COVID-19 imported cases and severity: expected information from genomic characterization of SARS-CoV-2 strains in Mozambique
Bibliographic record
Abstract
INTRODUCTION: The novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), etiological agent of coronavirus disease 2019 (COVID-19) was first reported in China in December 2019 and spread worldwide. As of March 6th, 2021, there have been 116,670,105 million confirmed cases globally including 2,592,085 deaths. COVID-19 cases have been reported in 219 countries and territories, creating global panic. Mozambique has witnessed the evolution of COVID-19 epidemic associated with the weakness of health system, mostly in terms of laboratory diagnosis capacity, concerns on compliance to effective public health measures including physical distancing, use of masks in crowded indoor areas, hand hygiene, isolation and quarantine of people. METHODOLOGY: The data included in this study were collected from published articles regarding COVID-19 imported cases and severity in Africa, especially in Mozambique. Additionally, official documents of COVID-19 epidemiology from Minister of Health and National Institute of Health of Mozambique from 22nd of March 2020 to 1st of August 2020 were included. RESULTS: The SARS-CoV-2 strains imported mainly from South Africa and European countries might have been playing an important role on COVID-19 epidemic evolution in Mozambique. CONCLUSIONS: These circulating strains in the country, might be similar enough to the strains found in other countries, yet the genomic characterization is needed particularly during the phase of borders reopening through understanding the source of infections and guiding the implementation of containment and mitigation measures in the country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".