Dynamical evolution of social network polarization and its impact on the propagation of a virus
- URL: http://arxiv.org/abs/2406.08299v1
- Date: Wed, 12 Jun 2024 15:00:05 GMT
- Title: Dynamical evolution of social network polarization and its impact on the propagation of a virus
- Authors: Ixandra Achitouv, David Chavalarias,
- Abstract summary: We analyse the dynamical polarization within a social network as well as the network properties before and after a vaccine was made available.
We simulate the propagation of a virus in a polarized society by assigning vaccines to pro-vaccine individuals and none to the anti-vaccine individuals.
In polarized networks, we observe a significantly more widespread diffusion of the virus, highlighting the importance of considering polarization for epidemic forecasting.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The COVID-19 pandemic that emerged in 2020 has highlighted the complex interplay between vaccine hesitancy and societal polarization. In this study, we analyse the dynamical polarization within a social network as well as the network properties before and after a vaccine was made available. Our results show that as the network evolves from a less structured state to one with more clustered communities. Then using an agent-based modeling approach, we simulate the propagation of a virus in a polarized society by assigning vaccines to pro-vaccine individuals and none to the anti-vaccine individuals. We compare this propagation to the case where the same number of vaccines is distributed homogeneously across the population. In polarized networks, we observe a significantly more widespread diffusion of the virus, highlighting the importance of considering polarization for epidemic forecasting.
Related papers
- Modeling the amplification of epidemic spread by misinformed populations [43.389128992784045]
We propose an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties.
Our model allows us to simulate and estimate various scenarios to understand the impact of misinformation on epidemic spreading.
Using this model, we estimate that misinformation could have led to 47 million additional COVID-19 infections in the U.S. in a worst-case scenario.
arXiv Detail & Related papers (2024-02-17T18:01:43Z) - Tracking the Structure and Sentiment of Vaccination Discussions on
Mumsnet [3.192308005611312]
Vaccination is one of the top 10 threats to global health in 2019 by the World Health Organization.
Online social media has been identified as a breeding ground for anti-vaccination discussions.
arXiv Detail & Related papers (2023-08-24T18:28:35Z) - Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance
of Containment Measures [65.62256987706128]
Compartmental epidemiological models categorize individuals based on their disease status.
We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens.
We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
arXiv Detail & Related papers (2023-07-28T08:01:05Z) - An agent-based epidemics simulation to compare and explain screening and
vaccination prioritisation strategies [0.0]
This paper describes an agent-based model of epidemics dynamics.
Its goal is not to predict the evolution of the epidemics, but to explain the underlying mechanisms in an interactive way.
The model is implemented in Netlogo in different simulators, published online to let people experiment with them.
arXiv Detail & Related papers (2022-10-24T10:15:07Z) - VacciNet: Towards a Smart Framework for Learning the Distribution Chain
Optimization of Vaccines for a Pandemic [0.0]
We put forward a novel framework leveraging Supervised Learning and Reinforcement Learning (RL) which we call VacciNet.
RL is capable of learning to predict the demand of vaccination in a state of a country as well as suggest optimal vaccine allocation in the state for minimum cost of procurement and supply.
arXiv Detail & Related papers (2022-08-01T19:37:33Z) - A feasibility study proposal of the predictive model to enable the
prediction of population susceptibility to COVID-19 by analysis of vaccine
utilization for advising deployment of a booster dose [0.0]
SARS-CoV-2 strain of B1.1.529 or Omicron spreading around the globe.
Concerns that it will not end soon and that it will be a race against time until a more contagious and virulent variant emerges.
One of the most promising approaches for preventing virus propagation is to maintain continuous high vaccination efficacy.
arXiv Detail & Related papers (2022-04-25T16:05:59Z) - Strategic COVID-19 vaccine distribution can simultaneously elevate
social utility and equity [22.800692128612983]
Social utility and equity can be simultaneously improved when vaccine access is prioritized for the most disadvantaged communities.
We design two behavior-and-demography-aware indices, community risk and societal harm, which capture the risks communities face and those they impose on society from not being vaccinated.
arXiv Detail & Related papers (2021-11-12T12:31:11Z) - A k-mer Based Approach for SARS-CoV-2 Variant Identification [55.78588835407174]
We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance.
We also show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC)
arXiv Detail & Related papers (2021-08-07T15:08:15Z) - Understanding the temporal evolution of COVID-19 research through
machine learning and natural language processing [66.63200823918429]
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world.
We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research.
Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
arXiv Detail & Related papers (2020-07-22T18:02:39Z) - Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries.
Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
arXiv Detail & Related papers (2020-06-05T02:04:25Z) - Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter [65.7192861893042]
We examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant.
We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers"
At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers.
arXiv Detail & Related papers (2020-03-26T13:55:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.