Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa
Bibliographic record
Abstract
Understanding the epidemiological patterns of infectious diseases across different regions and periods is essential, as it helps identify areas with elevated risks that require targeted control strategies. This paper aimed to investigate the spatial clustering pattern of COVID-19 at the country level and explore the association between global development indicators covering the 48 countries in the five regions of Africa. The data utilized were extracted from multiple databases. The data were analysed by applying spatial analysis, including the Moran-I index, the Local Indicator of Spatial Association (LISA), and spatial regression models, to study the spatial variations in the effects of socioeconomic indicators on COVID-19 mortality at the country level. The preliminary test on the spatial correlation of COVID-19 showed a significant result; thus, a spatial regression approach was employed. The spatial clustering relationships between COVID-19 mortality and the socioeconomic indicators were analysed using ordinary least squares (OLS), spatial lag models (SLM), and spatial error models (SEM). We observed the clustering of countries for COVID-19 mortality, signifying spatial correlation within the countries of Africa. Among all the socioeconomic indicators included, the effects of Gross Domestic Product and age dependency ratio on COVID-19 mortality were the most critical indicators that described the pandemic evolution across the subregion. These results highlight the need to draw preventive and response policies applicable to infectious diseases with more significant consideration of the different geographical points in the region.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".