Epidemic and Intervention Modelling-A Scientific Rationale for Policy Decisions? Lessons from the 2009 Influenza Pandemic/ Epidemie et Modelisation D'intervention-Une Justification Scientifique Aux Decisions Politiques? Lecons Tirees De la Pandemie De Grippe De 2009/ Modelizadon Epidemica E Intervencionista-[??]Un Fundamento Cientifico Para la Toma De Decisiones? Lecciones De la Gripe Pandemica De 2009
Notice bibliographique
Résumé
Background Outbreak analysis and mathematical modelling have played an important role in the planning of the public health response to infectious disease outbreaks, epidemics and pandemics. These tools can help quantify the risk to human health posed by a new infectious organism, rapidly analyse and interpret limited data in the early stages of an epidemic, and use such analysis to predict future developments. All of these actions are necessary to evaluate the potential benefits of specific control measures. Statistical and mathematical models integrate and synthesize epidemiological, clinical, virologic, genetic and sociodemographic data to gain quantitative insights into patterns of disease transmission. (1) Soon after the emergence of A(H1N1)pdm09 in North America in 2009, the World Health Organization (WHO) convened an informal mathematical modelling network of public health experts and mathematical modelling groups in academic institutions. This network worked collaboratively to characterize the dynamics and impact of the pandemic and demonstrate the potential outcome of various interventions in different settings. This work was published in formats suitable for various audiences, including technical experts, policymakers and the general public. Emphasis was on adapting and interpreting experiences from developed countries for application to low-resource settings. (2) In this paper we provide an overview of the analysis and mathematical modelling undertaken during and following the 2009 pandemic, with an emphasis on research of relevance to public health planning and decision-making. Pre-pandemic planning Mathematical models have been used by ministries of health and governments to inform influenza pandemic planning in many developed countries. Planning assumptions--in which disease severity (e.g. the case-fatality ratio) and the transmission characteristics (e.g. the basic reproductive number, [R.sub.0]) of the influenza virus are based on past pandemics (e.g. 1918, 1957, 1968) or potential pandemic viral strains (e.g. highly pathogenic avian influenza subtype H5N1)--are modelled to estimate the potential incidence trajectory of infected and fatal cases and the likely impact of control measures. Such information makes it possible to determine the medical and non-medical interventions required, the feasibility of containment and the optimal size of the medication stockpile and best use of pharmaceuticals once a pandemic begins. (3,4) Modelling during the 2009 pandemic During the 2009 A(H1N1) pandemic, members of the influenza modelling community worked closely with public health agencies and ministries of health. Efforts focused on rapidly quantifying transmission to provide evidence for WHO pandemic phase changes; (5) assessing severity (6) and seasonality; (7,8) interpreting epidemiologic trends over time; measuring antigenic changes in the virus (9) and assessing the potential impact of interventions. (10,11) Modellers in public health agencies also provided input into study design and helped to identify key data to address public health challenges. (12,13) Although mathematical modelling was used for planning purposes and to explore mitigation options in many countries of the Americas (e.g. Canada, Mexico and the United States of America), Europe (e.g. France, Germany, the Netherlands and the United Kingdom of Great Britain and Northern Ireland), Asia (e.g. China and Japan) and the Pacific (Australia and New Zealand), it was not sophisticated simulation modelling, but rather, real-time statistical analyses based on mechanistic transmission models and the interpretation of emerging epidemiologic and virologic data that most often informed policy decisions on a day-to-day basis. These results were widely disseminated in peer-reviewed publications, yet much of the advice and guidance derived from the modelling was never formally published but was presented instead during face-to-face meetings with national policy-makers, with occasional documentation in meeting minutes or reports. …
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,017 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,008 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».