Do pre-existing medical conditions affect COVID-19 incidence and fatality in Nigeria? A Geographical Perspective
Bibliographic record
Abstract
Abstract Clinical evidence shows the incidence of novel coronavirus is associated with pre-existing medical conditions. Thus, people with pre-existing medical conditions are more likely to be infected with COVID-19. In light of this, this paper examined the extent to which pre-existing medical conditions are related to COVID-19 incidence and mortality in Nigeria from a geographical perspective. We used the geographically weighted regression (GWR) to determine the effect and extent to which pre-existing medical conditions affect COVID-19 incidence in Nigeria. Our findings show that besides the remarkable spatial variation in COVID-19 incidence and mortality, obesity was a significant predictor of COVID-19 with its effect strongest in southwest Nigeria and other parts of the country. The conclusion of the paper is that areas with high prevalence of pre-existing medical conditions coincide with areas with high COVID-19 incidence and fatality. We recommended that there should be a spatially explicit intervention on the reduction of exposure to COVID-19 among states with high prevalence of pre-existing medical conditions through vaccination.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".