ArticleScientific reports2016
The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern.PLoS neglected tropical diseases · 2019Pooled it
- Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Impact of high-order time-delayed information on epidemic propagation in multiplex networks.Infectious Disease Modelling · 2026Article
- Social, mobility and contact networks in shaping health behaviours and infectious disease dynamics: a scoping review.Infectious diseases of poverty · 2025Article
- Impact of information dissemination and behavioural responses on epidemic dynamics: A multi-layer network analysis.Infectious Disease Modelling · 2025Article
- Quasispecies theory and emerging viruses: challenges and applications.Npj viruses · 2024Review
- The coevolution of the spread of a disease and competing opinions in multiplex networks.Chaos, solitons, and fractals · 2023Article
- Influence of Information Blocking on the Spread of Virus in Multilayer Networks.Entropy (Basel, Switzerland) · 2023Article
- Modeling the effect of observational social learning on parental decision-making for childhood vaccination and diseases spread over household networks.Frontiers in epidemiology · 2023Article
- Using awareness to Z-control a SEIR model with overexposure: Insights on Covid-19 pandemic.Chaos, solitons, and fractals · 2021Article
- Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection.Proceedings. Biological sciences · 2021Article
- A review and agenda for integrated disease models including social and behavioural factors.Nature human behaviour · 2021Review
- The Dynamical Interplay of Collective Attention, Awareness and Epidemics Spreading in the Multiplex Social Networks During COVID-19.IEEE access : practical innovations, open solutions · 2020Article
- Investigation of epidemic spreading process on multiplex networks by incorporating fatal properties.Applied mathematics and computation · 2019Article
- Social dynamics modeling of chrono-nutrition.PLoS computational biology · 2019Article
- Quantifying the propagation of distress and mental disorders in social networks.Scientific reports · 2018Article
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Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.