Epidemiological and Clinical Characteristics, and Virologic Features of COVID-19 Patients in Kazakhstan: a Nation-Wide, Retrospective, Cohort Study
Bibliographic record
Abstract
ABSTRACT Background The earliest coronavirus disease-2019 (COVID-19) cases in Central Asia were announced in March 2020 by Kazakhstan. Despite the implementation of aggressive measures to curb infection spread, gaps remain in the understanding of the clinical and epidemiologic features of the regional pandemic. Methods We did a retrospective, observational cohort study of patients with laboratory-confirmed COVID-19 in Kazakhstan between February and April 2020. We compared demographic, clinical, laboratory and radiological data of patients with different COVID-19 severities on admission. Univariable and multivariable logistic regression was used to assess factors associated with disease severity and death. Whole-genome SARS-CoV-2 analysis was performed in 53 patients without a recent history of international travel. Findings Of the 1072 patients with laboratory-confirmed COVID-19 in March-April 2020, the median age was 36 years (IQR 24–50) and 484 (45%) were male. On admission, 683 (64%) participants had mild, 341 (32%) moderate, and 47 (4%) severe-to-critical COVID-19 manifestation; 20 deaths (1.87%) were reported at study exit. Multivariable regression indicated increasing odds of severe disease associated with older age (odds ratio 1.05, 95% CI 1.03-1.07, per year increase; p<0.001), the presence of comorbidities (2.13, 95% CI 1.07-4.23; p<0.031) and elevated white blood cell count (WBC, 1.14, 95% CI 1.01-1.28; p<0.032) on admission, while older age (1.09, 95% CI 1.06-1.12, per year increase; p<0.001) and male sex (5.97, 95% CI 1.95-18.32; p<0.002) were associated with increased odds of death. The Kazakhstan SARS-CoV-2 isolates grouped into seven distinct lineages O/B.4.1, S/A.2, S/B.1.1, G/B.1, GH/B.1.255, GH/B.1.3 and GR/B.1.1.10. Interpretation Older age, comorbidities, increased WBC count, and male sex were risk factors for COVID-19 disease severity and mortality in Kazakhstan. The broad SARS-CoV-2 diversity suggests multiple importations and community-level amplification, likely predating the declaration of state emergency. Continuous epidemiologic and genomic surveillance may be critical for a better understanding of the regional COVID-19 dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".