<scp>SARS‐CoV</scp>‐2 seroprevalence among blood donors in Uganda: 2019–2022
Bibliographic record
Abstract
BACKGROUND: The true burden of COVID-19 in low- and middle-income countries remains poorly characterized, especially in Africa. Even prior to the availability of SARS-CoV-2 vaccines, countries in Africa had lower numbers of reported COVID-19 related hospitalizations and deaths than other regions globally. METHODS: Ugandan blood donors were evaluated between October 2019 and April 2022 for IgG antibodies to SARS-CoV-2 nucleocapsid (N), spike (S), and five variants of the S protein using multiplexed electrochemiluminescence immunoassays (MesoScale Diagnostics, Rockville, MD). Seropositivity for N and S was assigned using manufacturer-provided cutoffs and trends in seroprevalence were estimated by quarter. Statistically significant associations between N and S antibody seropositivity and donor characteristics in November-December 2021 were assessed by chi-square tests. RESULTS: A total of 5393 blood unit samples from donors were evaluated. N and S seropositivity increased throughout the pandemic to 82.6% in January-April 2022. Among seropositive individuals, N and S antibody levels increased ≥9-fold over the study period. In November-December 2021, seropositivity to N and S antibody was higher among repeat donors (61.3%) compared with new donors (55.1%; p = .043) and among donors from Kampala (capital city of Uganda) compared with rural regions (p = .007). Seropositivity to S antibody was significantly lower among HIV-seropositive individuals (58.8% vs. 84.9%; p = .009). CONCLUSIONS: Despite previously reported low numbers of COVID-19 cases and related deaths in Uganda, high SARS-CoV-2 seroprevalence and increasing antibody levels among blood donors indicated that the country experienced high levels of infection over the course of the pandemic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".