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Enregistrement W4390541912 · doi:10.1111/irv.13249

Comparison of the Oxford COVID‐19 Government Response Tracker and the ECDC‐JRC Response Measures Database for nonpharmaceutical interventions

2024· letter· en· W4390541912 sur OpenAlexaff
Susanne Heemskerk, Peter Spreeuwenberg, Harish Nair, John Paget

Notice bibliographique

RevueInfluenza and Other Respiratory Viruses · 2024
Typeletter
Langueen
DomaineMathematics
ThématiqueCOVID-19 epidemiological studies
Établissements canadiensCentre for Global Health Research
Organismes subventionnairesHorizon 2020 Framework ProgrammeInnovative Medicines InitiativeEuropean CommissionEuropean Federation of Pharmaceutical Industries and Associations
Mots-clésContext (archaeology)Psychological interventionGovernment (linguistics)Public healthDatabaseMedicineEnvironmental healthGeographyNursingComputer science

Résumé

récupéré en direct d'OpenAlex

During the COVID-19 pandemic, governments implemented different public health measures and interventions to control COVID-19. These wide-ranging public health interventions, also known as non-pharmaceutical interventions (NPIs), have been documented in Europe in two different publicly available databases. In the context of an EU-funded research project aimed at Preparing for Respiratory Syncytial Virus (RSV) Immunisation and Surveillance in Europe (PROMISE),1 we will use these databases to assess the impact of NPIs (e.g., school closures) on the seasonality of RSV. In this letter, we will focus on the comparison of the NPI databases, which we will later use for our analyses. The first database is the Oxford COVID-19 Government Response Tracker (OxCGRT)2 which collects policy measures implemented from 01 January 2020 to 31 December 2022 in 185 countries and contains 25 indicators that are recorded on an ordinal scale that represents the level of strictness of the policy. The second database is the European Centre for Disease Prevention and Control (ECDC) and the Joint Research Centre (JRC) Response Measures Database (ECDC-JRC RMD)3 which is an archive of NPIs introduced by 30 countries in the EU and EEA from 01 January 2020 to 30 September 2022. We compared five NPI measures related to RSV transmission in the OxCGRT and ECDC-JRC RMD databases and found important differences (see Figure 1). We chose five measures with similar definitions: workplace measures, public gathering restrictions, closure of public spaces, closure of educational institutions and protective mask use. We chose the measures that were most comparable across both databases and chose the strictest measure to define whether an intervention was implemented or not (e.g., we chose full implementation over partial implementation to assess whether a measure was introduced). The four countries in Figure 1 were selected to capture different regions in Europe, ranging from Portugal in the West to the Czech Republic in the East and Denmark and the Netherlands in Northern Europe. The figure shows that the measures in the two databases are often similar, but differences between the start and end dates for each NPI are clearly observed. For example, in Denmark, public gathering restrictions started in March 2020 and ended around August 2021 according to the OxCGRT, while the ECDC-JRC RMD shows approximately the same start and end date but with multiple weeks where public gatherings restrictions were not applied. It is difficult to observe clear patterns in the differences between the two databases, and it is not possible to say which database is more conservative or strict (i.e., one database consistently indicates shorter intervention periods). Another study compared international border restrictions in four countries (Morocco, New Zealand, South Korea and the United States) across five NPI databases, including OxCGRT and also found discrepancies between the timing of interventions.5 The variation between the databases might be explained by differences in definitions, the methodology regarding how the databases are constructed or the way the data were collected. It is also possible that one database is better for certain indicators, while the other is better for other indicators. Considering these points, it is not possible for us to say which NPI database is best and it may be advisable for researchers to run their analyses on both databases (separately). Our assessment finds that the OxCGRT and ECDC-JRC RMD databases are valuable for research purposes; they are comprehensive, freely available and easily accessible. However, despite the extensive documentation provided, we encountered challenges in synchronising the databases and we observed many disparities. This means it is difficult for researchers to select a suitable NPI database for research purposes, including for our modelling study. In summary, we found important differences between the two databases regarding NPIs related to RSV and we would recommend that an evaluation of the databases (e.g., accuracy and completeness) is initiated to support other researchers wanting to use these databases for research purposes. SH and JP have contributed to the conception and design of the study. SH was responsible for data analysis and interpretation of the data. SH wrote the letter, and JP revised all versions. JP was involved until one of the final versions of the letter; after his passing, only small textual changes occurred, with no substantive alterations taking place. PS and HN critically reviewed the manuscript, provided comments and approved this manuscript. JP declares that Nivel has received unrestricted grants from the World Health Organization, Sanofi and the Foundation for Influenza Epidemiology outside the submitted work. HN reports grants from the World Health Organization, the National Institute for Health Research, Pfizer and Icosavax and personal fees from the Bill & Melinda Gates Foundation, Pfizer, GSK, Merck, AbbVie, Janssen, Icosavax, Sanofi, Novavax, outside the submitted work.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,099
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,053
Score d'incertitude au seuil0,108

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,099
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0070,013
Études des sciences et des technologies0,0000,001
Communication savante0,0030,002
Science ouverte0,0020,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,703
Tête enseignante GPT0,568
Écart entre enseignants0,135 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2024
Routes d'admission1
Résumé présentoui

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