About: The role of directionality, heterogeneity and correlations in epidemic risk and spread

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	The role of directionality, heterogeneity and correlations in epidemic risk and spread
Creator	Scarpino, Samuel Allard, Antoine Althouse, Benjamin Hébert-Dufresne, Laurent Moore, Cristopher
Source	ArXiv
abstract	Most models of epidemic spread, including many designed specifically for COVID-19, implicitly assume that social networks are undirected, i.e., that the infection is equally likely to spread in either direction whenever a contact occurs. In particular, this assumption implies that the individuals most likely to spread the disease are also the most likely to receive it from others. Here, we review results from the theory of random directed graphs which show that many important quantities, including the reproductive number and the epidemic size, depend sensitively on the joint distribution of in- and out-degrees (%22risk%22and%22spread%22), including their heterogeneity and the correlation between them. By considering joint distributions of various kinds we elucidate why some types of heterogeneity cause a deviation from the standard Kermack-McKendrick analysis of SIR models, i.e., so called mass-action models where contacts are homogeneous and random, and some do not. We also show that some structured SIR models informed by complex contact patterns among types of individuals (age or activity) are simply mixtures of Poisson processes and tend not to deviate significantly from the simplest mass-action model. Finally, we point out some possible policy implications of this directed structure, both for contact tracing strategy and for interventions designed to prevent superspreading events. In particular, directed networks have a forward and backward version of the classic%22friendship paradox%22-- forward links tend to lead to individuals with high risk, while backward links lead to individuals with high spread -- such that a combination of both forward and backward contact tracing is necessary to find superspreading events and prevent future cascades of infection.
has issue date	2020-05-22(xsd:dateTime)
has license	arxiv
sha1sum (hex)	2e0a4cf46393e64475eadb01d12c0449539c0b5c
resource representing a document's title	The role of directionality, heterogeneity and correlations in epidemic risk and spread
resource representing a document's body	covid:2e0a4cf46393e64475eadb01d12c0449539c0b5c#body_text
is schema:about of	named entity 'FORWARD' named entity 'TO PREVENT' named entity 'SOCIAL NETWORKS' named entity 'FUTURE' named entity 'RECEIVE' named entity 'DISEASE' named entity 'WHY' named entity 'MODEL' named entity 'DIRECTED' named entity 'IMPORTANT' named entity 'THEIR' named entity 'REPRODUCTIVE' named entity 'RESULTS' named entity 'COVID-19' named entity 'correlation' named entity 'reproductive number' named entity 'contact tracing' named entity 'infection' named entity 'infection' named entity 'heterogeneity' named entity 'heterogeneity' named entity 'superspreading events' named entity 'in-degree' named entity 'out-degree' named entity 'probability' named entity 'number of new cases' named entity 'degree distribution' named entity 'epidemic' named entity 'epidemic' named entity 'in-degree' named entity 'macroscopic' named entity 'out-degree' named entity 'joint distribution' named entity 'emerging infectious diseases' named entity 'social network' named entity 'in-degree' named entity 'infection' named entity 'probability' named entity 'heavy-tailed' named entity 'correlation' named entity 'generating function' named entity 'epidemic' named entity 'directed graph' named entity 'degree distribution' named entity 'friendship paradox' named entity 'secondary contacts' named entity 'transmission rate' named entity 'infection' named entity 'friendship paradox' named entity 'infection' named entity 'variance' named entity 'out-degree' named entity 'probability' named entity 'epidemics' named entity 'general solution' named entity 'macroscopic' named entity 'probability' named entity 'out-degree' named entity 'parametrization' named entity 'high-risk' named entity 'correlation' named entity 'directed graphs' named entity 'self-loops' named entity 'weighted average' named entity 'probability' named entity 'epidemic' named entity 'probability' named entity 'epidemic' named entity 'viral load' named entity 'SEIR' named entity 'degree distribution' named entity 'epidemic' named entity 'epidemic' named entity 'graph' named entity 'degree distribution' named entity 'Poisson' named entity 'degree distribution' named entity 'epidemic' named entity 'superspreading events' named entity 'secondary contacts' named entity 'prophylactics' named entity 'contact tracing' named entity 'transmission rate'

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