Assessing the Intervention’s Effectiveness and Health System Efficiency During COVID-19 Crisis using A Signal-to-Noise Ratio Index
Notice bibliographique
Résumé
Abstract During COVID-19 nearly everyone around the globe was monitoring the situation on a daily, if not hourly, basis by tracking a set of numbers that were reported by different institutions through multiple platforms: either official, or informal. Irrespective of the sources from which the data was pulled, many researchers, reporters and professionals made the effort to represent the data in different ways in an effort to explain: what happened, what was happening, and what might happen; with the hope of seeing a sign of slowing down the spread of the virus (SARS-CoV-2). A subset of these reported numbers included: the confirmed cases, number of deaths, number of recovered cases along with the number of tests being carried out by each country. Each of these numbers (metrics) was able to reveal only one side of the reality ignoring the messages that might come from other metrics (numbers). Focusing only on one single metric to reflect on the situation opened the doors for emergent opinions, theories speculations, and even confusion among the professionals before the public. In fact, all of these efforts to explain and describe the situation through the same available numbers did not manage to see clearly or shed the light on the performance and the efficiency of the country’s health system in dealing with the ongoing COVID-19 outbreak. It was evident that none of these numbers could reconstruct the full picture about the virus spread behavior nor about the capability and capacity of the health system in dealing with the pandemic. A combined metric should have been developed to best reflect the performance of the health system during the crisis. In this paper, a signal to noise ratio like index, snr was introduced in an attempt to evaluate the efficiency of the health system as well as the effectiveness of the interventions taken by the stakeholders in an effort to control the virus spread during any health-related crisis. Using this proposed index ( snr ), it was possible to carry out a data-driven comparison among different countries in their efforts of dealing with the crisis. The primary focus of this study was to assess the interventions’ effectiveness by the decision makers along with the health system’s efficiency of the countries that experienced a relatively high pressure and stress on their systems. In this study, 19 countries were selected based on predefined criteria that included: (1) the reported total confirmed cases should exceed 5,000, and (2) the total confirmed cases per 1 million people should exceed 200, at the time this study was concluded. According to the proposed snr index, the findings showed that Germany and South Korea were ahead of the game, by far, compared to other countries such as the USA, Spain, Italy, Belgium, Netherlands, and UK. Some other countries, such as Canada, Austria, Switzerland managed to slightly pivot their interventions at a later stage in effective manners according to the snr index. The study explains the foundation, and the underlying calculations of the proposed snr index. Moreover, the study shows how reliably the snr index measures the interventions’ effectiveness and health system’s efficiency during the crisis or during any health related crisis. An additional, yet interesting finding from this study, was that the snr curve showed a persistent four episode (segment) structure or pattern during the pandemic. This finding could suggest a benchmark of the expected pattern of the fight against the virus spread during the pandemic that could offer a significant tool, or approach, for the decision makers. Finally, it is worth mentioning that the implementation of this proposed index is only valid and meaningful during a crisis. In a none crisis time, the required data to calculate the snr index is not available and rather mathematically misleading.
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 | 0,017 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».