About: A minimal and adaptive prediction strategy for critical resource planning in a pandemic

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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	A minimal and adaptive prediction strategy for critical resource planning in a pandemic
Creator	Prakash, Meher Ansumali, Santosh Bhattacharya, Soumyadeep Kaushal, Shaurya Kumar, Aloke Chandran, Akshay
Source	MedRxiv
abstract	Current epidemiological models can in principle model the temporal evolution of a pandemic. However, any such model will rely on parameters that are unknown, which in practice are estimated using stochastic and poorly measured quantities. As a result, an early prediction of the long-term evolution of a pandemic will quickly lose relevance, while a late model will be too late to be useful for disaster management. Unless a model is designed to be adaptive, it is bound either to lose relevance over time, or lose trust and thus not have a second-chance for retraining. We propose a strategy for estimating the number of infections and the number of deaths, that does away with time-series modeling, and instead makes use of a 'phase portrait approach'. We demonstrate that, with this approach, there is a universality to the evolution of the disease across countries, that can then be usedto make reliable predictions. These same models can also be used to plan the requirements for critical resources during the pandemic. The approach is designed for simplicity of interpretation, and adaptivity over time. Using our model, we predict the number of infections and deaths in Italy and New York State, based on an adaptive algorithm which uses early available data, and show that our predictions closely match the actual outcomes. We also carry out a similar exercise for India, where in addition to projecting the number of infections and deaths, we also project the expected range of critical resource requirements for hospitalizations in a location.
has issue date	2020-04-10(xsd:dateTime)
bibo:doi	10.1101/2020.04.08.20057414
has license	medrxiv
sha1sum (hex)	a9e6585dd4a06940cd7ca08ab2296378b62d0fcb
schema:url	https://doi.org/10.1101/2020.04.08.20057414
resource representing a document's title	A minimal and adaptive prediction strategy for critical resource planning in a pandemic
resource representing a document's body	covid:a9e6585dd4a06940cd7ca08ab2296378b62d0fcb#body_text
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