Seroprevalence survey of SARS-CoV-2, community behaviors, and practices in Kansanshi and Kalumbila mining towns
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (SARS-CoV-2) was declared a global pandemic by WHO after it spreads quickly around the world from its source city in Wuhan. Africa has some of the lowest documented SARS-CoV-2 incidences globally, with over 9 million confirmed cases as of December 2022. This may be due to efficient mitigation, outbreak response, or demographic traits. Surveillance capability may have suffered as nations changed funding, regulations, and testing plans. Therefore, this study was to document the prevalence of SARS-CoV-2, its characteristics, and the socio-economic characteristics in the two mining districts of Solwezi and Kalumbila of Zambia. Methods: Between 28 March and 26 April 2021, a cross-sectional cluster-sample survey of households in two mining districts of Zambia was conducted. Twenty standard enumeration areas (SEAs) were randomly selected in Kansanshi (17 SEA) and Kalumbila (3 SEA) from a total of 67 SEA that encompass the two mines. Members of households aged <5 years were not eligible to participate in the survey. All participants that consented to participate in the interview were also asked to consent to test for SARS-CoV-2 infection using a rapid diagnostic test (RDT), which tested for recent infection and past exposure to the virus (IgM and IgG, respectively). Result: Out of the total sample of 3,047 that were present for the interview, 622 of them agreed to test for COVID-19. Of the total that tested for SARS-CoV-2, 2.6% were IgM positive while 9.0% were IgG positive. Despite the above results, 1,586 participants that agreed to the interview indicated a low self-risk assessment of getting COVID-19 (46.5%) or someone (45.5%). On the public health measures, participants who did handwashing more than usual (65.0%), not hand sanitizing more than usual (69.0%), not disinfecting surfaces in their households than usual (87.5%), not avoiding drinking from bars or nightclubs (90.6%), and not wearing a mask when out in public places (71.1%). In the logistic multivariable model, participants with age 24 years and above (AOR = 2.94; 95% CI = 1.10, 7.81) and having experienced symptoms of SARS-CoV-2 (AOR = 2.60; 95% CI: 1.33, 5.05) had a significant effect on testing positive for SARS-CoV-2. Conclusion: Although the results showed that active COVID-19 prevalence in Solwezi and Kalumbila communities surrounding the two mines was low, exposure to infection was five times high. Government and mining firms should continue to sensitize the community members on the preventive measures of COVID-19 and continue with community testing so that all those positive but without symptoms can self-isolate and those with symptoms and sick can be admitted to the hospital.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".