About: Modeling the dynamics of COVID19 spread during and after social distancing

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Modeling the dynamics of COVID19 spread during and after social distancing
Creator	Komarova, Natalia Wodarz, Dominik
source	MedRxiv
abstract	Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of COVID19. In the United States, strict social distancing has resulted in different types infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. While these plateau dynamics cannot be readily reproduced with standard SIR infection models, we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a degree of 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection %22corridors%22, resulting in plateau dynamics. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a potential second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.
has issue date	2020-06-16(xsd:dateTime)
bibo:doi	10.1101/2020.06.13.20130625
has license	medrxiv
sha1sum (hex)	54ffb8debe9f5ddb1e20c4fc649e87e94cfb2788
schema:url	https://doi.org/10.1101/2020.06.13.20130625
resource representing a document's title	Modeling the dynamics of COVID19 spread during and after social distancing
resource representing a document's body	covid:54ffb8debe9f5ddb1e20c4fc649e87e94cfb2788#body_text
is schema:about of	named entity 'reproduce' named entity 'potential' named entity 'quadratically' named entity 'observed' named entity 'DEGREE' »more»

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