the steady state.
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Affiliation(s)
- Jaime Cascante-Vega
- Departamento de Ingeniería Biomédica, Grupo de Investigación en Biología Matemática y Computacional, Universidad de los Andes, Bogotá D.C., Colombia
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Samuel Torres-Florez
- Departamento de Ingeniería Biomédica, Grupo de Investigación en Biología Matemática y Computacional, Universidad de los Andes, Bogotá D.C., Colombia
| | - Juan Cordovez
- Departamento de Ingeniería Biomédica, Grupo de Investigación en Biología Matemática y Computacional, Universidad de los Andes, Bogotá D.C., Colombia
| | - Mauricio Santos-Vega
- Departamento de Ingeniería Biomédica, Grupo de Investigación en Biología Matemática y Computacional, Universidad de los Andes, Bogotá D.C., Colombia
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5
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Ye M, Zino L, Rizzo A, Cao M. Game-theoretic modeling of collective decision making during epidemics. Phys Rev E 2021; 104:024314. [PMID: 34525543 DOI: 10.1103/physreve.104.024314] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/30/2021] [Indexed: 11/07/2022]
Abstract
The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral-epidemic model, in which an interplay of realistic factors shapes the coevolution of individual decision making and epidemics on a network. Although such a coevolution is deeply intertwined in the real world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our paradigm offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks. The framework also allows us to provide analytical insights on the epidemic process and to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model.
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Affiliation(s)
- Mengbin Ye
- School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia
| | - Lorenzo Zino
- Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, Netherlands
| | - Alessandro Rizzo
- Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy
| | - Ming Cao
- Faculty of Science and Engineering, University of Groningen, 9747 AG Groningen, Netherlands
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6
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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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7
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Fan R, Wang Y, Lin J. Study on Multi-Agent Evolutionary Game of Emergency Management of Public Health Emergencies Based on Dynamic Rewards and Punishments. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8278. [PMID: 34444028 PMCID: PMC8391147 DOI: 10.3390/ijerph18168278] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/16/2021] [Accepted: 07/28/2021] [Indexed: 11/28/2022]
Abstract
In the context of public health emergency management, it is worth studying ways to mobilize the enthusiasm of government, community, and residents. This paper adopts the method of combining evolutionary game and system dynamics to conduct a theoretical modeling and simulation analysis on the interactions of the behavioral strategies of the three participants. In response to opportunistic behavior and inadequate supervision in the static reward and punishment mechanism, we introduced a dynamic reward and punishment mechanism that considers changes in the social environment and the situation of epidemic prevention and control. This paper proves that the dynamic reward and punishment mechanism can effectively suppress the fluctuation problem in the evolutionary game process under static scenarios and achieve better supervision results through scenario analysis and simulation experiments.
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Affiliation(s)
| | - Yibo Wang
- School of Economics and Management, Wuhan University, Wuhan 430072, China; (R.F.); (J.L.)
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8
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Castro BM, de Abreu de Melo Y, Fernanda Dos Santos N, Luiz da Costa Barcellos A, Choren R, Salles RM. Multi-agent simulation model for the evaluation of COVID-19 transmission. Comput Biol Med 2021; 136:104645. [PMID: 34325230 PMCID: PMC8275845 DOI: 10.1016/j.compbiomed.2021.104645] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 01/04/2023]
Abstract
This work proposes an agent-based model to analyze the spread processes of the COVID-19 epidemics in open regions and based on hypothetical social scenarios of viral transmissibility. Differently from other previous models, we consider the environment to be a multi-region space in which the epidemic spreads according to the dynamics and the concentration of agents in such regions. This paper suggests that software agents can provide a more suitable model for individuals, and their features, thus showing the influence of civil society in the context of pandemic management. This is achieved by modeling an individual as an agent with a wide range of features (health condition, purchasing power, awareness, mobility, professional activity, age, and gender). The model supports the design of populations and interactions akin to real-life scenarios. Simulation results show that the proposed model can be applied in several ways to support decision-makers to better understand the epidemic spread and the actions that can be taken against the pandemic.
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Affiliation(s)
- Brenno Moura Castro
- Military Institute of Engineering (IME), Pc General Tibúrcio 80, 22290-270, Rio de Janeiro, Brazil.
| | - Yuri de Abreu de Melo
- Military Institute of Engineering (IME), Pc General Tibúrcio 80, 22290-270, Rio de Janeiro, Brazil.
| | | | | | - Ricardo Choren
- Military Institute of Engineering (IME), Pc General Tibúrcio 80, 22290-270, Rio de Janeiro, Brazil.
| | - Ronaldo Moreira Salles
- Military Institute of Engineering (IME), Pc General Tibúrcio 80, 22290-270, Rio de Janeiro, Brazil.
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9
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Kuga K, Tanaka M, Tanimoto J. Pair approximation model for the vaccination game: predicting the dynamic process of epidemic spread and individual actions against contagion. Proc Math Phys Eng Sci 2021; 477:20200769. [PMID: 35153542 PMCID: PMC8317980 DOI: 10.1098/rspa.2020.0769] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 01/12/2021] [Indexed: 12/31/2022] Open
Abstract
We successfully establish a theoretical framework of pairwise approximation for the vaccination game in which both the dynamic process of epidemic spread and individual actions in helping prevent social behaviours are quantitatively evaluated. In contrast with mean-field approximation, our model captures higher-order effects from neighbours by using an underlying network that shows how the disease spreads and how individual decisions evolve over time. This model considers not only imperfect vaccination but also intermediate protective measures other than vaccines. Our analytical predictions are validated by multi-agent simulation results that estimate random regular graphs at varying degrees.
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Affiliation(s)
- Kazuki Kuga
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
| | - Masaki Tanaka
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
| | - Jun Tanimoto
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan.,Faculty of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga-shi, Fukuoka 816-8580, Japan
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10
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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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11
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Gozzi N, Perrotta D, Paolotti D, Perra N. Towards a data-driven characterization of behavioral changes induced by the seasonal flu. PLoS Comput Biol 2020; 16:e1007879. [PMID: 32401809 PMCID: PMC7250468 DOI: 10.1371/journal.pcbi.1007879] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 05/26/2020] [Accepted: 04/15/2020] [Indexed: 11/19/2022] Open
Abstract
In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 - 18 and 2018 - 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals' characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases.
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Affiliation(s)
- Nicolò Gozzi
- Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom
| | | | | | - Nicola Perra
- Networks and Urban Systems Centre, University of Greenwich, London, United Kingdom
- ISI Foundation, Turin, Italy
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12
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Tanaka M, Tanimoto J. Is subsidizing vaccination with hub agent priority policy really meaningful to suppress disease spreading? J Theor Biol 2020; 486:110059. [DOI: 10.1016/j.jtbi.2019.110059] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 10/16/2019] [Accepted: 10/29/2019] [Indexed: 11/26/2022]
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13
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Li W, Zhou J, Lu JA. The effect of behavior of wearing masks on epidemic dynamics. NONLINEAR DYNAMICS 2020; 101:1995-2001. [PMID: 32836804 PMCID: PMC7307808 DOI: 10.1007/s11071-020-05759-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 06/10/2020] [Indexed: 05/09/2023]
Abstract
Recently, COVID-19 has attracted a lot of attention of researchers from different fields. Wearing masks is a frequently adopted precautionary measure. In this paper, we investigate the effect of behavior of wearing masks on epidemic dynamics in the context of COVID-19. At each time, every susceptible individual chooses whether to wear a mask or not in the next time step, which depends on an evaluation of the potential costs and perceived risk of infection. When the cost of infection is high, the majority of the population choose to wear masks, where global awareness plays a significant role. However, if the mask source is limited, global awareness may give rise to a negative result. In this case, more mask source should be allocated to the individuals with high risk of infection.
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Affiliation(s)
- Weiqiang Li
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 China
| | - Jin Zhou
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 China
| | - Jun-an Lu
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 China
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14
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Huang J, Wang J, Xia C. Role of vaccine efficacy in the vaccination behavior under myopic update rule on complex networks. CHAOS, SOLITONS, AND FRACTALS 2020; 130:109425. [PMID: 32288356 PMCID: PMC7111283 DOI: 10.1016/j.chaos.2019.109425] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 08/27/2019] [Accepted: 09/02/2019] [Indexed: 05/25/2023]
Abstract
How to effectively prevent the diffusion of infectious disease has become an intriguing topic in the field of public hygienics. To be noted that, for the non-periodic infectious diseases, many people hope to obtain the vaccine of epidemics in time to be inoculated, rather than at the end of the epidemic. However, the vaccine may fail as a result of invalid storage, transportation and usage, and then vaccinated individuals may become re-susceptible and be infected again during the outbreak. To this end, we build a new framework that considers the imperfect vaccination during the one cycle of infectious disease within the spatially structured and heterogeneous population. Meanwhile, we propose a new vaccination update rule: myopic update rule, which is only based on one focal player's own perception regarding the disease outbreak, and one susceptible individual makes a decision to adopt the vaccine just by comparing the perceived payoffs vaccination with the perceived ones of being infected. Extensive Monte-Carlo simulations are performed to demonstrate the imperfect vaccination behavior under the myopic update rule in the spatially structured and heterogeneous population. The results indicate that healthy individuals are often willing to inoculate the vaccine under the myopic update rule, which can stop the infectious disease from being spread, in particular, it is found that the vaccine efficacy influences the fraction of vaccinated individuals much more than the relative cost of vaccination on the regular lattice, Meanwhile, vaccine efficacy is more sensitive on the heterogeneous scale-free network. Current results are helpful to further analyze and model the choice of vaccination strategy during the disease outbreaks.
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Affiliation(s)
- Jiechen Huang
- Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, PR China
- Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin 300384, China
| | - Juan Wang
- School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300384, PR China
| | - Chengyi Xia
- Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, PR China
- Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin 300384, China
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15
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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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16
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Xin Y, Gerberry D, Just W. Open-minded imitation can achieve near-optimal vaccination coverage. J Math Biol 2019; 79:1491-1514. [PMID: 31327021 DOI: 10.1007/s00285-019-01401-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 06/04/2019] [Indexed: 10/26/2022]
Abstract
Game-theoretic studies of voluntary vaccination predict that a socially unstructured population that is guided exclusively by individual rational self-interest always reaches a Nash equilibrium with vaccination coverage that is below the societal optimum. Human decision-making involves additional mechanisms, such as imitation of the successful strategies of others. However, previous research has found that imitation leads to vaccination coverage that is even below the Nash equilibrium. In this work, we note that these conclusions rely on the widely accepted use of Fermi functions for modeling the probabilities of switching to another strategy. We consider here a more general functional form of the switching probabilities. It involves one additional parameter [Formula: see text]. This parameter can be loosely interpreted as a degree of open-mindedness. The resulting dynamics are consistent with the ones that would be generated by functions that give best fits for empirical data in a widely cited psychological experiment. We show that sufficiently high levels of open-mindedness, as conceptualized by our parameter [Formula: see text], will drive equilibrium vaccination coverage levels above the Nash equilibrium, and in fact arbitrarily close to the societal optimum. These results were obtained both through mathematical analysis and numerical simulations.
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Affiliation(s)
- Ying Xin
- Department of Mathematics, Ohio University, Athens, OH, 45701, USA.,Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - David Gerberry
- Department of Mathematics, Xavier University, Cincinnati, OH, 45207, USA
| | - Winfried Just
- Department of Mathematics, Ohio University, Athens, OH, 45701, USA. .,Quantitative Biology Institute, Ohio University, Athens, OH, 45701, USA.
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17
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Sharma A, Menon SN, Sasidevan V, Sinha S. Epidemic prevalence information on social networks can mediate emergent collective outcomes in voluntary vaccine schemes. PLoS Comput Biol 2019; 15:e1006977. [PMID: 31120877 PMCID: PMC6532839 DOI: 10.1371/journal.pcbi.1006977] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 03/25/2019] [Indexed: 11/19/2022] Open
Abstract
The effectiveness of a mass vaccination program can engender its own undoing if individuals choose to not get vaccinated believing that they are already protected by herd immunity. This would appear to be the optimal decision for an individual, based on a strategic appraisal of her costs and benefits, even though she would be vulnerable during subsequent outbreaks if the majority of the population argues in this manner. We investigate how voluntary vaccination can nevertheless emerge in a social network of rational agents, who make informed decisions whether to be vaccinated, integrated with a model of epidemic dynamics. The information available to each agent includes the prevalence of the disease in their local network neighborhood and/or globally in the population, as well as the fraction of their neighbors that are protected against the disease. Crucially, the payoffs governing the decision of agents vary with disease prevalence, resulting in the vaccine uptake behavior changing in response to contagion spreading. The collective behavior of the agents responding to local prevalence can lead to a significant reduction in the final epidemic size, particularly for less contagious diseases having low basic reproduction number [Formula: see text]. Near the epidemic threshold ([Formula: see text]) the use of local prevalence information can result in divergent responses in the final vaccine coverage. Our results suggest that heterogeneity in the risk perception resulting from the spatio-temporal evolution of an epidemic differentially affects agents' payoffs, which is a critical determinant of the success of voluntary vaccination schemes.
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Affiliation(s)
- Anupama Sharma
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India
| | - Shakti N. Menon
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India
| | - V. Sasidevan
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India
- Department of Physics, Cochin University of Science and Technology, Cochin, India
| | - Sitabhra Sinha
- The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, India
- * E-mail:
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18
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Bhattacharyya S, Vutha A, Bauch CT. The impact of rare but severe vaccine adverse events on behaviour-disease dynamics: a network model. Sci Rep 2019; 9:7164. [PMID: 31073195 PMCID: PMC6509123 DOI: 10.1038/s41598-019-43596-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 04/25/2019] [Indexed: 12/02/2022] Open
Abstract
The propagation of rumours about rare but severe adverse vaccination or infection events through social networks can strongly impact vaccination uptake. Here we model a coupled behaviour-disease system where individual risk perception regarding vaccines and infection are shaped by their personal experiences and the experiences of others. Information about vaccines and infection either propagates through the network or becomes available through globally available sources. Dynamics are studied on a range of network types. Individuals choose to vaccinate according to their personal perception of risk and information about infection prevalence. We study events ranging from common and mild, to severe and rare. We find that vaccine and infection adverse events have asymmetric impacts. Vaccine (but not infection) adverse events may significantly prolong the tail of an outbreak. Similarly, introducing a small risk of a vaccine adverse event may cause a steep decline in vaccine coverage, especially on scale-free networks. Global dissemination of information about infection prevalence boosts vaccine coverage more than local dissemination. Taken together, these findings highlight the dangers associated with vaccine rumour propagation through scale-free networks such as those exhibited by online social media, as well as the benefits of disseminating public health information through mass media.
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Affiliation(s)
- Samit Bhattacharyya
- Department of Mathematics, School of Natural Sciences, Shiv Nadar University, Greater Noida, India.
| | - Amit Vutha
- ICTS, Tata Institute for Fundamental Research, Bangalore, India.
| | - Chris T Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada.
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19
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To vaccinate or not to vaccinate: A comprehensive study of vaccination-subsidizing policies with multi-agent simulations and mean-field modeling. J Theor Biol 2019; 469:107-126. [DOI: 10.1016/j.jtbi.2019.02.013] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 02/20/2019] [Accepted: 02/22/2019] [Indexed: 01/24/2023]
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20
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Soltanolkottabi M, Ben-Arieh D, Wu CH. Modeling Behavioral Response to Vaccination Using Public Goods Game. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2019; 6:268-276. [PMID: 32391406 PMCID: PMC7176036 DOI: 10.1109/tcss.2019.2896227] [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: 01/24/2018] [Accepted: 01/14/2019] [Indexed: 06/11/2023]
Abstract
Epidemics of infectious disease can be traced back to the early days of mankind. Only in the last two centuries vaccination has become a viable strategy to prevent such epidemics. In addition to the clinical efficacy of this strategy, the behavior and public attitudes affect the success of vaccines. This paper describes modeling the efficacy of vaccination considering the cost and benefit of vaccination to individual players. The model is based on the public goods game and is presented as a spatial game on a lattice. Using this model, individuals can contribute to the public health by paying the cost of vaccination or choose to be protected by the public who is vaccinated rather than pay the cost and share the risk of vaccination. Thus, in this model individuals can choose to stay susceptible, can become infected, or choose to vaccinate once in each episode. This paper presents the behavioral changes of the population and the cost to the society as a function of the cost of vaccines, cost of being infected, and the "fear factor" created by the public media.
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Affiliation(s)
- Marzieh Soltanolkottabi
- Department of Industrial and Manufacturing Systems EngineeringKansas State UniversityManhattanKS66506USA
| | - David Ben-Arieh
- Department of Industrial and Manufacturing Systems EngineeringKansas State UniversityManhattanKS66506USA
| | - Chih-Hang Wu
- Department of Industrial and Manufacturing Systems EngineeringKansas State UniversityManhattanKS66506USA
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21
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Zhan XX, Liu C, Zhou G, Zhang ZK, Sun GQ, Zhu JJH, Jin Z. Coupling dynamics of epidemic spreading and information diffusion on complex networks. APPLIED MATHEMATICS AND COMPUTATION 2018; 332:437-448. [PMID: 32287501 PMCID: PMC7112333 DOI: 10.1016/j.amc.2018.03.050] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 03/06/2018] [Accepted: 03/11/2018] [Indexed: 05/20/2023]
Abstract
The interaction between disease and disease information on complex networks has facilitated an interdisciplinary research area. When a disease begins to spread in the population, the corresponding information would also be transmitted among individuals, which in turn influence the spreading pattern of the disease. In this paper, firstly, we analyze the propagation of two representative diseases (H7N9 and Dengue fever) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes. Secondly, inspired by empirical analyses, we propose a nonlinear model to further interpret the coupling effect based on the SIS (Susceptible-Infected-Susceptible) model. Both simulation results and theoretical analysis show that a high prevalence of epidemic will lead to a slow information decay, consequently resulting in a high infected level, which shall in turn prevent the epidemic spreading. Finally, further theoretical analysis demonstrates that a multi-outbreak phenomenon emerges via the effect of coupling dynamics, which finds good agreement with empirical results. This work may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.
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Affiliation(s)
- Xiu-Xiu Zhan
- Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Chuang Liu
- Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Ge Zhou
- Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Zi-Ke Zhang
- Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
- Institute of Automation, Shanghai Jiaotong University, Shanghai 200030, PR China
| | - Gui-Quan Sun
- Complex Systems Research Center, Shanxi University, Taiyuan 030006, PR China
| | - Jonathan J H Zhu
- Web Mining Lab, Department of Media and Communication, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region, PR China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Taiyuan 030006, PR China
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22
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Zhan XX, Liu C, Sun GQ, Zhang ZK. Epidemic dynamics on information-driven adaptive networks. CHAOS, SOLITONS, AND FRACTALS 2018; 108:196-204. [PMID: 32288352 PMCID: PMC7126912 DOI: 10.1016/j.chaos.2018.02.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 01/19/2018] [Accepted: 02/08/2018] [Indexed: 05/05/2023]
Abstract
Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In this work, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice as well as on a real-world network give visual representations about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, a continuous dynamic behavior, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects human activities on responding to epidemic spreading.
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Affiliation(s)
- Xiu-Xiu Zhan
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft 2628 CD, The Netherlands
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
| | - Gui-Quan Sun
- Complex Sciences Center, Shanxi University, Taiyuan 030006, PR China
| | - Zi-Ke Zhang
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China
- Alibaba Research Institute, Hangzhou 311121, PR China
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23
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Social Dilemma Analysis of the Spread of Infectious Disease. EVOLUTIONARY GAMES WITH SOCIOPHYSICS 2018. [PMCID: PMC7124076 DOI: 10.1007/978-981-13-2769-8_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Understanding and controlling the spread of infectious disease is a pressing issue for our society. Contemporary globally connected civilization is more at risk from various modern infectious diseases than classical ones such as pests, cholera, and tuberculosis. Over the last few years, pandemic outbreaks of highly virulent influenza, possibly related to avian flu, severe acute respiratory syndrome (SARS), and middle-eastern respiratory syndrome coronavirus (MARSE) have been a threat. Beyond this, the intentional spread of infectious disease, e.g., “bioterrorism”, has come to be recognized as being just as dangerous as nuclear weapons. An infectious disease spreads on human social networks. Each individual can protect himself through several measures. Pre-emptive vaccination is thought to be most effective, although it incurs a partial cost to each individual. This brings about a social dilemma, because an individual may be able to rely on so-called “herd immunity” to avoid his own infection without himself being vaccinated. Also, besides vaccination, there may be several practical ways to protect against contagion, such as wearing a mask, keeping away from crowds, and self-isolation by leaving the home less often, which may be less costly and less effective than vaccination. In any case, there is a human-decision-making process regarding what steps should be taken, while the dynamics of infectious-disease spread can themselves be evaluated as a diffusion problem that has been well-studied in physics for many years. Thus, based on the concept of human–environment–social interaction, a basic-physics model for this diffusion problem that considers evolutionary game theory (EGT) may lead us to obtain some meaningful solutions that can be proposed to our society. Following the previous chapter explaining how EGT can be applied to traffic-flow analysis, this chapter describes this practical problem.
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24
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Shi B, Wang W, Qiu H, Chen YW, Peng S. Exploring Voluntary Vaccinating Behaviors using Evolutionary N-person Threshold Games. Sci Rep 2017; 7:16355. [PMID: 29180687 PMCID: PMC5704005 DOI: 10.1038/s41598-017-16680-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 11/16/2017] [Indexed: 01/13/2023] Open
Abstract
Understanding individuals' voluntary vaccinating behaviors plays essential roles in making vaccination policies for many vaccinepreventable diseases. Usually, individuals decide whether to vaccinate through evaluating the relative cost of vaccination and infection according to their own interests. Mounting evidence shows that the best vaccine coverage level for the population as a whole can hardly be achieved due to the effects of herd immunity. In this paper, taking into consideration the herd immunity threshold, we present an evolutionary N-person threshold game, where individuals can dynamically adjust their vaccinating strategies and their payoffs depend nonlinearly on whether or not the herd immunity threshold is reached. First, in well-mixed populations, we analyze the relationships at equilibrium among the fraction of vaccinated individuals, the population size, the basic reproduction number and the relative cost of vaccination and infection. Then, we carry out simulations on four types of complex networks to explore the evolutionary dynamics of the N-person threshold game in structured populations. Specifically, we investigate the effects of disease severity and population structure on the vaccine coverage for different relative costs of vaccination and infection. The results and findings can offer new insight into designing incentive-based vaccination policies for disease intervention and control.
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Affiliation(s)
- Benyun Shi
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Weihao Wang
- School of Information Engineering, Nanjing University of Finance & Economics, Nanjing, 210003, China
| | - Hongjun Qiu
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Yu-Wang Chen
- Decision and Cognitive Sciences Research Centre, The University of Manchester, Manchester, M13 9SS, UK
| | - Shaoliang Peng
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Computer Science, National University of Defense Technology, Changsha, 410073, China
- College of Computer Science and Electronic Engineering & National Supercomputer Centre in Changsha, Hunan University, Changsha, 410082, China
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25
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26
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Shi B, Qiu H, Niu W, Ren Y, Ding H, Chen D. Voluntary Vaccination through Self-organizing Behaviors on Locally-mixed Social Networks. Sci Rep 2017; 7:2665. [PMID: 28572623 PMCID: PMC5453996 DOI: 10.1038/s41598-017-02967-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 04/20/2017] [Indexed: 01/13/2023] Open
Abstract
Voluntary vaccination reflects how individuals weigh the risk of infection and the cost of vaccination against the spread of vaccine-preventable diseases, such as smallpox and measles. In a homogeneously mixing population, the infection risk of an individual depends largely on the proportion of vaccinated individuals due to the effects of herd immunity. While in a structured population, the infection risk can also be affected by the structure of individuals' social network. In this paper, we focus on studying individuals' self-organizing behaviors under the circumstance of voluntary vaccination in different types of social networks. Specifically, we assume that each individual together with his/her neighbors forms a local well-mixed environment, where individuals meet equally often as long as they have a common neighbor. We carry out simulations on four types of locally-mixed social networks to investigate the network effects on voluntary vaccination. Furthermore, we also evaluate individuals' vaccinating decisions through interacting with their "neighbors of neighbors". The results and findings of this paper provide a new perspective for vaccination policy-making by taking into consideration human responses in complex social networks.
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Affiliation(s)
- Benyun Shi
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, 310018, China.
| | - Hongjun Qiu
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, 310018, China
| | - Wenfang Niu
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, 310018, China
| | - Yizhi Ren
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, 310018, China.
| | - Hong Ding
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, 310018, China
| | - Dan Chen
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
- School of Computer, Wuhan University, Wuhan, 430072, China
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27
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Kan JQ, Zhang HF. Effects of awareness diffusion and self-initiated awareness behavior on epidemic spreading - An approach based on multiplex networks. COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION 2017; 44:193-203. [PMID: 32288421 PMCID: PMC7128930 DOI: 10.1016/j.cnsns.2016.08.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2015] [Revised: 05/24/2015] [Accepted: 08/11/2016] [Indexed: 05/03/2023]
Abstract
In this paper, we study the interplay between the epidemic spreading and the diffusion of awareness in multiplex networks. In the model, an infectious disease can spread in one network representing the paths of epidemic spreading (contact network), leading to the diffusion of awareness in the other network (information network), and then the diffusion of awareness will cause individuals to take social distances, which in turn affects the epidemic spreading. As for the diffusion of awareness, we assume that, on the one hand, individuals can be informed by other aware neighbors in information network, on the other hand, the susceptible individuals can be self-awareness induced by the infected neighbors in the contact networks (local information) or mass media (global information). Through Markov chain approach and numerical computations, we find that the density of infected individuals and the epidemic threshold can be affected by the structures of the two networks and the effective transmission rate of the awareness. However, we prove that though the introduction of the self-awareness can lower the density of infection, which cannot increase the epidemic threshold no matter of the local information or global information. Our finding is remarkably different to many previous results on single-layer network: local information based behavioral response can alter the epidemic threshold. Furthermore, our results indicate that the nodes with more neighbors (hub nodes) in information networks are easier to be informed, as a result, their risk of infection in contact networks can be effectively reduced.
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Affiliation(s)
- Jia-Qian Kan
- School of Mathematical Science, Anhui University, Hefei 230601, PR China
| | - Hai-Feng Zhang
- School of Mathematical Science, Anhui University, Hefei 230601, PR China
- Research centre of information supply & assurance, Anhui University, Hefei 230601, PR China
- Department of Communication Engineering, North University of China, Taiyuan, Shan’xi 030051, PR China
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28
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Verelst F, Willem L, Beutels P. Behavioural change models for infectious disease transmission: a systematic review (2010-2015). J R Soc Interface 2016; 13:20160820. [PMID: 28003528 PMCID: PMC5221530 DOI: 10.1098/rsif.2016.0820] [Citation(s) in RCA: 169] [Impact Index Per Article: 21.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 11/25/2016] [Indexed: 12/13/2022] Open
Abstract
We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010-2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.
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Affiliation(s)
- Frederik Verelst
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, New South Wales, Australia
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29
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Liu QH, Wang W, Tang M, Zhang HF. Impacts of complex behavioral responses on asymmetric interacting spreading dynamics in multiplex networks. Sci Rep 2016; 6:25617. [PMID: 27156574 PMCID: PMC4860576 DOI: 10.1038/srep25617] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 04/20/2016] [Indexed: 11/29/2022] Open
Abstract
Information diffusion and disease spreading in communication-contact layered network are typically asymmetrically coupled with each other, in which disease spreading can be significantly affected by the way an individual being aware of disease responds to the disease. Many recent studies have demonstrated that human behavioral adoption is a complex and non-Markovian process, where the probability of behavior adoption is dependent on the cumulative times of information received and the social reinforcement effect of the cumulative information. In this paper, the impacts of such a non-Markovian vaccination adoption behavior on the epidemic dynamics and the control effects are explored. It is found that this complex adoption behavior in the communication layer can significantly enhance the epidemic threshold and reduce the final infection rate. By defining the social cost as the total cost of vaccination and treatment, it can be seen that there exists an optimal social reinforcement effect and optimal information transmission rate allowing the minimal social cost. Moreover, a mean-field theory is developed to verify the correctness of simulation results.
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Affiliation(s)
- Quan-Hui Liu
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Wang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ming Tang
- Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hai-Feng Zhang
- School of Mathematical Science, Anhui University, Hefei 230039, China
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30
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Han D, Sun M. An evolutionary vaccination game in the modified activity driven network by considering the closeness. PHYSICA A 2016; 443:49-57. [PMID: 32288095 PMCID: PMC7134395 DOI: 10.1016/j.physa.2015.09.073] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Revised: 09/07/2015] [Indexed: 06/11/2023]
Abstract
In this paper, we explore an evolutionary vaccination game in the modified activity driven network by considering the closeness. We set a closeness parameter p which is used to describe the way of connection between two individuals. The simulation results show that the closeness p may have an active role in weakening both the spreading of epidemic and the vaccination. Besides, when vaccination is not allowed, the final recovered density increases with the value of the ratio of the infection rate to the recovery rate λ / μ . However, when vaccination is allowed the final density of recovered individual first increases and then decreases with the value of λ / μ . Two variables are designed to identify the relation between the individuals' activities and their states. The results draw that both recovered and vaccinated frequency increase with the increase of the individuals' activities. Meanwhile, the immune fee has less impact on the individuals' vaccination than the closeness. While the λ / μ is in a certain range, with the increase of the value of λ / μ , the recovered frequency of the whole crowds reduces. Our results, therefore, reveal the fact that the best of intentions may lead to backfire.
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Affiliation(s)
- Dun Han
- Nonlinear Scientific Research Center, Jiangsu University, Zhenjiang, Jiangsu, PR China
- Levich Institute and Physics Department, City College of New York, New York, NY 10031, USA
| | - Mei Sun
- Nonlinear Scientific Research Center, Jiangsu University, Zhenjiang, Jiangsu, PR China
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31
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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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Dong C, Yin Q, Liu W, Yan Z, Shi T. Can rewiring strategy control the epidemic spreading? PHYSICA A 2015; 438:169-177. [PMID: 32288093 PMCID: PMC7126863 DOI: 10.1016/j.physa.2015.06.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Revised: 05/19/2015] [Indexed: 06/01/2023]
Abstract
Relation existed in the social contact network can affect individuals' behaviors greatly. Considering the diversity of relation intimacy among network nodes, an epidemic propagation model is proposed by incorporating the link-breaking threshold, which is normally neglected in the rewiring strategy. The impact of rewiring strategy on the epidemic spreading in the weighted adaptive network is explored. The results show that the rewiring strategy cannot always control the epidemic prevalence, especially when the link-breaking threshold is low. Meanwhile, as well as strong links, weak links also play a significant role on epidemic spreading.
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Affiliation(s)
| | | | | | - Zhijun Yan
- Corresponding author. Tel.: +86 10 68912845.
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