About: Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19 Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19
Creator	Maslov, Sergei Goldenfeld, Nigel Wong, George Weiner, Zachary Elbanna, Ahmed Tkachenko, Alexei
Source	MedRxiv
abstract	It has become increasingly clear that the COVID-19 epidemic is characterized by overdispersion whereby the majority of the transmission is driven by a minority of infected individuals. Such a strong departure from the homogeneity assumptions of traditional well-mixed compartment model is usually hypothesized to be the result of short-term super-spreader events, such as individual's extreme rate of virus shedding at the peak of infectivity while attending a large gathering without appropriate mitigation. However, heterogeneity can also arise through long-term, or persistent variations in individual susceptibility or infectivity. Here, we show how to incorporate persistent heterogeneity into a wide class of epidemiological models, and derive a non-linear dependence of the effective reproduction number R_e on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of overdispersion: (1) It results in a major modification of the early epidemic dynamics; (2) It significantly suppresses the herd immunity threshold; (3) It significantly reduces the final size of the epidemic. We estimate social and biological contributions to persistent heterogeneity using data on real-life face-to-face contact networks and age variation of the incidence rate during the COVID-19 epidemic, and show that empirical data from the COVID-19 epidemic in New York City (NYC) and Chicago and all 50 US states provide a consistent characterization of the level of persistent heterogeneity. Our estimates suggest that the hardest-hit areas, such as NYC, are close to the persistent heterogeneity herd immunity threshold following the first wave of the epidemic, thereby limiting the spread of infection to other regions during a potential second wave of the epidemic. Our work implies that general considerations of persistent heterogeneity in addition to overdispersion act to limit the scale of pandemics.
has issue date	2020-07-29(xsd:dateTime)
bibo:doi	10.1101/2020.07.26.20162420
has license	medrxiv
sha1sum (hex)	f94deaf9e881408afd238a91931d7079bd4626e9
schema:url	https://doi.org/10.1101/2020.07.26.20162420
resource representing a document's title	Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19
resource representing a document's body	covid:f94deaf9e881408afd238a91931d7079bd4626e9#body_text
is schema:about of	named entity 'events' named entity 'models' named entity 'persistent' named entity 'pandemics' named entity 'individual' named entity 'data' named entity 'incidence' named entity 'epidemic' named entity 'extreme' named entity 'close' named entity 'early' named entity 'overdispersion' named entity 'NETWORKS' named entity 'HERD IMMUNITY' named entity 'CLASS' named entity 'SCALE' named entity 'APPROPRIATE' named entity 'NEW YORK CITY' named entity 'VIRUS SHEDDING' named entity '27S' named entity 'USING' named entity 'COVID-19' named entity 'major' named entity 'contact' named entity 'size' named entity 'Our' named entity 'empirical data' named entity 'face-to-face' named entity 'epidemic' named entity 'overdispersion' named entity 'New York City' named entity 'super-spreader' named entity 'biological' named entity 'individual' named entity 'persistent' named entity 'mitigation' named entity 'epidemic' named entity 'herd immunity' named entity 'infection' named entity 'herd immunity' named entity 'non-linear' named entity 'empirical data' named entity 'epidemiological models' named entity 'epidemic' named entity 'herd immunity' named entity 'heterogeneity' named entity 'overdispersion' named entity 'COVID-19' named entity 'Black Death' named entity 'herd immunity' named entity 'regions of Italy' named entity 'subpopulation' named entity 'COVID-19' named entity 'reproduction number' named entity 'COVID-19' named entity 'COVID-19' named entity 'herd immunity' named entity 'infectivity' named entity 'contact tracing' named entity 'non-linear' named entity 'social activity' named entity 'herd immunity' named entity 'epidemic' named entity 'COVID-19' named entity 'population dynamics' named entity 'Austria' named entity 'long-term' named entity 'NYC' named entity 'power law' named entity 'herd immunity' named entity 'infection' named entity 'epidemic' named entity 'gamma-distributed' named entity 'COVID-19' named entity 'immune response' named entity 'herd immunity' named entity 'infectivity' named entity 'skewness' named entity 'June 1' named entity 'overdispersion'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software