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Enregistrement W4411393084 · doi:10.20944/preprints202506.1240.v1

Constraints from Geotemporal Evolution of All-Cause Mortality on the Hypothesis of Disease Spread During COVID

2025· preprint· en· W4411393084 sur OpenAlexaff
Joseph Hickey, Denis Rancourt, Christian Linard

Notice bibliographique

RevuePreprints.org · 2025
Typepreprint
Langueen
DomaineMathematics
ThématiqueCOVID-19 epidemiological studies
Établissements canadiensUniversité du Québec à Trois-Rivières
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)Disease2019-20 coronavirus outbreakPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Evolutionary biologyBiologyVirologyMedicineInfectious disease (medical specialty)OutbreakInternal medicine

Résumé

récupéré en direct d'OpenAlex

Large peaks of excess all-cause mortality occurred immediately following the World Health Organization (WHO)’s March 11, 2020 COVID-19 pandemic declaration, in March-May 2020, in several jurisdictions in the Northern Hemisphere. The said large excess-mortality peaks are usually assumed to be due to a novel and virulent virus (SARS-CoV-2) that spreads by person-to-person contact, and are often referred to as resulting from the so-called first wave of infections. We tested the presumption of this viral spread paradigm using high-resolution spatial and temporal variations of all-cause mortality in Europe and the USA.We studied excess all-cause mortality for subnational regions in the USA (states and counties) and Europe (NUTS statistical regions at levels 0-3) during March-May 2020, which we call the “first-peak period”, and also during June-September 2020, which we call the “summer-peak period”.The data reveal several definitive features that are incompatible with the viral spread hypothesis (in comparison with qualified predictions of the leading spatiotemporal epidemic models): • Geographic heterogeneity of first-peak period excess mortality: There was a high degree of geographic heterogeneity in excess mortality in the USA and Europe, with a handful of geographic regions having essentially synchronous (within weeks of each other) large peaks of first-peak period excess mortality (“F-peaks”) and all other regions having low or negligible excess mortality in the said first-peak period. This includes vastly different F-peak sizes (up to a factor of 10 or more) for subnational regions on either side of an international border, such as Germany’s NUTS1 regions on its western border (small F-peaks) compared to the NUTS1 regions on the other side of the international border in the Netherlands, Belgium and France (large F-peaks), despite significant documented cross-border traffic volumes between the regions. • Temporal synchrony of first-peak period excess mortality: F-peaks for USA states and European countries were almost all positioned within three or four weeks of one another and never earlier than the week of the WHO’s pandemic declaration. For a given large-F-peak European country, the F-peaks for all subnational regions rose and fell in lockstep synchrony but showed large variation in peak height and total integrated excess mortality. A similar result was seen for the counties of large-F-peak USA states. • Large differences in first-peak period excess mortality for comparable cities with large airports in the same countries: We compare mortality results for Rome vs Milan in Italy, and Los Angeles and San Francisco vs New York City in the USA, and show that there was a dramatic difference in first-peak period excess mortality between the compared cities, despite their having similar demographics, health care systems, and international air travel traffic, including from China and East Asia.We also examined data concerning the location of death (whether in hospital, at home, in a nursing home, etc.) and socioeconomic vulnerability (poverty, minority status, crowded living conditions, etc.) at high geographic resolutions, which support an alternative hypothesis that excess mortality in jurisdictions with large F-peaks was caused by the application of dangerous medical treatments (in particular, invasive mechanical ventilation and pharmaceutical treatments) and pneumonia induced by biological stress due to treatment and lockdown measures.Exceptionally large F-peaks occurred in areas with large publicly-funded hospitals serving poor or socioeconomically frail communities, in regions where poor neighbourhoods are situated in proximity to wealthy neighbourhoods, such as the case of The Bronx in New York City, and the boroughs of Brent and Westminster in London, UK.Taken together, our study represents strong evidence that the patterns of excess mortality observed for the USA and Europe in March-May 2020 could not have been caused by a spreading respiratory virus, and instead were due to the medical and government interventions that were applied and mostly killed elderly and poor individuals.

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)0,003
score de la tête « metaresearch » (Gemma)0,014
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: Empirique
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,037

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,014
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,546
Tête enseignante GPT0,460
Écart entre enseignants0,086 · 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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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