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Epidemiological versus meteorological forecasts: Best practice for linking models to policymaking
dc.contributor.author | Coughlan de Perez, Erin |
dc.contributor.author | Stephens, Elisabeth |
dc.contributor.author | van Aalst, Maarten |
dc.contributor.author | Bazo, Juan |
dc.contributor.author | Fournier Tombs, Eleonore |
dc.contributor.author | Funk, Sebastian |
dc.contributor.author | Hess, Jeremy J. |
dc.contributor.author | Ranger, Nicola |
dc.contributor.author | Lowe, Rachel |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2021-10-27T14:42:07Z |
dc.date.available | 2021-10-27T14:42:07Z |
dc.date.issued | 2021 |
dc.identifier.citation | Coughlan de Perez, E. [et al.]. Epidemiological versus meteorological forecasts: Best practice for linking models to policymaking. "International Journal of Forecasting", 2021, |
dc.identifier.issn | 0169-2070 |
dc.identifier.uri | http://hdl.handle.net/2117/354772 |
dc.description.abstract | Weather forecasts, climate change projections, and epidemiological predictions all represent domains that are using forecast data to take early action for risk management. However, the methods and applications of the modeling efforts in each of these three fields have been developed and applied with little cross-fertilization. This perspective identifies best practices in each domain that can be adopted by the others, which can be used to inform each field separately as well as to facilitate a more effective combined use for the management of compound and evolving risks. In light of increased attention to predictive modeling during the COVID-19 pandemic, we identify three major areas that all three of these modeling fields should prioritize for future investment and improvement: (1) decision support, (2) conveying uncertainty, and (3) capturing vulnerability. |
dc.description.sponsorship | E Stephens and E Coughlan de Perez acknowledge support from the UK’s Natural Environment Research Council (NERC) and Foreign, Commonwealth and Development Office through the FATHUM (Forecasts for AnTicipatory HUManitarian action, grant number NE/P000525/1) project, part of the Science for Humanitarian Emergencies and Resilience (SHEAR) research programme. R Lowe was supported by a Royal Society Dorothy Hodgkin Fellowship. S Funk is funded by the Wellcome Trust (210758/Z/18/Z). |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | Elsevier |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia |
dc.subject.lcsh | COVID-19 (Disease) |
dc.subject.lcsh | Policymaking |
dc.subject.lcsh | Climate change--Health aspects |
dc.subject.other | Forecasting |
dc.subject.other | COVID-19 |
dc.subject.other | Weather |
dc.subject.other | Climate |
dc.subject.other | Disasters |
dc.subject.other | Risk |
dc.subject.other | Communication |
dc.subject.other | Vulnerability |
dc.subject.other | Uncertainty |
dc.title | Epidemiological versus meteorological forecasts: Best practice for linking models to policymaking |
dc.type | Article |
dc.subject.lemac | COVID-19 (Malaltia) |
dc.identifier.doi | 10.1016/j.ijforecast.2021.08.003 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0169207021001254?via%3Dihub#! |
dc.rights.access | Open Access |
dc.description.version | Postprint (published version) |
local.citation.publicationName | International Journal of Forecasting |
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