About: Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium

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About: Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium Goto Sponge NotDistinct Permalink

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	Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium
Creator	Vandamme, Anne-Mieke Debackere, Koenraad Decock, Kristof Looy, Bart
Source	Medline; PMC
abstract	The recent ‘outburst’ of COVID-19 spurred efforts to model and forecast its diffusion patterns, either in terms of infections, people in need of medical assistance (ICU occupation) or casualties. Forecasting patterns and their implied end states remains cumbersome when few (stochastic) data points are available during the early stage of diffusion processes. Extrapolations based on compounded growth rates do not account for inflection points nor end-states. In order to remedy this situation, we advance a set of heuristics which combine forecasting and scenario thinking. Inspired by scenario thinking we allow for a broad range of end states (and their implied growth dynamics, parameters) which are consecutively being assessed in terms of how well they coincide with actual observations. When applying this approach to the diffusion of COVID-19, it becomes clear that combining potential end states with unfolding trajectories provides a better-informed decision space as short term predictions are accurate, while a portfolio of different end states informs the long view. The creation of such a decision space requires temporal distance. Only to the extent that one refrains from incorporating more recent data, more plausible end states become visible. Such dynamic approach also allows one to assess the potential effects of mitigating measures. As such, our contribution implies a plea for dynamically blending forecasting algorithms and scenario-oriented thinking, rather than conceiving them as substitutes or complements.
has issue date	2020-07-13(xsd:dateTime)
bibo:doi	10.1007/s11192-020-03591-6
bibo:pmid	32836528
has license	no-cc
sha1sum (hex)	5fcc6bf27fd1e159506f4346c7bbbe89ec8d46b4
schema:url	https://doi.org/10.1007/s11192-020-03591-6
resource representing a document's title	Scenario-driven forecasting: modeling peaks and paths. Insights from the COVID-19 pandemic in Belgium
has PubMed Central identifier	PMC7355133
has PubMed identifier	32836528
schema:publication	Scientometrics
resource representing a document's body	covid:5fcc6bf27fd1e159506f4346c7bbbe89ec8d46b4#body_text
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