High seroprevalence of anti-SARS-CoV-2 antibodies in the capital of Chad
Bibliographic record
Abstract
Background. Since the start of the COVID-19 pandemic, Chad has had 7,417 confirmed cases and 193 deaths, one of the lowest in Africa.Objective. This study assessed SARS-CoV-2 immunity in N’Djamena.Methods. In August-October 2021, eleven N’Djamena hospitals collected outpatient data and samples. IgG antibodies against SARSCoV- 2 nucleocapsid protein were identified using ELISA. “Bambino Gesù” Laboratory, Rome, Italy, performed external quality control with chemiluminescence assay. Results. 25-34-year-old (35.2%) made up the largest age group at 31.9 12.6 years. 56.4% were women, 1.3 women/men. The 7th district had 22.5% and the 1st 22.3%. Housewives and students dominated. Overall seroprevalence was 69.5% (95% CI: 67.7-71.3), females 68.2% (65.8-70.5) and males 71.2% (68.6-73.8). 44-year-old had 73.9% seroprevalence. Under-15s were 57.4% positive. Housewives (70.9%), civil servants (71.5%), and health workers (9.7%) had the highest antibody positivity. N’Djamena’s 9th district had 73.1% optimism and the 3rd district had 52.5%. Seroprevalences were highest at Good Samaritan Hospital (75.4%) and National General Referral Hospital (74.7%).Conclusion. Our findings indicate a high circulation of SARSCoV- 2 in N’Djamena, despite low mortality and morbidity after the first two COVID-19 pandemic waves. This high seroprevalence must be considered in Chad’s vaccine policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".