Open Access
Issue |
Natl Sci Open
Volume 2, Number 6, 2023
|
|
---|---|---|
Article Number | 20220051 | |
Number of page(s) | 13 | |
Section | Information Sciences | |
DOI | https://doi.org/10.1360/nso/20220051 | |
Published online | 31 March 2023 |
- Ferguson N. Capturing human behaviour. Nature 2007; 446: 733.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Funk S, Salathé M, Jansen VAA. Modelling the influence of human behaviour on the spread of infectious diseases: a review. J R Soc Interface 2010; 7: 1247–1256.[Article] [CrossRef] [PubMed] [Google Scholar]
- Funk S, Gilad E, Watkins C, et al. The spread of awareness and its impact on epidemic outbreaks. Proc Natl Acad Sci USA 2009; 106: 6872–6877.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Wang W, Liu QH, Liang J, et al. Coevolution spreading in complex networks. Phys Rep 2019; 820: 1–51.[Article] [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Wu Q, Fu X, Small M, et al. The impact of awareness on epidemic spreading in networks. Chaos 2012; 22: 013101.[Article] [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Li K, Li C, Xiang Y, et al. Policy and newly confirmed cases universally shape the human mobility during COVID-19. Sci Open 2022; 1: 20220003.[Article] [Google Scholar]
- Granell C, Gómez S, Arenas A. Dynamical interplay between awareness and epidemic spreading in multiplex networks. Phys Rev Lett 2013; 111: 128701.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Granell C, Gómez S, Arenas A. Competing spreading processes on multiplex networks: Awareness and epidemics. Phys Rev E 2014; 90: 012808.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- de Arruda GF, Rodrigues FA, Moreno Y. Fundamentals of spreading processes in single and multilayer complex networks. Phys Rep 2018; 756: 1–59[Article] [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
- Sánchez-García RJ, Cozzo E, Moreno Y. Dimensionality reduction and spectral properties of multilayer networks. Phys Rev E 2014; 89: 052815.[Article] [CrossRef] [PubMed] [Google Scholar]
- Sun M, Small M, Lee SS, et al. An exploration and simulation of epidemic spread and its control in multiplex networks. SIAM J Appl Math 2018; 78: 1602–1631.[Article] [CrossRef] [MathSciNet] [Google Scholar]
- Guo Q, Lei Y, Jiang X, et al. Epidemic spreading with activity-driven awareness diffusion on multiplex network. Chaos 2016; 26: 043110.[Article] [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Kan JQ, Zhang HF. Effects of awareness diffusion and self-initiated awareness behavior on epidemic spreading - An approach based on multiplex networks. Commun Nonlinear Sci Numer Simul 2017; 44: 193–203.[Article] [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Ye Y, Zhang Q, Ruan Z, et al. Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission. Phys Rev E 2020; 102: 042314.[Article] [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Velásquez-Rojas F, Ventura PC, Connaughton C, et al. Disease and information spreading at different speeds in multiplex networks. Phys Rev E 2020; 102: 022312.[Article] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Gray A, Greenhalgh D, Hu L, et al. A stochastic differential equation sis epidemic model. SIAM J Appl Math 2011; 71: 876–902.[Article] [CrossRef] [MathSciNet] [Google Scholar]
- McCluskey CC. Complete global stability for an SIR epidemic model with delay—Distributed or discrete. Nonlinear Anal-Real World Appl 2010; 11: 55–59.[Article] [CrossRef] [MathSciNet] [Google Scholar]
- Kabir KMA, Tanimoto J. Analysis of epidemic outbreaks in two-layer networks with different structures for information spreading and disease diffusion. Commun Nonlinear Sci Numer Simul 2019; 72: 565–574.[Article] [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
- Bagnoli F, Lió P, Sguanci L. Risk perception in epidemic modeling. Phys Rev E 2007; 76: 061904.[Article] [CrossRef] [PubMed] [Google Scholar]
- Lazer DMJ, Baum MA, Benkler Y, et al. The science of fake news. Science 2018; 359: 1094–1096.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Iyengar S, Massey DS. Scientific communication in a post-truth society. Proc Natl Acad Sci USA 2019; 116: 7656–7661.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Scheufele DA, Krause NM. Science audiences, misinformation, and fake news. Proc Natl Acad Sci USA 2019; 116: 7662–7669.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Zhu JH. Issue competition and attention distraction: A zero-sum theory of agenda-setting. Jism Q 1992; 69: 825–836.[Article] [Google Scholar]
- Kuehne LM, Olden JD. Lay summaries needed to enhance science communication. Proc Natl Acad Sci USA 2015; 112: 3585–3586.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Fahy D, Nisbet MC. The science journalist online: Shifting roles and emerging practices. Journalism 2011; 12: 778–793.[Article] [CrossRef] [Google Scholar]
- Brauer F, Castillo-Chavez C, Castillo-Chavez C. Mathematical Models in Population Biology and Epidemiology. New York: Springer, 2012 [CrossRef] [Google Scholar]
- Bertozzi AL, Franco E, Mohler G, et al. The challenges of modeling and forecasting the spread of COVID-19. Proc Natl Acad Sci USA 2020; 117: 16732–16738.[Article] [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- Li Q, Chen H, Li Y, et al. Network spreading among areas: A dynamical complex network modeling approach. Chaos 2022; 32: 103102.[Article] [CrossRef] [PubMed] [Google Scholar]
- Thurner S, Klimek P, Hanel R. A network-based explanation of why most COVID-19 infection curves are linear. Proc Natl Acad Sci USA 2020; 117: 22684–22689.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Myers SA, Sharma A, Gupta P, Lin J. Information network or social network? the structure of the twitter follow graph. In: Proceedings of the 23rd International Conference on World Wide Web, WWW ’14 Companion (Association for Computing Machinery, New York, NY, USA), p. 493-498 (2014).[Article] [Google Scholar]
- Hofman JM, Sharma A, Watts DJ. Prediction and explanation in social systems. Science 2017; 355: 486–488.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Small M, Cavanagh D. Modelling strong control measures for epidemic propagation with networks—A COVID-19 case study. IEEE Access 2020; 8: 109719.[Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.