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
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".