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Highlighting the impact of social relationships on the propagation of respiratory viruses using percolation theory.

ABSTRACT: We develop a site-bond percolation model, called PERCOVID, in order to describe the time evolution of all epidemics propagating through respiratory tract or by skin contacts in human populations. This model is based on a network of social relationships representing interconnected households experiencing governmental non-pharmaceutical interventions. As a very first testing ground, we apply our model to the understanding of the dynamics of the COVID-19 pandemic in France from December 2019 up to December 2021. Our model shows the impact of lockdowns and curfews, as well as the influence of the progressive vaccination campaign in order to keep COVID-19 pandemic under the percolation threshold. We illustrate the role played by social interactions by comparing two typical scenarios with low or high strengths of social relationships as compared to France during the first wave in March 2020. We investigate finally the role played by the α and δ variants in the evolution of the epidemic in France till autumn 2021, paying particular attention to the essential role played by the vaccination. Our model predicts that the rise of the epidemic observed in July and August 2021 would not result in a new major epidemic wave in France.

SUBMITTER: Mathiot JF

PROVIDER: S-EPMC8692486 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Abstract Alzheimer’s disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Identification of brain functional biomarkers is expected to help determine preclinical stages for targeted mechanistic studies and development of therapeutic interventions to deter disease progression. Connectomic analysis, a graph theory–based methodology used in the analysis of brain-derived connectivity matrices was used in conjunction with percolation theory targeted attack model to investigate the network effects of AD-related amyloid deposition. We used matrices derived from resting-state functional magnetic resonance imaging collected on mice with extracellular amyloidosis (TgCRND8 mice, n = 17) and control littermates (n = 17). Global, nodal, spatial, and percolation-based analysis was performed comparing AD and control mice. These data indicate a short-term compensatory response to neurodegeneration in the AD brain via a strongly connected core network with highly vulnerable or disconnected hubs. Targeted attacks demonstrated a greater vulnerability of AD brains to all types of attacks and identified progression models to mimic AD brain functional connectivity through betweenness centrality and collective influence metrics. Furthermore, both spatial analysis and percolation theory identified a key disconnect between the anterior brain of the AD mice to the rest of the brain network. Author Summary Accurate biomarkers of Alzheimer’s disease (AD) are needed for early diagnosis and treatments. Connectomic analysis, a graph theory approach, coupled with percolation theory, a network attack approach, were applied here to analyze neuroimaging through a quantitative lens. We report a marker of AD vulnerability, which highlighted a core network disconnected from key hubs, notably within the anterior portion of the brain disconnected. Additionally, preliminary models using targeted attacks provide potential pathways of neurodegeneration from the control state to the diseased state. These findings show key differences in brain connectivity due to AD and provide a potential methodology for identifying biomarkers.

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Highlighting the impact of social relationships on the propagation of respiratory viruses using percolation theory.

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