Incidence rate and predictors of COVID-19 in the two largest cities of Burkina Faso - prospective cohort study in 2021 (ANRS-COV13)
Bibliographic record
Abstract
BACKGROUND: Early data on COVID-19 (based primarily on PCR testing) indicated a low burden in Sub-Saharan Africa. To better understand this, this study aimed to estimate the incidence rate and identify predictors of SARS-CoV-2 seroconversion in the two largest cities of Burkina Faso. This study is part of the EmulCOVID-19 project (ANRS-COV13). METHODS: Our study utilized the WHO Unity protocol for cohort sero-epidemiological studies of COVID-19 in general population. We conducted random sampling stratified by age group and sex. Individuals aged 10 years and older in the cities of Ouagadougou and Bobo-Dioulasso, Burkina Faso were included and surveyed at 4 time points, each 21 days apart, from March 3 to May 15, 2021. WANTAI SARS-CoV-2 Ab ELISA serological tests were used to detect total antibodies (IgM, IgG) in serum. Predictors were investigated using Cox proportional hazards regression. RESULTS: We analyzed the data from 1399 participants (1051 in Ouagadougou, 348 in Bobo-Dioulasso) who were SARS-CoV-2 seronegative at baseline and had at least one follow-up visit. The incidence rate of SARS-CoV-2 seroconversion was 14.3 cases [95%CI 13.3-15.4] per 100 person-weeks. The incidence rate was almost three times higher in Ouagadougou than in Bobo-Dioulasso (Incidence rate ratio: IRR = 2.7 [2.2-3.2], p < 0.001). The highest incidence rate was reported among women aged 19-59 years in Ouagadougou (22.8 cases [19.6-26.4] per 100 person-weeks) and the lowest among participants aged 60 years and over in Bobo-Dioulasso, 6.3 cases [4.6-8.6] per 100 person-weeks. Multivariable analysis showed that participants aged 19 years and older were almost twice as likely to seroconvert during the study period compared with those aged 10 to 18 years (Hazard ratio: HR = 1.7 [1.3-2.3], p < 0.001). Those aged 10-18 years exhibited more asymptomatic forms than those aged 19 years and older, among those who achieved seroconversion (72.9% vs. 40.4%, p < 0.001). CONCLUSION: The spread of COVID-19 is more rapid in adults and in large cities. Strategies to control this pandemic in Burkina Faso, must take this into account. Adults living in large cities should be the priority targets for vaccination efforts against COVID-19.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".