Copula-based estimation of health concentration curves with an application to COVID-19
Bibliographic record
Abstract
COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that we use to build our estimators of HC. For the semi-parametric estimator, a parametric copula is used to model the dependence between health and socio-economic variables. The copula function is estimated using maximum pseudo-likelihood estimator after replacing the cumulative distribution of health variable by its empirical analogue. For the non-parametric estimator, we replace the copula function by a Bernstein copula estimator. Furthermore, we use the above estimators of HC to derive copula-based estimators of health Gini coeffcient. We establish the consistency and the asymptotic normality of HC’s estimators. Using different data-generating processes and sample sizes, a Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Integrated Mean Squared Error. Finally, we run an extensive empirical study to illustrate the importance of HC’s estimators for investigating inequality in COVID-19 infections and deaths in the U.S. The empirical results show that the inequalities in state’s socio-economic variables like poverty, race/ethnicity, and economic prosperity are behind the observed inequalities in the U.S.’s COVID-19 infections and deaths.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".