Assessment of Epidemiological Determinants of COVID-19 Pandemic Related to Social and Economic Factors Globally
Bibliographic record
Abstract
The COVID-19 pandemic has manifested more than a health crisis and has severely impacted on social, economic, and development crises in the world. The relationship of COVID-19 with countries’ economic and other demographic statuses is an important criterion with which to assess the impact of this current outbreak. Based on available data from the online platform, we tested the hypotheses of a country’s economic status, population density, the median age of the population, and urbanization pattern influence on the test, attack, case fatality, and recovery rates of COVID-19. We performed correlation and multivariate multinomial regression analysis with relative risk ratio (RRR) to test the hypotheses. The correlation analysis showed that population density and test rate had a significantly negative association (r = −0.2384, p = 0.00). In contrast, the median age had a significant positive correlation with recovery rate (r = 0.4654, p = 0.00) and case fatality rate (r = 0.2847, p = 0.00). The urban population rate had a positive significant correlation with recovery rate (r = 0.1610, p = 0.04). Lower-middle-income countries had a negative significant correlation with case fatality rate (r= −0.3310, p = 0.04). The multivariate multinomial logistic regression analysis revealed that low-income countries are more likely to have an increased risk of case fatality rate (RRR = 0.986, 95% Confidence Interval; CI = 0.97−1.00, p < 0.05) and recovery rate (RRR = 0.967, 95% CI = 0.95–0.98, p = 0.00). The lower-income countries are more likely to have a higher risk in case of attack rate (RRR = 0.981, 95% CI = 0.97–0.99, p = 0.00) and recovery rate (RRR = 0.971, 95% CI = 0.96–0.98, p = 0.00). Similarly, upper middle-income countries are more likely to have higher risk in case of attack rate (RRR = 0.988, 95% CI = 0.98–1.0, p = 0.01) and recovery rate (RRR = 0.978, 95% CI = 0.97–0.99, p = 0.00). The low- and lower-middle-income countries should invest more in health care services and implement adequate COVID-19 preventive measures to reduce the risk burden. We recommend a participatory, whole-of-government and whole-of-society approach for responding to the socio-economic challenges of COVID-19 and ensuring more resilient and robust health systems to safeguard against preventable deaths and poverty by improving public health outcomes.
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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 | 0.000 | 0.000 |
| 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".