Bibliographic record
Abstract
This paper examines the geographic factors that are associated with the spread of COVID-19 during the first wave in Sweden. We focus particularly on the role of place-based factors versus factors associated with the spread or diffusion of COVID-19 across places. Sweden is a useful case study to examine the interplay of these factors because it did not impose mandatory lockdowns and because there were essentially no regional differences in the pandemic policies or strategies during the first wave of COVID-19. We examine the role of place-based factors like density, age structures and different socioeconomic factors on the geographic variation of COVID-19 cases and on deaths, across both municipalities and neighborhoods. Our findings show that factors associated with diffusion matter more than place-based factors in the geographic incidence of COVID-19 in Sweden. The most significant factor of all is proximity to places with higher levels of infections. COVID-19 is also higher in places that were hit earliest in the outbreak. Of place-based factors, the geographic variation in COVID-19 is most significantly related to the presence of high-risk nursing homes, and only modestly associated with factors like density, population size, income and other socioeconomic characteristics of places.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".