Constraints from Geotemporal Evolution of All-Cause Mortality on the Hypothesis of Disease Spread During COVID
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".