About: The challenges of modeling and forecasting the spread of COVID-19

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	The challenges of modeling and forecasting the spread of COVID-19
Creator	Mohler, George Short, Martin Sledge, Daniel Bertozzi, Andrea Franco, Elisa Topaz, Chad Schneider, Tapio Witelski, Thomas
Source	Medline; PMC
abstract	The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
has issue date	2020-07-21(xsd:dateTime)
bibo:doi	10.1073/pnas.2006520117
bibo:pmid	32616574
has license	cc-by
sha1sum (hex)	2d1321fa944eca49a3effd56418dbfe6a428d2f0
schema:url	https://doi.org/10.1073/pnas.2006520117
resource representing a document's title	The challenges of modeling and forecasting the spread of COVID-19
has PubMed Central identifier	PMC7382213
has PubMed identifier	32616574
schema:publication	Proc Natl Acad Sci U S A
resource representing a document's body	covid:2d1321fa944eca49a3effd56418dbfe6a428d2f0#body_text
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